"""
    #################################################################################################################################################################################################
    # 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
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQuestionAnsweringModelOutput,
Seq2SeqSequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings)
from ...utils.model_parallel_utils import assert_device_map, get_device_map
from .configuration_mt5 import MT5Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MT5Config"
_CHECKPOINT_FOR_DOC = "mt5-small"
PARALLELIZE_DOCSTRING = r"""
    This is an experimental feature and is a subject to change at a moment's notice.
    Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
    it will evenly distribute blocks across all devices.
    Args:
        device_map (`Dict[int, list]`, *optional*):
            A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
            automatically mapped to the first device (for esoteric reasons). That means that the first device should
            have fewer attention modules mapped to it than other devices. For reference, the mt5 models have the
            following number of attention modules:
                - mt5-small: 6
                - mt5-base: 12
                - mt5-large: 24
                - mt5-xl: 24
                - mt5-xxl: 24
    Example:
    ```python
    model = MT5ForConditionalGeneration.from_pretrained("mt5-xl")
    device_map = {0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23]}
    model.parallelize(device_map)
    ```
"""
DEPARALLELIZE_DOCSTRING = r"""
    Moves the model to cpu from a model parallel state.
    Example:
    ```python
    model = MT5ForConditionalGeneration.from_pretrained("Mt5-xl")
    device_map = {0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23]}
    model.parallelize(device_map)  # Splits the model across several devices
    model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
    ```
"""
class MT5LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype)
        return self.weight * hidden_states
class MT5DenseActDense(nn.Module):
    def __init__(self, config: MT5Config):
        super().__init__()
        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]
    def forward(self, hidden_states):
        hidden_states = self.wi(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8): hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states
class MT5DenseGatedActDense(nn.Module):
    def __init__(self, config: MT5Config):
        super().__init__()
        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]
    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8): hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states
class MT5LayerFF(nn.Module):
    def __init__(self, config: MT5Config):
        super().__init__()
        if config.is_gated_act: self.DenseReluDense = MT5DenseGatedActDense(config)
        else: self.DenseReluDense = MT5DenseActDense(config)
        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states):
        forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.DenseReluDense(forwarded_states)
        hidden_states = hidden_states + self.dropout(forwarded_states)
        return hidden_states
class MT5Attention(nn.Module):
    def __init__(self, config: MT5Config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.has_relative_attention_bias = has_relative_attention_bias
        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.relative_attention_max_distance = config.relative_attention_max_distance
        self.d_model = config.d_model
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.key_value_proj_dim
        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
        if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
        self.pruned_heads = set()
        self.gradient_checkpointing = False
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads)
        self.q = prune_linear_layer(self.q, index)
        self.k = prune_linear_layer(self.k, index)
        self.v = prune_linear_layer(self.v, index)
        self.o = prune_linear_layer(self.o, index, dim=1)
        self.n_heads = self.n_heads - len(heads)
        self.inner_dim = self.key_value_proj_dim * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    @staticmethod
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        relative_buckets = 0
        if bidirectional:
            num_buckets //= 2
            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
            relative_position = torch.abs(relative_position)
        else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact
        relative_position_if_large = max_exact + (torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long)
        relative_position_if_large = torch.min(relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1))
        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
        return relative_buckets
    def compute_bias(self, query_length, key_length, device=None):
        if device is None: device = self.relative_attention_bias.weight.device
        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
        relative_position = memory_position - context_position
        relative_position_bucket = self._relative_position_bucket(relative_position, bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets,
        max_distance=self.relative_attention_max_distance)
        values = self.relative_attention_bias(relative_position_bucket)
        values = values.permute([2, 0, 1]).unsqueeze(0)
        return values
    def forward(self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False):
        batch_size, seq_length = hidden_states.shape[:2]
        real_seq_length = seq_length
        if past_key_value is not None:
            if len(past_key_value) != 2: raise ValueError(f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states")
