





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 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			str											=	torch.exp(__a   )
	snake_case_		:			Dict											=	torch.sum(__a   , dim=1   )  # sum of exp(x_i)
	snake_case_		:			Union[str, Any]											=	torch.sum(x * exp_x   , dim=1   )  # sum of x_i * exp(x_i)
	return torch.log(__a   ) - B / A
class SCREAMING_SNAKE_CASE_						(					nn.Module					):






						def __init__(      self      :      Union[str, Any]		,   _A      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


							super().__init__()
							snake_case_		:			str											=	config.output_attentions
							snake_case_		:			str											=	config.output_hidden_states
							snake_case_		:			List[Any]											=	nn.ModuleList([BertLayer(_A	) for _ in range(config.num_hidden_layers	)]	)
							snake_case_		:			Any											=	nn.ModuleList([BertHighway(_A	) for _ in range(config.num_hidden_layers	)]	)

							snake_case_		:			Tuple											=	[-1 for _ in range(config.num_hidden_layers	)]






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      Optional[int]	)							->    Any:







							"""simple docstring"""


							if (type(_A	) is float) or (type(_A	) is int):
								for i in range(len(self.early_exit_entropy	)	):
									snake_case_		:			int											=	x
							else:
								snake_case_		:			List[Any]											=	x






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[str]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[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      :      Dict		,   _A      :      Union[str, Any]		,   _A      :      Tuple=None		,   _A      :      str=None		,   _A      :      Tuple=None		,   _A      :      Any=None		,   )							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	()
							snake_case_		:			List[Any]											=	()
							snake_case_		:			List[Any]											=	()
							for i, layer_module in enumerate(self.layer	):
								if self.output_hidden_states:
									snake_case_		:			List[Any]											=	all_hidden_states + (hidden_states,)

								snake_case_		:			int											=	layer_module(
								    _A		,   _A		,   head_mask[i]		,   _A		,   _A	)
								snake_case_		:			str											=	layer_outputs[0]

								if self.output_attentions:
									snake_case_		:			Tuple											=	all_attentions + (layer_outputs[1],)

								snake_case_		:			Tuple											=	(hidden_states,)
								if self.output_hidden_states:
									snake_case_		:			Any											=	current_outputs + (all_hidden_states,)
								if self.output_attentions:
									snake_case_		:			Dict											=	current_outputs + (all_attentions,)

								snake_case_		:			Optional[int]											=	self.highway[i](_A	)
								# logits, pooled_output

								if not self.training:
									snake_case_		:			Any											=	highway_exit[0]
									snake_case_		:			Tuple											=	entropy(_A	)
									snake_case_		:			Dict											=	highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
									snake_case_		:			int											=	all_highway_exits + (highway_exit,)

									if highway_entropy < self.early_exit_entropy[i]:
										snake_case_		:			int											=	(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
										raise HighwayException(_A		,   i + 1	)
								else:
									snake_case_		:			Dict											=	all_highway_exits + (highway_exit,)

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

							snake_case_		:			Dict											=	(hidden_states,)
							if self.output_hidden_states:
								snake_case_		:			Any											=	outputs + (all_hidden_states,)
							if self.output_attentions:
								snake_case_		:			int											=	outputs + (all_attentions,)

							snake_case_		:			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). "       ,				snake_case_       ,				)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      int		,   _A      :      str	)							->    str:







							"""simple docstring"""


							super().__init__(_A	)
							snake_case_		:			Dict											=	config

							snake_case_		:			Optional[int]											=	BertEmbeddings(_A	)
							snake_case_		:			List[str]											=	DeeBertEncoder(_A	)
							snake_case_		:			Union[str, Any]											=	BertPooler(_A	)

							self.init_weights()






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							self.encoder.init_highway_pooler(self.pooler	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							return self.embeddings.word_embeddings






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any	)							->    str:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	value






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str	)							->    int:







							"""simple docstring"""


							for layer, heads in heads_to_prune.items():
								self.encoder.layer[layer].attention.prune_heads(_A	)






