"""
    #################################################################################################################################################################################################
    # 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 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 ...utils import (DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings)
from .configuration_umt5 import UMT5Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "UMT5Config"
_CHECKPOINT_FOR_DOC = "google/umt5-small"
class UMT5LayerNorm(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 UMT5DenseActDense(nn.Module):
    def __init__(self, config: UMT5Config):
        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 UMT5DenseGatedActDense(nn.Module):
    def __init__(self, config: UMT5Config):
        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 UMT5LayerFF(nn.Module):
    def __init__(self, config: UMT5Config):
        super().__init__()
        if config.is_gated_act: self.DenseReluDense = UMT5DenseGatedActDense(config)
        else: self.DenseReluDense = UMT5DenseActDense(config)
        self.layer_norm = UMT5LayerNorm(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 UMT5Attention(nn.Module):
    def __init__(self, config, 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()
    def _shape(self, projection: torch.Tensor) -> torch.Tensor:
        new_projection_shape = projection.size()[:-1] + (self.n_heads, self.key_value_proj_dim)
        new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
        return new_projection
    def _relative_position_bucket(self, relative_position):
        relative_buckets = 0
        num_buckets = self.relative_attention_num_buckets
        max_distance = self.relative_attention_max_distance
        if not self.is_decoder:
            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
        log_ratio = torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact)
        log_ratio = log_ratio * (num_buckets - max_exact)
        relative_position_if_large = max_exact + log_ratio.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)
        values = self.relative_attention_bias(relative_position_bucket)
        values = values.permute([2, 0, 1]).unsqueeze(0)
        return values
    def forward(self, hidden_states: torch.Tensor, encoder_hidden_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):
        is_cross_attention = encoder_hidden_states is not None
        batch_size, seq_length = hidden_states.shape[:2]
        current_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
        if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        else:
            key_states = self._shape(self.k(current_states))
            value_states = self._shape(self.v(current_states))
            if past_key_value is not None and not is_cross_attention:
                key_states = torch.cat([past_key_value[0], key_states], dim=2)
                value_states = torch.cat([past_key_value[1], value_states], dim=2)
        query_states = self._shape(self.q(hidden_states))
        attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
        if self.has_relative_attention_bias:
            query_length = seq_length
            if past_key_value is not None: query_length += past_key_value[0].shape[2]
            position_bias = self.compute_bias(query_length, key_states.size(2), device=attention_scores.device)
        else: position_bias = torch.zeros((1, self.n_heads, seq_length, key_states.size(2)), device=attention_scores.device, dtype=attention_scores.dtype, requires_grad=self.training)
        if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
        if attention_mask is not None: position_bias = position_bias + attention_mask
        if self.is_decoder: past_key_value = (key_states, value_states)
        attention_scores += position_bias
        attn_weights = nn.functional.softmax(attention_scores.float(), dim=-1).type_as(attention_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
        context_states = torch.matmul(attn_weights, value_states)
        context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1)
        attn_output = self.o(context_states)
        return attn_output, attn_weights, past_key_value
class UMT5LayerSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.SelfAttention = UMT5Attention(config, has_relative_attention_bias=True)
        self.layer_norm = UMT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, attention_mask=None, layer_head_mask=None, past_key_value=None):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(normed_hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, past_key_value=past_key_value)
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]
        return outputs
class UMT5LayerCrossAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.EncDecAttention = UMT5Attention(config, has_relative_attention_bias=False)
        self.layer_norm = UMT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, layer_head_mask=None, past_key_value=None):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(normed_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, past_key_value=past_key_value)
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]
        return outputs
class UMT5Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.layer = nn.ModuleList()
        self.layer.append(UMT5LayerSelfAttention(config))
        if self.is_decoder: self.layer.append(UMT5LayerCrossAttention(config))
        self.layer.append(UMT5LayerFF(config))
    def forward(self, hidden_states, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, layer_head_mask=None, cross_attn_layer_head_mask=None,
    past_key_value=None, use_cache=False, output_attentions=False):
        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.layer[0](hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value)
        if hidden_states.dtype == torch.float16:
            max_dtype = torch.finfo(hidden_states.dtype).max
            clamp_value = torch.where(torch.isinf(hidden_states).any(), max_dtype - 1000, max_dtype)
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        cross_attn_present_key_value = None
        cross_attn_weights = None
        do_cross_attention = self.is_decoder and encoder_hidden_states is not None
        if do_cross_attention:
            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.layer[1](hidden_states, encoder_hidden_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)
            if hidden_states.dtype == torch.float16:
                max_dtype = torch.finfo(hidden_states.dtype).max
                clamp_value = torch.where(torch.isinf(hidden_states).any(), max_dtype - 1000, max_dtype)
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
            present_key_value += cross_attn_present_key_value
        hidden_states = self.layer[-1](hidden_states)
        if hidden_states.dtype == torch.float16:
            max_dtype = torch.finfo(hidden_states.dtype).max
            clamp_value = torch.where(torch.isinf(hidden_states).any(), max_dtype - 1000, max_dtype)
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        outputs = (hidden_states, present_key_value)
        if output_attentions: outputs += (self_attn_weights, cross_attn_weights)
        return outputs
class UMT5ClassificationHead(nn.Module):
    def __init__(self, config: UMT5Config):
        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 UMT5PreTrainedModel(PreTrainedModel):
    config_class = UMT5Config
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["UMT5Block"]
    _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, UMT5LayerNorm): module.weight.data.fill_(factor * 1.0)