            real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
        key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
        def shape(states): return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
        def unshape(states): return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
        def project(hidden_states, proj_layer, key_value_states, past_key_value):
            if key_value_states is None: hidden_states = shape(proj_layer(hidden_states))
            elif past_key_value is None: hidden_states = shape(proj_layer(key_value_states))
            if past_key_value is not None:
                if key_value_states is None: hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
                elif past_key_value.shape[2] != key_value_states.shape[1]: hidden_states = shape(proj_layer(key_value_states))
                else: hidden_states = past_key_value
            return hidden_states
        query_states = shape(self.q(hidden_states))
        key_states = project(hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None)
        value_states = project(hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None)
        scores = torch.matmul(query_states, key_states.transpose(3, 2))
        if position_bias is None:
            if not self.has_relative_attention_bias:
                position_bias = torch.zeros((1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype)
                if self.gradient_checkpointing and self.training: position_bias.requires_grad = True
            else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
            if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
            if mask is not None: position_bias = position_bias + mask
        if self.pruned_heads:
            mask = torch.ones(position_bias.shape[1])
            mask[list(self.pruned_heads)] = 0
            position_bias_masked = position_bias[:, mask.bool()]
        else: position_bias_masked = position_bias
        scores += position_bias_masked
        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask
        attn_output = unshape(torch.matmul(attn_weights, value_states))
        attn_output = self.o(attn_output)
        present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
        if output_attentions: outputs = outputs + (attn_weights,)
        return outputs
class MT5LayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.SelfAttention = MT5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask,
        past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions)
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]
        return outputs
class MT5LayerCrossAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.EncDecAttention = MT5Attention(config, has_relative_attention_bias=False)
        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False,
    query_length=None, output_attentions=False):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias,
        layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions)
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]
        return outputs
class MT5Block(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.layer = nn.ModuleList()
        self.layer.append(MT5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
        if self.is_decoder: self.layer.append(MT5LayerCrossAttention(config))
        self.layer.append(MT5LayerFF(config))
    def forward(self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
    layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True):
        if past_key_value is not None:
            if not self.is_decoder: logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.")
            expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
            if len(past_key_value) != expected_num_past_key_values: raise ValueError(f"There should be {expected_num_past_key_values} past states. {'2 (key / value) for cross attention. ' if expected_num_past_key_values == 4 else ''}Got {len(past_key_value)} past key / value states")
            self_attn_past_key_value = past_key_value[:2]
            cross_attn_past_key_value = past_key_value[2:]
        else: self_attn_past_key_value, cross_attn_past_key_value = None, None
        self_attention_outputs = self.layer[0](hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions)
        hidden_states, present_key_value_state = self_attention_outputs[:2]
        attention_outputs = self_attention_outputs[2:]
        if hidden_states.dtype == torch.float16:
            clamp_value = torch.where(torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max)
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        do_cross_attention = self.is_decoder and encoder_hidden_states is not None
        if do_cross_attention:
            if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2]
            else: query_length = None
            cross_attention_outputs = self.layer[1](hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias,
            layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions)
            hidden_states = cross_attention_outputs[0]
            if hidden_states.dtype == torch.float16:
                clamp_value = torch.where(torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max)
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
            if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1]
            attention_outputs = attention_outputs + cross_attention_outputs[2:]
        hidden_states = self.layer[-1](hidden_states)
        if hidden_states.dtype == torch.float16:
            clamp_value = torch.where(torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max)
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        outputs = (hidden_states,)
        if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs
        else: outputs = outputs + attention_outputs
        return outputs
def load_tf_weights_in_mt5(model, config, tf_checkpoint_path):
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.")
