"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import copy
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQuestionAnsweringModelOutput, Seq2SeqSequenceClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...utils import (add_code_sample_docstrings, add_end_docstrings, add_start_docstrings,
add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/bigbird-pegasus-large-arxiv"
_CONFIG_FOR_DOC = "BigBirdPegasusConfig"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 1024]
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
    shifted_input_ids[:, 0] = decoder_start_token_id
    if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.")
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
    return shifted_input_ids
class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
    def __init__(self, num_embeddings: int, embedding_dim: int): super().__init__(num_embeddings, embedding_dim)
    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
        bsz, seq_len = input_ids_shape[:2]
        positions = torch.arange(past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device)
        return super().forward(positions)
class BigBirdPegasusScaledWordEmbedding(nn.Embedding):
    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.embed_scale = embed_scale
    def forward(self, input_ids: torch.Tensor): return super().forward(input_ids) * self.embed_scale
class BigBirdPegasusSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.is_decoder = config.is_decoder
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False):
        mixed_query_layer = self.query(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if is_cross_attention and past_key_value is not None:
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        if self.is_decoder: past_key_value = (key_layer, value_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        if self.is_decoder: outputs = outputs + (past_key_value,)
        return outputs
class BigBirdPegasusBlockSparseAttention(nn.Module):
    def __init__(self, config, seed=None):
        super().__init__()
        self.max_seqlen = config.max_position_embeddings
        self.seed = seed
        if config.hidden_size % config.num_attention_heads != 0: raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}.")
        self.num_attention_heads = config.num_attention_heads
        self.num_random_blocks = config.num_random_blocks
        self.block_size = config.block_size
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, hidden_states, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=None, to_blocked_mask=None, output_attentions=None):
        batch_size, seqlen, _ = hidden_states.size()
        to_seq_length = from_seq_length = seqlen
        from_block_size = to_block_size = self.block_size
        if from_seq_length % from_block_size != 0: raise ValueError("Query sided sequence length must be multiple of block size")
        if to_seq_length % to_block_size != 0: raise ValueError("Key/Value sided sequence length must be multiple of block size")
        query_layer = self.transpose_for_scores(self.query(hidden_states))
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        context_layer, attention_probs = self.bigbird_block_sparse_attention(query_layer, key_layer, value_layer, band_mask, from_mask, to_mask, from_blocked_mask,
        to_blocked_mask, self.num_attention_heads, self.num_random_blocks, self.attention_head_size, from_block_size, to_block_size, batch_size, from_seq_length,
        to_seq_length, seed=self.seed, plan_from_length=None, plan_num_rand_blocks=None, output_attentions=output_attentions)
        context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
    @staticmethod
    def torch_bmm_nd(inp_1, inp_2, ndim=None): return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1]))
    @staticmethod
    def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None): return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2)).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 2]))
    def bigbird_block_sparse_attention(self, query_layer, key_layer, value_layer, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, n_heads, n_rand_blocks,
    attention_head_size, from_block_size, to_block_size, batch_size, from_seq_len, to_seq_len, seed, plan_from_length, plan_num_rand_blocks, output_attentions):
        if from_seq_len // from_block_size != to_seq_len // to_block_size: raise ValueError("Error the number of blocks needs to be same!")