        elif isinstance(module, (UMT5Model, UMT5ForConditionalGeneration, UMT5EncoderModel, UMT5ForQuestionAnswering)):
            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, UMT5ForTokenClassification):
            if hasattr(module, "classifier"):
                module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
                module.classifier.bias.data.zero_()
        elif isinstance(module, UMT5ClassificationHead):
            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, UMT5DenseActDense):
            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, UMT5DenseGatedActDense):
            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, UMT5Attention):
            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 UMT5 it is usually set to the pad_token_id. See UMT5 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 UMT5Stack(UMT5PreTrainedModel):
    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([UMT5Block(config) for i in range(config.num_layers)])
        self.final_layer_norm = UMT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.gradient_checkpointing = False
        self.post_init()
    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):
        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 attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
            encoder_seq_length = encoder_hidden_states.shape[1]
            encoder_attention_mask = torch.ones(batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long)
        if past_key_values is None: past_key_values = [None] * len(self.block)
        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)
            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
        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 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, encoder_hidden_states, encoder_extended_attention_mask,
                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, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask,
                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)
                hidden_states = layer_outputs[0]
            if use_cache: present_key_value_states += (layer_outputs[1],)
            if output_attentions:
                all_attentions += (layer_outputs[2],)
                if self.is_decoder: all_cross_attentions += (layer_outputs[3],)
        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)
UMT5_START_DOCSTRING = r"""
    The UMT5 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 ([`UMT5Config`]): 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.
"""
UMT5_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. UMT5 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 [UMT5 Training](./umt5#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)
            UMT5 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 [UMT5
            Training](./umt5#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.
"""
UMT5_ENCODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. UMT5 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 [UMT5 Training](./umt5#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.
"""
@add_start_docstrings("The bare UMT5 Model transformer outputting raw hidden-states without any specific head on top.", UMT5_START_DOCSTRING)
class UMT5Model(UMT5PreTrainedModel):
    model_type = "umt5"
    config_class = UMT5Config
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config):
        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 = UMT5Stack(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 = UMT5Stack(decoder_config, self.shared)
        self.post_init()
    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 _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
    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(UMT5_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 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=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("UMT5 Model with a `language modeling` head on top.", UMT5_START_DOCSTRING)
class UMT5ForConditionalGeneration(UMT5PreTrainedModel, GenerationMixin):
    model_type = "umt5"
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
    def __init__(self, config):
        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 = UMT5Stack(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 = UMT5Stack(decoder_config, self.shared)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
        self.post_init()
    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 _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 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(UMT5_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 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 labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = self._shift_right(labels)
        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.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)
    @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
@add_start_docstrings("The bare UMT5 Model transformer outputting encoder's raw hidden-states without any specific head on top.", UMT5_START_DOCSTRING)
class UMT5EncoderModel(UMT5PreTrainedModel):
    model_type = "umt5"
    _tied_weights_keys = ["encoder.embed_tokens.weight"]
    def __init__(self, config):
        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 = UMT5Stack(encoder_config, self.shared)
        self.post_init()
    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 _tie_weights(self):
        if self.config.tie_word_embeddings: self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
    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(UMT5_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("UMT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", UMT5_START_DOCSTRING)
class UMT5ForSequenceClassification(UMT5PreTrainedModel):
    _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: UMT5Config):
        super().__init__(config)
        self.transformer = UMT5Model(config)
        self.classification_head = UMT5ClassificationHead(config)
        self.post_init()
        self.model_parallel = False
    @add_start_docstrings_to_model_forward(UMT5_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("UMT5 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.", UMT5_START_DOCSTRING)
class UMT5ForTokenClassification(UMT5PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
    _tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
    def __init__(self, config: UMT5Config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = UMT5EncoderModel(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(UMT5_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("UMT5 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`).", UMT5_START_DOCSTRING)
class UMT5ForQuestionAnswering(UMT5PreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config):
        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 = UMT5Stack(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 = UMT5Stack(decoder_config, self.shared)
        self.num_labels = config.num_labels
        self.qa_outputs = nn.Linear(config.d_model, config.num_labels)
        self.post_init()
    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 _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(UMT5_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 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.                                                                 #
    #################################################################################################################################################################################################
"""