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    init_vars = tf.train.list_variables(tf_path)
    names = []
    tf_weights = {}
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        tf_weights[name] = array
    for txt_name in names:
        name = txt_name.split("/")
        if any(n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name):
            logger.info(f"Skipping {'/'.join(name)}")
            tf_weights.pop(txt_name, None)
            continue
        if "_slot_" in name[-1]:
            logger.info(f"Skipping {'/'.join(name)}")
            tf_weights.pop(txt_name, None)
            continue
        pointer = model
        array = tf_weights[txt_name]
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name)
            else: scope_names = [m_name]
            if scope_names[0] in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight")
            elif scope_names[0] == "self_attention":
                pointer = getattr(pointer, "layer")
                pointer = pointer[0]
            elif scope_names[0] == "enc_dec_attention":
                pointer = getattr(pointer, "layer")
                pointer = pointer[1]
            elif scope_names[0] == "dense_relu_dense":
                pointer = getattr(pointer, "layer")
                pointer = pointer[2]
            elif scope_names[0] == "rms_norm":
                if hasattr(pointer, "layer_norm"): pointer = getattr(pointer, "layer_norm")
                elif hasattr(pointer, "final_layer_norm"): pointer = getattr(pointer, "final_layer_norm")
            elif scope_names[0] == "scale": pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias")
            elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier")
            elif scope_names[0] == "decoder" and name[1] == "logits": continue
            elif scope_names[0] == "logits": pointer = getattr(pointer, "lm_head")
            elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit():
                pointer = getattr(pointer, f"wi_{scope_names[1]}")
                continue
            else:
                try: pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info(f"Skipping {'/'.join(name)}")
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if scope_names[0] not in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight")
        if scope_names[0] != "embedding":
            logger.info(f"Transposing numpy weight of shape {array.shape} for {name}")
            array = np.transpose(array)
        try: assert (pointer.shape == array.shape), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array.astype(np.float32))
        tf_weights.pop(txt_name, None)
    logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
    return model
class MT5ClassificationHead(nn.Module):
    def __init__(self, config: MT5Config):
        super().__init__()
        self.dense = nn.Linear(config.d_model, config.d_model)
        self.dropout = nn.Dropout(p=config.classifier_dropout)
        self.out_proj = nn.Linear(config.d_model, config.num_labels)
    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 MT5PreTrainedModel(PreTrainedModel):
    config_class = MT5Config
    load_tf_weights = load_tf_weights_in_mt5
    base_model_prefix = "transformer"
    is_parallelizable = True
    supports_gradient_checkpointing = True
    _no_split_modules = ["MT5Block"]
    _keep_in_fp32_modules = ["wo"]
    @property
    def dummy_inputs(self):
        input_ids = torch.tensor(DUMMY_INPUTS)
        input_mask = torch.tensor(DUMMY_MASK)
        dummy_inputs = {"decoder_input_ids": input_ids, "input_ids": input_ids, "decoder_attention_mask": input_mask}
        return dummy_inputs
    def _init_weights(self, module):
        factor = self.config.initializer_factor
        if isinstance(module, MT5LayerNorm): module.weight.data.fill_(factor * 1.0)
        elif isinstance(module, (MT5Model, MT5ForConditionalGeneration, MT5EncoderModel, MT5ForQuestionAnswering)):
            module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
            if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
            if hasattr(module, "qa_outputs"):
                module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
                module.qa_outputs.bias.data.zero_()
        elif isinstance(module, MT5ForTokenClassification):
            if hasattr(module, "classifier"):
                module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
                module.classifier.bias.data.zero_()
        elif isinstance(module, MT5ClassificationHead):
            module.dense.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.dense, "bias") and module.dense.bias is not None: module.dense.bias.data.zero_()
            module.out_proj.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None: module.out_proj.bias.data.zero_()
        elif isinstance(module, MT5DenseActDense):
            module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_()
        elif isinstance(module, MT5DenseGatedActDense):
            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_()
            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_()
        elif isinstance(module, MT5Attention):
            d_model = self.config.d_model
            key_value_proj_dim = self.config.d_kv
            n_heads = self.config.num_heads
            module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
            module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
            module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
            module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
            if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id
        if decoder_start_token_id is None: raise ValueError("self.model.config.decoder_start_token_id has to be defined. In MT5 it is usually set to the pad_token_id. See MT5 docs for more information.")
