


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 							__lowerCAmelCase      (			__magic_name__      ):
 _lowercase:       str						=				torch.exp(lowerCAmelCase__      )
 _lowercase:       Union[str, Any]						=				torch.sum(lowerCAmelCase__ ,   dim=1      )  # sum of exp(x_i)
 _lowercase:       Any						=				torch.sum(x * exp_x ,   dim=1      )  # sum of x_i * exp(x_i)
 return torch.log(lowerCAmelCase__      ) - B / A



class        A     (		nn.Module	):







     '''simple docstring'''







     def __init__(				self    :							List[Any]			,				_UpperCamelCase    :							Tuple):
      super().__init__()
      _lowercase:       int						=				config.output_attentions
      _lowercase:       List[str]						=				config.output_hidden_states
      _lowercase:       List[Any]						=				nn.ModuleList([BertLayer(_UpperCamelCase) for _ in range(config.num_hidden_layers)])
      _lowercase:       List[Any]						=				nn.ModuleList([BertHighway(_UpperCamelCase) for _ in range(config.num_hidden_layers)])

      _lowercase:       Optional[Any]						=				[-1 for _ in range(config.num_hidden_layers)]

     def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							int):
      if (type(_UpperCamelCase) is float) or (type(_UpperCamelCase) is int):
       for i in range(len(self.early_exit_entropy)):
        _lowercase:       Optional[int]						=				x
      else:
       _lowercase:       List[str]						=				x

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							Any):
      _lowercase:       Any						=				pooler.state_dict()
      for highway in self.highway:
       for name, param in highway.pooler.state_dict().items():
        param.copy_(loaded_model[name])






     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Optional[int]=None			,				_UpperCamelCase    :							List[Any]=None			,				_UpperCamelCase    :							Dict=None			,				_UpperCamelCase    :							Optional[Any]=None			,				):
      _lowercase:       Optional[int]						=				()
      _lowercase:       List[Any]						=				()
      _lowercase:       Dict						=				()
      for i, layer_module in enumerate(self.layer):
       if self.output_hidden_states:
        _lowercase:       List[str]						=				all_hidden_states + (hidden_states,)

       _lowercase:       Dict						=				layer_module(
           _UpperCamelCase			,				_UpperCamelCase			,				head_mask[i]			,				_UpperCamelCase			,				_UpperCamelCase)
       _lowercase:       Dict						=				layer_outputs[0]

       if self.output_attentions:
        _lowercase:       int						=				all_attentions + (layer_outputs[1],)

       _lowercase:       List[str]						=				(hidden_states,)
       if self.output_hidden_states:
        _lowercase:       Union[str, Any]						=				current_outputs + (all_hidden_states,)
       if self.output_attentions:
        _lowercase:       int						=				current_outputs + (all_attentions,)

       _lowercase:       str						=				self.highway[i](_UpperCamelCase)
       # logits, pooled_output

       if not self.training:
        _lowercase:       Tuple						=				highway_exit[0]
        _lowercase:       str						=				entropy(_UpperCamelCase)
        _lowercase:       Optional[int]						=				highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
        _lowercase:       Tuple						=				all_highway_exits + (highway_exit,)

        if highway_entropy < self.early_exit_entropy[i]:
         _lowercase:       Union[str, Any]						=				(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
         raise HighwayException(_UpperCamelCase			,				i + 1)
       else:
        _lowercase:       Optional[int]						=				all_highway_exits + (highway_exit,)

        # Add last layer
      if self.output_hidden_states:
       _lowercase:       Tuple						=				all_hidden_states + (hidden_states,)

      _lowercase:       str						=				(hidden_states,)
      if self.output_hidden_states:
       _lowercase:       Optional[int]						=				outputs + (all_hidden_states,)
      if self.output_attentions:
       _lowercase:       Tuple						=				outputs + (all_attentions,)

      _lowercase:       Tuple						=				outputs + (all_highway_exits,)
      return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """	, lowerCamelCase_	, )
class        A     (		lowerCamelCase_	):







     '''simple docstring'''







     def __init__(				self    :							Optional[int]			,				_UpperCamelCase    :							Any):
      super().__init__(_UpperCamelCase)
      _lowercase:       Any						=				config

      _lowercase:       List[str]						=				BertEmbeddings(_UpperCamelCase)
      _lowercase:       List[str]						=				DeeBertEncoder(_UpperCamelCase)
      _lowercase:       Dict						=				BertPooler(_UpperCamelCase)

      self.init_weights()