        rsqrt_d = 1 / math.sqrt(attention_head_size)
        bsz = batch_size
        attn_mask_penalty = -10000.0
        np.random.seed(seed)
        if from_seq_len in [1024, 3072, 4096]: rand_attn = [self._bigbird_block_rand_mask(self.max_seqlen, self.max_seqlen, from_block_size, to_block_size, n_rand_blocks, last_idx=1024)[: (from_seq_len // from_block_size - 2)] for _ in range(n_heads)]
        else:
            if plan_from_length is None: plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(from_seq_len, from_block_size, n_rand_blocks)
            rand_attn = self._bigbird_block_rand_mask_with_head(from_seq_length=from_seq_len, to_seq_length=to_seq_len, from_block_size=from_block_size,
            to_block_size=to_block_size, num_heads=n_heads, plan_from_length=plan_from_length, plan_num_rand_blocks=plan_num_rand_blocks)
        rand_attn = np.stack(rand_attn, axis=0)
        rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long)
        rand_attn.unsqueeze_(0)
        rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0)
        rand_mask = self._create_rand_mask_from_inputs(from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size)
        blocked_query_matrix = query_layer.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
        blocked_key_matrix = key_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
        blocked_value_matrix = value_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
        gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn)
        gathered_key = gathered_key.view(bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1)
        gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn)
        gathered_value = gathered_value.view(bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1)
        first_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 0], key_layer, ndim=4)
        first_product = first_product * rsqrt_d
        first_product += (1.0 - to_mask) * attn_mask_penalty
        first_attn_weights = nn.functional.softmax(first_product, dim=-1)
        first_context_layer = self.torch_bmm_nd(first_attn_weights, value_layer, ndim=4)
        first_context_layer.unsqueeze_(2)
        second_key_mat = torch.cat([blocked_key_matrix[:, :, 0], blocked_key_matrix[:, :, 1], blocked_key_matrix[:, :, 2], blocked_key_matrix[:, :, -1], gathered_key[:, :, 0]], dim=2)
        second_value_mat = torch.cat([blocked_value_matrix[:, :, 0], blocked_value_matrix[:, :, 1], blocked_value_matrix[:, :, 2], blocked_value_matrix[:, :, -1], gathered_value[:, :, 0]], dim=2)
        second_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 1], second_key_mat, ndim=4)
        second_seq_pad = torch.cat([to_mask[:, :, :, : 3 * to_block_size], to_mask[:, :, :, -to_block_size:], to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size])], dim=3)
        second_rand_pad = torch.cat([rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]), rand_mask[:, :, 0]], dim=3)
        second_product = second_product * rsqrt_d
        second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
        second_attn_weights = nn.functional.softmax(second_product, dim=-1)
        second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4)
        second_context_layer.unsqueeze_(2)
        exp_blocked_key_matrix = torch.cat([blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], dim=3)
        exp_blocked_value_matrix = torch.cat([blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]], dim=3)
        middle_query_matrix = blocked_query_matrix[:, :, 2:-2]
        inner_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, exp_blocked_key_matrix, ndim=5)
        inner_band_product = inner_band_product * rsqrt_d
        rand_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5)
        rand_band_product = rand_band_product * rsqrt_d
        first_band_product = torch.einsum("bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0])
        first_band_product = first_band_product * rsqrt_d
        last_band_product = torch.einsum("bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1])
        last_band_product = last_band_product * rsqrt_d
        inner_band_product += (1.0 - band_mask) * attn_mask_penalty
        first_band_product += (1.0 - to_mask[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty
        last_band_product += (1.0 - to_mask[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty
        rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty
        band_product = torch.cat([first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1)
        attn_weights = nn.functional.softmax(band_product, dim=-1)
        context_layer = self.torch_bmm_nd(attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix, ndim=5)
        context_layer += self.torch_bmm_nd(attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size], gathered_value[:, :, 1:-1], ndim=5)
        context_layer += torch.einsum("bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0])
        context_layer += torch.einsum("bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1])
        second_last_key_mat = torch.cat([blocked_key_matrix[:, :, 0], blocked_key_matrix[:, :, -3], blocked_key_matrix[:, :, -2], blocked_key_matrix[:, :, -1], gathered_key[:, :, -1]], dim=2)
        second_last_value_mat = torch.cat([blocked_value_matrix[:, :, 0], blocked_value_matrix[:, :, -3], blocked_value_matrix[:, :, -2], blocked_value_matrix[:, :, -1], gathered_value[:, :, -1]], dim=2)
        second_last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -2], second_last_key_mat, ndim=4)
        second_last_seq_pad = torch.cat([to_mask[:, :, :, :to_block_size], to_mask[:, :, :, -3 * to_block_size :], to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size])], dim=3)
        second_last_rand_pad = torch.cat([rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]), rand_mask[:, :, -1]], dim=3)
        second_last_product = second_last_product * rsqrt_d
        second_last_product += (1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
        second_last_attn_weights = nn.functional.softmax(second_last_product, dim=-1)
        second_last_context_layer = self.torch_bmm_nd(second_last_attn_weights, second_last_value_mat, ndim=4)