        if is_torch_fx_proxy(input_ids):
            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
        else:
            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 MT5Stack(MT5PreTrainedModel):
    def __init__(self, config, embed_tokens=None):
        super().__init__(config)
        self.embed_tokens = embed_tokens
        self.is_decoder = config.is_decoder
        self.block = nn.ModuleList([MT5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)])
        self.final_layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.post_init()
        self.model_parallel = False
        self.device_map = None
        self.gradient_checkpointing = False
    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn("`MT5Stack.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0, 'block.1': 1, ...}", FutureWarning)
        self.device_map = (get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.block))
        self.model_parallel = True
        self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
        self.last_device = "cuda:" + str(max(self.device_map.keys()))
        for k, v in self.device_map.items():
            for layer in v:
                cuda_device = "cuda:" + str(k)
                self.block[layer] = self.block[layer].to(cuda_device)
        self.embed_tokens = self.embed_tokens.to(self.first_device)
        self.final_layer_norm = self.final_layer_norm.to(self.last_device)
    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn("Like `parallelize`, `deparallelize` is deprecated and will be removed in v1 of Sapiens Transformers.", FutureWarning)
        self.model_parallel = False
        self.device_map = None
        self.first_device = "cpu"
        self.last_device = "cpu"
        for i in range(len(self.block)): self.block[i] = self.block[i].to("cpu")
        self.embed_tokens = self.embed_tokens.to("cpu")
        self.final_layer_norm = self.final_layer_norm.to("cpu")
        torch.cuda.empty_cache()
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings
    def forward(self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None,
    past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None):
        if self.model_parallel:
            torch.cuda.set_device(self.first_device)
            self.embed_tokens = self.embed_tokens.to(self.first_device)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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:
            err_msg_prefix = "decoder_" if self.is_decoder else ""
            raise ValueError(f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}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:
            err_msg_prefix = "decoder_" if self.is_decoder else ""
            raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
        if inputs_embeds is None:
            if self.embed_tokens is None: raise ValueError("You have to initialize the model with valid token embeddings")
            inputs_embeds = self.embed_tokens(input_ids)
        batch_size, seq_length = input_shape
        mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
        if use_cache is True:
            if not self.is_decoder: raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
        if past_key_values is None: past_key_values = [None] * len(self.block)
        if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
        if self.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device, dtype=torch.long)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else: encoder_extended_attention_mask = None
        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
        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
        cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
        present_key_value_states = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and self.is_decoder) else None
        position_bias = None
        encoder_decoder_position_bias = None
        hidden_states = self.dropout(inputs_embeds)
        for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
            layer_head_mask = head_mask[i]
            cross_attn_layer_head_mask = cross_attn_head_mask[i]
            if self.model_parallel:
                torch.cuda.set_device(hidden_states.device)
                if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device)
                if position_bias is not None: position_bias = position_bias.to(hidden_states.device)
                if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.to(hidden_states.device)
                if encoder_extended_attention_mask is not None: encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device)
                if encoder_decoder_position_bias is not None: encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device)
                if layer_head_mask is not None: layer_head_mask = layer_head_mask.to(hidden_states.device)
                if cross_attn_layer_head_mask is not None: cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device)
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(layer_module.forward, hidden_states, extended_attention_mask, position_bias, encoder_hidden_states,
                encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, use_cache, output_attentions)
            else:
                layer_outputs = layer_module(hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask,
                cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions)
            if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
            hidden_states, present_key_value_state = layer_outputs[:2]
            position_bias = layer_outputs[2]
            if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
            if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,)
            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[3],)
                if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
            if self.model_parallel:
                for k, v in self.device_map.items():
                    if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1))
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states,
        attentions=all_attentions, cross_attentions=all_cross_attentions)
MT5_START_DOCSTRING = r"""
    The MT5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
    Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
    Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
    text-to-text denoising generative setting.
    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, pruning heads
    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 ([`MT5Config`]): 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.
"""
MT5_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
            should be able to pad the inputs on both the right and the left.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.
            [What are input IDs?](../glossary#input-ids)
            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
        attention_mask (`torch.FloatTensor` 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*):
            Indices of decoder input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are decoder input IDs?](../glossary#decoder-input-ids)
            MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
            Training](./mt5#training).
        decoder_attention_mask (`torch.BoolTensor` 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.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
                Mask to nullify selected heads of the cross-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)` 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))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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.