     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      self.encoder.init_highway_pooler(self.pooler)

     def 					UpperCAmelCase__       (				self    :							Dict):
      return self.embeddings.word_embeddings

     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							Dict):
      _lowercase:       Optional[int]						=				value

     def 					UpperCAmelCase__       (				self    :							int			,				_UpperCamelCase    :							List[str]):
      for layer, heads in heads_to_prune.items():
       self.encoder.layer[layer].attention.prune_heads(_UpperCamelCase)






     @add_start_docstrings_to_model_forward(_UpperCamelCase)
     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							Optional[Any]=None			,				_UpperCamelCase    :							Optional[Any]=None			,				_UpperCamelCase    :							Optional[Any]=None			,				_UpperCamelCase    :							Optional[int]=None			,				_UpperCamelCase    :							Optional[Any]=None			,				_UpperCamelCase    :							int=None			,				_UpperCamelCase    :							int=None			,				_UpperCamelCase    :							str=None			,				):
      if input_ids is not None and inputs_embeds is not None:
       raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
      elif input_ids is not None:
       _lowercase:       Optional[Any]						=				input_ids.size()
      elif inputs_embeds is not None:
       _lowercase:       int						=				inputs_embeds.size()[:-1]
      else:
       raise ValueError("You have to specify either input_ids or inputs_embeds")

      _lowercase:       Optional[Any]						=				input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
       _lowercase:       Union[str, Any]						=				torch.ones(_UpperCamelCase			,				device=_UpperCamelCase)
      if encoder_attention_mask is None:
       _lowercase:       List[str]						=				torch.ones(_UpperCamelCase			,				device=_UpperCamelCase)
      if token_type_ids is None:
       _lowercase:       List[str]						=				torch.zeros(_UpperCamelCase			,				dtype=torch.long			,				device=_UpperCamelCase)

      # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
      # ourselves in which case we just need to make it broadcastable to all heads.
      _lowercase:       Any						=				self.get_extended_attention_mask(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase)

      # If a 2D ou 3D attention mask is provided for the cross-attention
      # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
      if encoder_attention_mask.dim() == 3:
       _lowercase:       List[str]						=				encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.dim() == 2:
       _lowercase:       Optional[Any]						=				encoder_attention_mask[:, None, None, :]

      _lowercase:       Union[str, Any]						=				encoder_extended_attention_mask.to(
          dtype=next(self.parameters()).dtype)  # fp16 compatibility
      _lowercase:       Union[str, Any]						=				(1.0 - encoder_extended_attention_mask) * -10_000.0

      # Prepare head mask if needed
      # 1.0 in head_mask indicate we keep the head
      # attention_probs has shape bsz x n_heads x N x N
      # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
      # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
      _lowercase:       Dict						=				self.get_head_mask(_UpperCamelCase			,				self.config.num_hidden_layers)

      _lowercase:       List[str]						=				self.embeddings(
          input_ids=_UpperCamelCase			,				position_ids=_UpperCamelCase			,				token_type_ids=_UpperCamelCase			,				inputs_embeds=_UpperCamelCase)
      _lowercase:       List[Any]						=				self.encoder(
          _UpperCamelCase			,				attention_mask=_UpperCamelCase			,				head_mask=_UpperCamelCase			,				encoder_hidden_states=_UpperCamelCase			,				encoder_attention_mask=_UpperCamelCase			,				)
      _lowercase:       Optional[int]						=				encoder_outputs[0]
      _lowercase:       Optional[int]						=				self.pooler(_UpperCamelCase)

      _lowercase:       List[str]						=				(
          sequence_output,
          pooled_output,
      ) + encoder_outputs[
          1:
      ]  # add hidden_states and attentions if they are here
      return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class        A     (		lowerCamelCase_	):







     '''simple docstring'''







     def __init__(				self    :							List[Any]			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							List[str]):
      _lowercase:       int						=				message
      _lowercase:       Optional[int]						=				exit_layer  # start from 1!






class        A     (		nn.Module	):







     '''simple docstring'''







     def __init__(				self    :							Optional[int]			,				_UpperCamelCase    :							Optional[int]):
      super().__init__()
      _lowercase:       Optional[int]						=				BertPooler(_UpperCamelCase)
      _lowercase:       Dict						=				nn.Dropout(config.hidden_dropout_prob)
      _lowercase:       int						=				nn.Linear(config.hidden_size			,				config.num_labels)






     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							Any):
      # Pooler
      _lowercase:       str						=				encoder_outputs[0]
      _lowercase:       Optional[Any]						=				self.pooler(_UpperCamelCase)
      # "return" pooler_output