        second_last_context_layer.unsqueeze_(2)
        last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -1], key_layer, ndim=4)
        last_product = last_product * rsqrt_d
        last_product += (1.0 - to_mask) * attn_mask_penalty
        last_attn_weights = nn.functional.softmax(last_product, dim=-1)
        last_context_layer = self.torch_bmm_nd(last_attn_weights, value_layer, ndim=4)
        last_context_layer.unsqueeze_(2)
        context_layer = torch.cat([first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer], dim=2)
        context_layer = context_layer.view((bsz, n_heads, from_seq_len, -1)) * from_mask
        context_layer = torch.transpose(context_layer, 1, 2)
        if output_attentions:
            attention_probs = torch.zeros(bsz, n_heads, from_seq_len, to_seq_len, dtype=torch.float, device=context_layer.device)
            attention_probs[:, :, :from_block_size, :] = first_attn_weights
            attention_probs[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[:, :, :, : 3 * to_block_size]
            attention_probs[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[:, :, :, 3 * to_block_size : 4 * to_block_size]
            for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights):
                for p2, i2, w2 in zip(range(n_heads), i1, w1):
                    attn_probs_view = attention_probs.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size)
                    right_slice = w2[:, 4 * to_block_size :]
                    attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view(from_block_size, n_rand_blocks, to_block_size)
            for q_idx in range(from_seq_len // from_block_size - 4):
                attn_probs_view = attention_probs.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size)[:, :, 2:-2, :, 1:-1, :]
                right_slice = attn_weights[:, :, q_idx, :, to_block_size : 4 * to_block_size]
                attn_probs_view[:, :, q_idx, :, q_idx : q_idx + 3, :] = right_slice.view(bsz, n_heads, from_block_size, 3, to_block_size)
            attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[:, :, :, :, :to_block_size].view(bsz, n_heads, -1, to_block_size)
            attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[:, :, :, :, -to_block_size:].view(bsz, n_heads, -1, to_block_size)
            for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights):
                for p2, i2, w2 in zip(range(n_heads), i1, w1):
                    for q_idx in range(1, len(i2) - 1):
                        attn_probs_view = attention_probs.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size)
                        right_slice = w2[q_idx - 1, :, 4 * to_block_size : -to_block_size]
                        attn_probs_view[p1, p2, q_idx + 1, :, i2[q_idx]] = right_slice.view(from_block_size, n_rand_blocks, to_block_size)
            attention_probs[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[:, :, :, :to_block_size]
            attention_probs[:, :, -2 * from_block_size : -from_block_size, -3 * to_block_size :] = (second_last_attn_weights[:, :, :, to_block_size : 4 * to_block_size])
            for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights):
                for p2, i2, w2 in zip(range(n_heads), i1, w1):
                    attn_probs_view = attention_probs.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size)
                    right_slice = w2[:, 4 * to_block_size :]
                    attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view(from_block_size, n_rand_blocks, to_block_size)
            attention_probs[:, :, -from_block_size:, :] = last_attn_weights
        else: attention_probs = None
        return context_layer, attention_probs
    @staticmethod
    def torch_gather_b2(params, indices):
        if params.shape[:2] != indices.shape[:2]: raise ValueError(f"Make sure that the first two dimensions of params and indices are identical, but they are params: {params.shape[:2]} vs. indices: {indices.shape[:2]}")
        num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
        num_indices_to_pick_from = params.shape[2]
        shift = torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
        indices_shift = torch.div(shift, num_indices_to_gather, rounding_mode="floor") * num_indices_to_pick_from
        flattened_indices = indices.view(-1) + indices_shift
        flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])
        out_flattened = flattened_params.index_select(0, flattened_indices)
        out = out_flattened.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:])
        return out
    @staticmethod
    def _create_rand_mask_from_inputs(from_blocked_mask, to_blocked_mask, rand_attn, num_attention_heads, num_rand_blocks, batch_size, from_seq_length, from_block_size):
        num_windows = from_seq_length // from_block_size - 2
        rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)])
        rand_mask = rand_mask.view(batch_size, num_attention_heads, num_windows, num_rand_blocks * from_block_size)
        rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
        return rand_mask
    @staticmethod
    def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
        plan_from_length = []
        plan_num_rand_blocks = []
        if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
            plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
            plan_num_rand_blocks.append(num_rand_blocks)
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(0)
        elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
            plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
            plan_num_rand_blocks.append(num_rand_blocks // 2)
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
        else:
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(num_rand_blocks)
        return plan_from_length, plan_num_rand_blocks
    def _bigbird_block_rand_mask(self, from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1):
        if from_seq_length // from_block_size != to_seq_length // to_block_size: raise ValueError("Error the number of blocks needs to be same!")
        rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32)
        if not self.training: return rand_attn
        middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32)
        last = to_seq_length // to_block_size - 1
        if last_idx > (2 * to_block_size): last = (last_idx // to_block_size) - 1
        r = num_rand_blocks
        for i in range(1, from_seq_length // from_block_size - 1):
            start = i - 2
            end = i
            if i == 1: rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
            elif i == 2: rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
            elif i == from_seq_length // from_block_size - 3: rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
            elif i == from_seq_length // from_block_size - 2: rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
            else:
                if start > last:
                    start = last
                    rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
                elif (end + 1) == last: rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
                else: rand_attn[i - 1, :] = np.random.permutation(np.concatenate((middle_seq[:start], middle_seq[end + 1 : last])))[:r]
        return rand_attn
    def _bigbird_block_rand_mask_with_head(self, from_seq_length, to_seq_length, from_block_size, to_block_size, num_heads, plan_from_length, plan_num_rand_blocks,
    window_block_left=1, window_block_right=1, global_block_top=1, global_block_bottom=1, global_block_left=1, global_block_right=1):
        if from_seq_length // from_block_size != to_seq_length // to_block_size: raise ValueError("Error the number of blocks needs to be same!")
        if from_seq_length not in plan_from_length: raise ValueError("Error from sequence length not in plan!")
        num_blocks = from_seq_length // from_block_size
        plan_block_length = np.array(plan_from_length) // from_block_size
        max_plan_idx = plan_from_length.index(from_seq_length)
        rand_attn = [np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32) for i in range(num_heads)]
        if not self.training:
            for nh in range(num_heads): rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
            return rand_attn
        for plan_idx in range(max_plan_idx + 1):
            rnd_r_cnt = 0
            if plan_idx > 0:
                if plan_num_rand_blocks[plan_idx] > 0:
                    rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
                    curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
                    for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
                        for h in range(num_heads):
                            rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(block_id=blk_rw_idx, to_start_block_id=plan_block_length[plan_idx - 1],
                            to_end_block_id=plan_block_length[plan_idx], num_rand_blocks=plan_num_rand_blocks[plan_idx], window_block_left=window_block_left, window_block_right=window_block_right,
                            global_block_left=global_block_left, global_block_right=global_block_right)
                for pl_id in range(plan_idx):
                    if plan_num_rand_blocks[pl_id] == 0: continue
                    for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
                        rnd_r_cnt = 0
                        to_start_block_id = 0
                        if pl_id > 0:
                            rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
                            to_start_block_id = plan_block_length[pl_id - 1]
                        curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
                        for h in range(num_heads):
                            rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(block_id=blk_rw_idx, to_start_block_id=to_start_block_id,
                            to_end_block_id=plan_block_length[pl_id], num_rand_blocks=plan_num_rand_blocks[pl_id], window_block_left=window_block_left, window_block_right=window_block_right,
                            global_block_left=global_block_left, global_block_right=global_block_right)
            if plan_num_rand_blocks[plan_idx] == 0: continue
            curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
            from_start_block_id = global_block_top
            to_start_block_id = 0
            if plan_idx > 0:
                rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
                from_start_block_id = plan_block_length[plan_idx - 1]
                to_start_block_id = plan_block_length[plan_idx - 1]
            for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
                for h in range(num_heads):
                    rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(block_id=blk_rw_idx, to_start_block_id=to_start_block_id, to_end_block_id=plan_block_length[plan_idx],
                    num_rand_blocks=plan_num_rand_blocks[plan_idx], window_block_left=window_block_left, window_block_right=window_block_right, global_block_left=global_block_left, global_block_right=global_block_right)
        for nh in range(num_heads): rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
        return rand_attn
    @staticmethod
    def _get_single_block_row_attention(block_id, to_start_block_id, to_end_block_id, num_rand_blocks, window_block_left=1, window_block_right=1, global_block_left=1, global_block_right=1):
        to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32)
        perm_block = np.random.permutation(to_block_list)
        illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))
        illegal_blocks.extend(list(range(global_block_left)))
        illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))
        if block_id == 1: illegal_blocks.append(to_end_block_id - 2)
        if block_id == to_end_block_id - 2: illegal_blocks.append(1)
        selected_random_blokcs = []
        for i in range(to_end_block_id - to_start_block_id):
            if perm_block[i] not in illegal_blocks: selected_random_blokcs.append(perm_block[i])
            if len(selected_random_blokcs) == num_rand_blocks: break