"""
MT5_ENCODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
            should be able to pad the inputs on both the right and the left.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.
            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
        attention_mask (`torch.FloatTensor` 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)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        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.
        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.
"""
__HEAD_MASK_WARNING_MSG = """
The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
num_heads)`.
"""
@add_start_docstrings("The bare MT5 Model transformer outputting raw hidden-states without any specific head on top.", MT5_START_DOCSTRING)
class MT5Model(MT5PreTrainedModel):
    model_type = "mt5"
    config_class = MT5Config
    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: MT5Config):
        super().__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = MT5Stack(encoder_config, self.shared)
        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = MT5Stack(decoder_config, self.shared)
        self.post_init()
        self.model_parallel = False
        self.device_map = None
    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn("`T5Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0': 0, 'encoder.block.1': 1, ...}", FutureWarning)
        self.device_map = (get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.encoder.block))
        self.encoder.parallelize(self.device_map)
        self.decoder.parallelize(self.device_map)
        self.model_parallel = True
    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn("Like `parallelize`, `deparallelize` is deprecated and will be removed in v1 of Sapiens Transformers.", FutureWarning)
        self.encoder.deparallelize()
        self.decoder.deparallelize()
        self.encoder = self.encoder.to("cpu")
        self.decoder = self.decoder.to("cpu")
        self.model_parallel = False
        self.device_map = None
        torch.cuda.empty_cache()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    inputs_embeds: Optional[torch.Tensor] = None, decoder_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) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
        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 head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, 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)
        hidden_states = encoder_outputs[0]
        if self.model_parallel:
            torch.cuda.set_device(self.decoder.first_device)
            hidden_states = hidden_states.to(self.decoder.first_device)
            if decoder_input_ids is not None: decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
            if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device)
            if decoder_attention_mask is not None: decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values,
        encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, 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("MT5 Model with a `language modeling` head on top.", MT5_START_DOCSTRING)
class MT5ForConditionalGeneration(MT5PreTrainedModel, GenerationMixin):
    model_type = "mt5"
    config_class = MT5Config
    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
    def __init__(self, config: MT5Config):
        super().__init__(config)
        self.model_dim = config.d_model
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = MT5Stack(encoder_config, self.shared)
        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = MT5Stack(decoder_config, self.shared)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
        self.post_init()
        self.model_parallel = False
        self.device_map = None
    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn("`T5ForConditionalGeneration.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0': 0, 'encoder.block.1': 1, ...}", FutureWarning)
        self.device_map = (get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.encoder.block))
        self.encoder.parallelize(self.device_map)
        self.decoder.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.decoder.first_device)
        self.model_parallel = True
    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn("Like `parallelize`, `deparallelize` is deprecated and will be removed in v1 of Sapiens Transformers.", FutureWarning)
        self.encoder.deparallelize()
        self.decoder.deparallelize()
        self.encoder = self.encoder.to("cpu")
        self.decoder = self.decoder.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.model_parallel = False
        self.device_map = None
        torch.cuda.empty_cache()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def get_output_embeddings(self): return self.lm_head
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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[torch.FloatTensor], Seq2SeqLMOutput]:
        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 head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, 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)
        hidden_states = encoder_outputs[0]
        if self.model_parallel: torch.cuda.set_device(self.decoder.first_device)
        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = self._shift_right(labels)
        if self.model_parallel:
            torch.cuda.set_device(self.decoder.first_device)
            hidden_states = hidden_states.to(self.decoder.first_device)
            if decoder_input_ids is not None: decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
            if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device)
            if decoder_attention_mask is not None: decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values,
        encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = decoder_outputs[0]
        if self.model_parallel:
            torch.cuda.set_device(self.encoder.first_device)
            self.lm_head = self.lm_head.to(self.encoder.first_device)
            sequence_output = sequence_output.to(self.lm_head.weight.device)
        if self.config.tie_word_embeddings: sequence_output = sequence_output * (self.model_dim**-0.5)
        lm_logits = self.lm_head(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            labels = labels.to(lm_logits.device)
            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
        if not return_dict:
            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
            return ((loss,) + output) if loss is not None else output