      # BertModel
      _lowercase:       List[Any]						=				(pooler_input, pooler_output) + encoder_outputs[1:]
      # "return" bmodel_output

      # Dropout and classification
      _lowercase:       int						=				bmodel_output[1]

      _lowercase:       Tuple						=				self.dropout(_UpperCamelCase)
      _lowercase:       Any						=				self.classifier(_UpperCamelCase)

      return logits, pooled_output






@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. """	, lowerCamelCase_	, )
class        A     (		lowerCamelCase_	):







     '''simple docstring'''







     def __init__(				self    :							str			,				_UpperCamelCase    :							Dict):
      super().__init__(_UpperCamelCase)
      _lowercase:       Tuple						=				config.num_labels
      _lowercase:       Optional[int]						=				config.num_hidden_layers

      _lowercase:       int						=				DeeBertModel(_UpperCamelCase)
      _lowercase:       Optional[Any]						=				nn.Dropout(config.hidden_dropout_prob)
      _lowercase:       str						=				nn.Linear(config.hidden_size			,				self.config.num_labels)

      self.init_weights()






     @add_start_docstrings_to_model_forward(_UpperCamelCase)
     def 					UpperCAmelCase__       (				self    :							Any			,				_UpperCamelCase    :							List[Any]=None			,				_UpperCamelCase    :							Dict=None			,				_UpperCamelCase    :							Optional[Any]=None			,				_UpperCamelCase    :							List[str]=None			,				_UpperCamelCase    :							str=None			,				_UpperCamelCase    :							Tuple=None			,				_UpperCamelCase    :							Tuple=None			,				_UpperCamelCase    :							str=-1			,				_UpperCamelCase    :							int=False			,				):
      _lowercase:       List[str]						=				self.num_layers
      try:
       _lowercase:       Dict						=				self.bert(
           _UpperCamelCase			,				attention_mask=_UpperCamelCase			,				token_type_ids=_UpperCamelCase			,				position_ids=_UpperCamelCase			,				head_mask=_UpperCamelCase			,				inputs_embeds=_UpperCamelCase			,				)
       # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

       _lowercase:       Optional[Any]						=				outputs[1]

       _lowercase:       Optional[int]						=				self.dropout(_UpperCamelCase)
       _lowercase:       Optional[int]						=				self.classifier(_UpperCamelCase)
       _lowercase:       Union[str, Any]						=				(logits,) + outputs[2:]  # add hidden states and attention if they are here
      except HighwayException as e:
       _lowercase:       Optional[int]						=				e.message
       _lowercase:       Optional[int]						=				e.exit_layer
       _lowercase:       List[Any]						=				outputs[0]

      if not self.training:
       _lowercase:       int						=				entropy(_UpperCamelCase)
       _lowercase:       List[str]						=				[]
       _lowercase:       int						=				[]
      if labels is not None:
       if self.num_labels == 1:
        #  We are doing regression
        _lowercase:       Optional[Any]						=				MSELoss()
        _lowercase:       Optional[int]						=				loss_fct(logits.view(-1)			,				labels.view(-1))
       else:
        _lowercase:       List[str]						=				CrossEntropyLoss()
        _lowercase:       int						=				loss_fct(logits.view(-1			,				self.num_labels)			,				labels.view(-1))

       # work with highway exits
       _lowercase:       Dict						=				[]
       for highway_exit in outputs[-1]:
        _lowercase:       Any						=				highway_exit[0]
        if not self.training:
         highway_logits_all.append(_UpperCamelCase)
         highway_entropy.append(highway_exit[2])
        if self.num_labels == 1:
         #  We are doing regression
         _lowercase:       Optional[Any]						=				MSELoss()
         _lowercase:       Dict						=				loss_fct(highway_logits.view(-1)			,				labels.view(-1))
        else:
         _lowercase:       Dict						=				CrossEntropyLoss()
         _lowercase:       List[str]						=				loss_fct(highway_logits.view(-1			,				self.num_labels)			,				labels.view(-1))
        highway_losses.append(_UpperCamelCase)

       if train_highway:
        _lowercase:       Any						=				(sum(highway_losses[:-1]),) + outputs
        # exclude the final highway, of course
       else:
        _lowercase:       str						=				(loss,) + outputs
      if not self.training:
       _lowercase:       List[str]						=				outputs + ((original_entropy, highway_entropy), exit_layer)
       if output_layer >= 0:
        _lowercase:       Tuple						=				(
            (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
        )  # use the highway of the last layer

      return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