        return np.array(selected_random_blokcs, dtype=np.int32)
class BigBirdPegasusEncoderAttention(nn.Module):
    def __init__(self, config, seed=None):
        super().__init__()
        self.config = config
        self.seed = seed
        self.attention_type = config.attention_type
        if self.attention_type == "original_full": self.self = BigBirdPegasusSelfAttention(config)
        elif self.attention_type == "block_sparse": self.self = BigBirdPegasusBlockSparseAttention(config, seed)
        else: raise ValueError(f"attention_type can either be original_full or block_sparse, but is {self.config.attention_type}")
        self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias)
    def set_attention_type(self, value: str):
        if value not in ["original_full", "block_sparse"]: raise ValueError(f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}")
        if value == self.attention_type: return
        self.attention_type = value
        if value == "original_full": attn_weights = BigBirdPegasusSelfAttention(self.config)
        else: attn_weights = BigBirdPegasusBlockSparseAttention(self.config, self.seed)
        attn_weights.query = self.self.query
        attn_weights.value = self.self.value
        attn_weights.key = self.self.key
        self.self = attn_weights
        self.attention_type = value
        if not self.training: self.self.eval()
    def forward(self, hidden_states, attention_mask=None, head_mask=None, past_key_value=None, output_attentions=False, band_mask=None, from_mask=None, to_mask=None,
    from_blocked_mask=None, to_blocked_mask=None):
        head_mask = head_mask.reshape(1, -1, 1, 1) if head_mask is not None else None
        if self.config.attention_type == "original_full": self_outputs = self.self(hidden_states, attention_mask, head_mask, past_key_value=past_key_value, output_attentions=output_attentions)
        else: self_outputs = self.self(hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions)
        attention_output = self.output(self_outputs[0])
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class BigBirdPegasusDecoderAttention(nn.Module):
    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[BigBirdPegasusConfig] = None):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config
        if (self.head_dim * num_heads) != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads}).")
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
    def forward(self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states) * self.scaling
        if (is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1]):
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
        if self.is_decoder: past_key_value = (key_states, value_states)
        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.reshape(*proj_shape)
        value_states = value_states.reshape(*proj_shape)
        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError(f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}")
        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}")
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,): raise ValueError(f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}")
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        if output_attentions:
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else: attn_weights_reshaped = None
        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = torch.bmm(attn_probs, value_states)
        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError(f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}")
        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights_reshaped, past_key_value
class BigBirdPegasusEncoderLayer(nn.Module):
    def __init__(self, config: BigBirdPegasusConfig, seed=None):
        super().__init__()
        self.attention_type = config.attention_type
        self.embed_dim = config.d_model
        self.self_attn = BigBirdPegasusEncoderAttention(config, seed=seed)
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, band_mask=None, from_mask=None, to_mask=None,
    from_blocked_mask=None, to_blocked_mask=None, output_attentions: bool = False):
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        self_attention_outputs = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, head_mask=layer_head_mask, output_attentions=output_attentions,
        band_mask=band_mask, from_mask=from_mask, to_mask=to_mask, from_blocked_mask=from_blocked_mask, to_blocked_mask=to_blocked_mask)
        hidden_states = self_attention_outputs[0]
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        if hidden_states.dtype == torch.float16 and (torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attention_outputs[1],)
        return outputs
    def set_attention_type(self, value: str):
        if value not in ["original_full", "block_sparse"]: raise ValueError(f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}")
        if value == self.attention_type: return
        self.attention_type = value
        self.self_attn.set_attention_type(value)
class BigBirdPegasusDecoderLayer(nn.Module):
    def __init__(self, config: BigBirdPegasusConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = BigBirdPegasusDecoderAttention(embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, bias=config.use_bias)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = BigBirdPegasusDecoderAttention(self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, bias=config.use_bias)
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None,
    layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False,
    use_cache: Optional[bool] = True) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask,
        layer_head_mask=layer_head_mask, output_attentions=output_attentions)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(hidden_states=hidden_states, key_value_states=encoder_hidden_states,
            attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions)
            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states
            present_key_value = present_key_value + cross_attn_present_key_value