        return Seq2SeqLMOutput(loss=loss, logits=lm_logits, 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)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, decoder_attention_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 input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = input_ids.shape[1] - 1
            input_ids = input_ids[:, remove_prefix_length:]
        return {"decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask,
        "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "decoder_attention_mask": decoder_attention_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 self._shift_right(labels)
    def _reorder_cache(self, past_key_values, beam_idx):
        if past_key_values is None:
            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
            return past_key_values
        reordered_decoder_past = ()
        for layer_past_states in past_key_values:
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states: reordered_layer_past_states = reordered_layer_past_states + (layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),)
            if reordered_layer_past_states[0].shape != layer_past_states[0].shape: raise ValueError(f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched")
            if len(reordered_layer_past_states) != len(layer_past_states): raise ValueError(f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched")
            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
        return reordered_decoder_past
@add_start_docstrings("The bare MT5 Model transformer outputting encoder's raw hidden-states without any specific head on top.", MT5_START_DOCSTRING)
class MT5EncoderModel(MT5PreTrainedModel):
    model_type = "mt5"
    config_class = MT5Config
    _tied_weights_keys = ["encoder.embed_tokens.weight"]
    def __init__(self, config: MT5Config):
        super().__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        encoder_config = copy.deepcopy(config)
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = MT5Stack(encoder_config, self.shared)
        self.post_init()
        self.model_parallel = False
        self.device_map = None
    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn("`T5EncoderModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0, 'block.1': 1, ...}", FutureWarning)
        self.device_map = (get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.encoder.block))
        self.encoder.parallelize(self.device_map)
        self.model_parallel = True
    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn("Like `parallelize`, `deparallelize` is deprecated and will be removed in v1 of Sapiens Transformers.", FutureWarning)
        self.encoder.deparallelize()
        self.encoder = self.encoder.to("cpu")
        self.model_parallel = False
        self.device_map = None
        torch.cuda.empty_cache()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
    def get_encoder(self): return self.encoder
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(MT5_ENCODER_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        return encoder_outputs
@add_start_docstrings("MT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", MT5_START_DOCSTRING)
class MT5ForSequenceClassification(MT5PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: MT5Config):
        super().__init__(config)
        self.transformer = MT5Model(config)
        self.classification_head = MT5ClassificationHead(config)
        self.post_init()
        self.model_parallel = False
    @add_start_docstrings_to_model_forward(MT5_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__}")
        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 = self._shift_right(input_ids)
        outputs = self.transformer(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]
        eos_mask = input_ids.eq(self.config.eos_token_id).to(sequence_output.device)
        if len(torch.unique_consecutive(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.")
        batch_size, _, hidden_size = sequence_output.shape
        sentence_representation = sequence_output[eos_mask, :].view(batch_size, -1, hidden_size)[:, -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("MT5 Encoder Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.", MT5_START_DOCSTRING)
class MT5ForTokenClassification(MT5PreTrainedModel):
    _tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
    def __init__(self, config: MT5Config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = MT5EncoderModel(config)
        self.dropout = nn.Dropout(config.classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
    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, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.transformer(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)
        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits, outputs[2:-1])
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("MT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).", MT5_START_DOCSTRING)
class MT5ForQuestionAnswering(MT5PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: MT5Config):
        super().__init__(config)
        self.model_dim = config.d_model
        self.shared = nn.Embedding(config.vocab_size, config.d_model)
        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = MT5Stack(encoder_config, self.shared)
        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = MT5Stack(decoder_config, self.shared)
        self.num_labels = config.num_labels
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
        self.model_parallel = False
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = 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[torch.FloatTensor], Seq2SeqQuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        if start_positions is not None and end_positions is not None: use_cache = False
        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 = self._shift_right(input_ids)
        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 head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, 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)
        hidden_states = encoder_outputs[0]
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=None,
        encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = decoder_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).to(start_logits.device)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1).to(end_logits.device)
            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) + decoder_outputs[1:] + encoder_outputs
            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=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)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