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights, cross_attn_weights)
        if use_cache: outputs += (present_key_value,)
        return outputs
class BigBirdPegasusClassificationHead(nn.Module):
    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
        super().__init__()
        self.dense = nn.Linear(input_dim, inner_dim)
        self.dropout = nn.Dropout(p=pooler_dropout)
        self.out_proj = nn.Linear(inner_dim, num_classes)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.dense(hidden_states)
        hidden_states = torch.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states
class BigBirdPegasusPreTrainedModel(PreTrainedModel):
    config_class = BigBirdPegasusConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_param_buffer_assignment = False
    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
    @property
    def dummy_inputs(self):
        pad_token = self.config.pad_token_id
        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
        dummy_inputs = {"attention_mask": input_ids.ne(pad_token), "input_ids": input_ids}
        return dummy_inputs
BIGBIRD_PEGASUS_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`BigBirdPegasusConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
BIGBIRD_PEGASUS_GENERATION_EXAMPLE = r"""
    Summarization example:
    ```python
    >>> from sapiens_transformers import AutoTokenizer, BigBirdPegasusForConditionalGeneration
    >>> model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv")
    >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
    >>> ARTICLE_TO_SUMMARIZE = (
    ...     "The dominant sequence transduction models are based on complex recurrent or convolutional neural "
    ...     "networks in an encoder-decoder configuration. The best performing models also connect the encoder "
    ...     "and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, "
    ...     "based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. "
    ...     "Experiments on two machine translation tasks show these models to be superior in quality "
    ...     "while being more parallelizable and requiring significantly less time to train."
    ... )
    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=4096, return_tensors="pt", truncation=True)
    >>> # Generate Summary
    >>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=15)
    >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    'dominant sequence models are based on recurrent or convolutional neural networks .'
    ```
"""
BIGBIRD_PEGASUS_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Provide for translation and summarization training. By default, the model will create this tensor by
            shifting the `input_ids` to the right, following the paper.
        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
            If you want to change padding behavior, you should read
            [`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
            [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
        decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)` and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
BIGBIRD_PEGASUS_STANDALONE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`ProphetNetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
    def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.attention_type = config.attention_type
        self.block_size = config.block_size
        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop
        embed_dim = config.d_model
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_position_embeddings
        embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
        self.embed_tokens = BigBirdPegasusScaledWordEmbedding(config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale)
        if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight
        self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(config.max_position_embeddings, embed_dim)
        self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) for i in range(config.encoder_layers)])
        self.layernorm_embedding = nn.LayerNorm(embed_dim)
        self.gradient_checkpointing = False
        self.post_init()
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids)
        embed_pos = self.embed_positions(input_shape)
        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        if attention_mask is None: attention_mask = torch.ones(input_shape, device=hidden_states.device)
        attention_mask = attention_mask.long()
        max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.config.block_size
        if self.attention_type == "block_sparse" and input_shape[1] <= max_tokens_to_attend:
            sequence_length = input_shape[1]
            logger.warning(f"Attention type 'block_sparse' is not possible if sequence_length: {sequence_length} <= num global tokens: 2 * config.block_size + min. num sliding tokens: 3 * config.block_size + config.num_random_blocks * config.block_size + additional buffer: config.num_random_blocks * config.block_size = {max_tokens_to_attend} with config.block_size = {self.config.block_size}, config.num_random_blocks = {self.config.num_random_blocks}. Changing attention type to 'original_full'...")
            self.set_attention_type("original_full")
        if self.attention_type == "block_sparse": padding_len, hidden_states, attention_mask = self._pad_to_block_size(hidden_states, attention_mask)
        else: padding_len = 0
        if self.attention_type == "original_full":
            attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
            blocked_encoder_mask = band_mask = from_mask = to_mask = None
        elif self.attention_type == "block_sparse":
            blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(attention_mask, self.block_size)
            attention_mask = None
        else: raise ValueError(f"attention_type can either be original_full or block_sparse, but is {self.attention_type}")
        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        if head_mask is not None:
            if head_mask.size()[0] != len(self.layers): raise ValueError(f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.")
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop: to_drop = True
            if to_drop: layer_outputs = (None, None)
            else:
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(encoder_layer.__call__, hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None),
                    band_mask, from_mask, to_mask, blocked_encoder_mask, blocked_encoder_mask, output_attentions)
                else:
                    layer_outputs = encoder_layer(hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), band_mask=band_mask,
                    from_mask=from_mask, to_mask=to_mask, from_blocked_mask=blocked_encoder_mask, to_blocked_mask=blocked_encoder_mask, output_attentions=output_attentions)
                hidden_states = layer_outputs[0]
            if output_attentions: all_attentions = all_attentions + (layer_outputs[1],)
        hidden_states = self.layernorm_embedding(hidden_states)
        if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
        if padding_len > 0: hidden_states = hidden_states[:, :-padding_len]
        if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        self.encoder_o = hidden_states
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
    def set_attention_type(self, value: str):
        if value not in ["original_full", "block_sparse"]: raise ValueError(f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}")
        if value == self.attention_type: return
        self.attention_type = value
        for layer in self.layers: layer.set_attention_type(value)
    @staticmethod
    def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int):
        batch_size, seq_length = attention_mask.size()
        if seq_length % block_size != 0: raise ValueError(f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block size is {block_size}.")
        def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
            exp_blocked_to_pad = torch.cat([to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2)
            band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
            band_mask.unsqueeze_(1)
            return band_mask
        blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size)
        band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)
        from_mask = attention_mask.view(batch_size, 1, seq_length, 1)
        to_mask = attention_mask.view(batch_size, 1, 1, seq_length)
        return blocked_encoder_mask, band_mask, from_mask, to_mask
    def _pad_to_block_size(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor):
        block_size = self.config.block_size
        batch_size, seq_len = hidden_states.shape[:2]
        padding_len = (block_size - seq_len % block_size) % block_size
        if padding_len > 0:
            logger.warning_once(f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of `config.block_size`: {block_size}")
            pad_id = self.config.pad_token_id
            device = hidden_states.device
            input_ids_padding = torch.ones((batch_size, padding_len), dtype=torch.long, device=device) * pad_id
            inputs_embeds_padding = self.embed_tokens(input_ids_padding)
            hidden_states = torch.cat([hidden_states, inputs_embeds_padding], dim=-2)
            attention_mask = nn.functional.pad(attention_mask, (0, padding_len), value=0)
        return padding_len, hidden_states, attention_mask
class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
    def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.embed_tokens = BigBirdPegasusScaledWordEmbedding(config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale)
        if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight
        self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(config.max_position_embeddings, config.d_model)
        self.layers = nn.ModuleList([BigBirdPegasusDecoderLayer(config) for _ in range(config.decoder_layers)])
        self.layernorm_embedding = nn.LayerNorm(config.d_model)
        self.gradient_checkpointing = False
        self.post_init()
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, value): self.embed_tokens = value
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids)
        attention_mask = _prepare_4d_causal_attention_mask(attention_mask, input_shape, inputs_embeds, past_key_values_length)
        if encoder_hidden_states is not None and encoder_attention_mask is not None: encoder_attention_mask = _prepare_4d_attention_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
        positions = self.embed_positions(input_shape, past_key_values_length)
        positions = positions.to(inputs_embeds.device)
        hidden_states = inputs_embeds + positions
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                use_cache = False
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != len(self.layers): raise ValueError(f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.")
        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop: continue
            past_key_value = past_key_values[idx] if past_key_values is not None else None
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(decoder_layer.__call__, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask,
                head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, output_attentions, use_cache)
            else:
                layer_outputs = decoder_layer(hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask,
                layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
                past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
            if output_attentions:
                all_self_attns += (layer_outputs[1],)
                if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],)
        hidden_states = self.layernorm_embedding(hidden_states)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        next_cache = next_decoder_cache if use_cache else None
        if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states,
        attentions=all_self_attns, cross_attentions=all_cross_attentions)
@add_start_docstrings("The bare BigBirdPegasus Model outputting raw hidden-states without any specific head on top.", BIGBIRD_PEGASUS_START_DOCSTRING)
class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: BigBirdPegasusConfig):
        super().__init__(config)
        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.shared = BigBirdPegasusScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
        self.encoder = BigBirdPegasusEncoder(config, self.shared)
        self.decoder = BigBirdPegasusDecoder(config, self.shared)
        self.post_init()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared
    def _tie_weights(self):
        if self.config.tie_word_embeddings:
            self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
            self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, Seq2SeqModelOutput]:
        if decoder_input_ids is None and decoder_inputs_embeds is None:
            if input_ids is None: raise ValueError("If no `decoder_input_ids` or `decoder_inputs_embeds` are passed, `input_ids` cannot be `None`. Please pass either `input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`.")
            decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id, self.config.decoder_start_token_id)
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions,
            output_hidden_states=output_hidden_states, return_dict=return_dict)
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput(last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask,
        head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        if not return_dict: return decoder_outputs + encoder_outputs
        return Seq2SeqModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states,
        decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state,
        encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
@add_start_docstrings("The BigBirdPegasus Model with a language modeling head. Can be used for summarization.", BIGBIRD_PEGASUS_START_DOCSTRING)
class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel, GenerationMixin):
    base_model_prefix = "model"
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
    _keys_to_ignore_on_load_missing = ["final_logits_bias"]
    def __init__(self, config: BigBirdPegasusConfig):
        super().__init__(config)
        self.model = BigBirdPegasusModel(config)
        self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
        self.post_init()
    def get_encoder(self): return self.model.get_encoder()
    def get_decoder(self): return self.model.get_decoder()
    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        self._resize_final_logits_bias(new_embeddings.weight.shape[0])
        return new_embeddings
    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
        old_num_tokens = self.final_logits_bias.shape[-1]
        if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens]
        else:
            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
        self.register_buffer("final_logits_bias", new_bias)
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
    @add_end_docstrings(BIGBIRD_PEGASUS_GENERATION_EXAMPLE)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, Seq2SeqLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
            use_cache = False
            if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask,
        head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        lm_logits = self.lm_head(outputs[0])
        lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device)
        masked_lm_loss = None
        if labels is not None:
            labels = labels.to(lm_logits.device)
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return Seq2SeqLMOutput(loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions,
        cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions)
    def prepare_inputs_for_generation(self, decoder_input_ids, past_key_values=None, attention_mask=None, decoder_attention_mask=None, head_mask=None,
    decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs):
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]
            if decoder_input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = decoder_input_ids.shape[1] - 1
            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
        return {"input_ids": None, "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache}
    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:])
        return reordered_past
@add_start_docstrings("""
    BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g.
    for GLUE tasks.
    """, BIGBIRD_PEGASUS_START_DOCSTRING)
class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: BigBirdPegasusConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.model = BigBirdPegasusModel(config)
        self.classification_head = BigBirdPegasusClassificationHead(config.d_model, config.d_model, config.num_labels, config.classifier_dropout)
        self.post_init()
    @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None: use_cache = False
        if input_ids is None and inputs_embeds is not None: raise NotImplementedError(f"Passing input embeddings is currently not supported for {self.__class__.__name__}")
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask,
        decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device)
        if len(torch.unique_consecutive(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.")
        sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[:, -1, :]
        logits = self.classification_head(sentence_representation)
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.config.num_labels == 1: self.config.problem_type = "regression"
                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.config.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return Seq2SeqSequenceClassifierOutput(loss=loss, logits=logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state,
        encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions)
@add_start_docstrings("""
    BigBirdPegasus Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
    linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """, BIGBIRD_PEGASUS_START_DOCSTRING)
class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config):
        super().__init__(config)
        config.num_labels = 2
        self.num_labels = config.num_labels
        self.model = BigBirdPegasusModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None,
    start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if start_positions is not None and end_positions is not None: use_cache = False
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask,
        decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[1:]
            return ((total_loss,) + output) if total_loss is not None else output
        return Seq2SeqQuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states,
        encoder_attentions=outputs.encoder_attentions)
class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.decoder = BigBirdPegasusDecoder(config)
    def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs)
class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        config = copy.deepcopy(config)
        config.is_decoder = True
        config.is_encoder_decoder = False
        super().__init__(config)
        self.model = BigBirdPegasusDecoderWrapper(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()
    def get_input_embeddings(self): return self.model.decoder.embed_tokens
    def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def set_decoder(self, decoder): self.model.decoder = decoder
    def get_decoder(self): return self.model.decoder
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
    head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.model.decoder(input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask,
        head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        logits = self.lm_head(outputs[0])
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return CausalLMOutputWithCrossAttentions(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states,
        attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs):
        if attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape)
        if past_key_values: input_ids = input_ids[:, -1:]
        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": use_cache}
    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
        return reordered_past
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