						@add_start_docstrings_to_model_forward(_A	)
						def UpperCAmelCase_				(      self      :      Any		,   _A      :      List[str]=None		,   _A      :      Any=None		,   _A      :      Union[str, Any]=None		,   _A      :      Union[str, Any]=None		,   _A      :      Optional[Any]=None		,   _A      :      str=None		,   _A      :      Union[str, Any]=None		,   _A      :      Any=None		,   )							->    str:







							"""simple docstring"""


							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:
								snake_case_		:			Tuple											=	input_ids.size()
							elif inputs_embeds is not None:
								snake_case_		:			Union[str, Any]											=	inputs_embeds.size()[:-1]
							else:
								raise ValueError('You have to specify either input_ids or inputs_embeds'	)

							snake_case_		:			Any											=	input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
								snake_case_		:			str											=	torch.ones(_A		,   device=_A	)
							if encoder_attention_mask is None:
								snake_case_		:			Tuple											=	torch.ones(_A		,   device=_A	)
							if token_type_ids is None:
								snake_case_		:			Any											=	torch.zeros(_A		,   dtype=torch.long		,   device=_A	)

							# 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.
							snake_case_		:			torch.Tensor											=	self.get_extended_attention_mask(_A		,   _A		,   _A	)

							# 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:
								snake_case_		:			Any											=	encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
								snake_case_		:			Any											=	encoder_attention_mask[:, None, None, :]

							snake_case_		:			Any											=	encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()	).dtype	)  # fp16 compatibility
							snake_case_		:			List[str]											=	(1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

							# 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]
							snake_case_		:			str											=	self.get_head_mask(_A		,   self.config.num_hidden_layers	)

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

							snake_case_		:			int											=	(
							    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 SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Any		,   _A      :      Dict		,   _A      :      Any	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	message
							snake_case_		:			Dict											=	exit_layer  # start from 1!
class SCREAMING_SNAKE_CASE_						(					nn.Module					):






						def __init__(      self      :      Tuple		,   _A      :      List[Any]	)							->    Optional[Any]:







							"""simple docstring"""


							super().__init__()
							snake_case_		:			Optional[int]											=	BertPooler(_A	)
							snake_case_		:			str											=	nn.Dropout(config.hidden_dropout_prob	)
							snake_case_		:			Dict											=	nn.Linear(config.hidden_size		,   config.num_labels	)






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[str]											=	encoder_outputs[0]
							snake_case_		:			Optional[int]											=	self.pooler(_A	)
							# "return" pooler_output

							# BertModel
							snake_case_		:			Optional[int]											=	(pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							snake_case_		:			Optional[int]											=	bmodel_output[1]

							snake_case_		:			int											=	self.dropout(_A	)
							snake_case_		:			int											=	self.classifier(_A	)

							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. "       ,				snake_case_       ,				)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Optional[int]		,   _A      :      str	)							->    List[str]:







							"""simple docstring"""


							super().__init__(_A	)
							snake_case_		:			Dict											=	config.num_labels
							snake_case_		:			Optional[Any]											=	config.num_hidden_layers

							snake_case_		:			List[str]											=	DeeBertModel(_A	)
							snake_case_		:			Union[str, Any]											=	nn.Dropout(config.hidden_dropout_prob	)
							snake_case_		:			Tuple											=	nn.Linear(config.hidden_size		,   self.config.num_labels	)

							self.init_weights()






						@add_start_docstrings_to_model_forward(_A	)
						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any=None		,   _A      :      Union[str, Any]=None		,   _A      :      Any=None		,   _A      :      Optional[Any]=None		,   _A      :      Union[str, Any]=None		,   _A      :      int=None		,   _A      :      Optional[Any]=None		,   _A      :      Dict=-1		,   _A      :      int=False		,   )							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	self.num_layers
							try:
								snake_case_		:			Union[str, Any]											=	self.bert(
								    _A		,   attention_mask=_A		,   token_type_ids=_A		,   position_ids=_A		,   head_mask=_A		,   inputs_embeds=_A		,   )
								# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

								snake_case_		:			Any											=	outputs[1]

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

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

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

								if train_highway:
									snake_case_		:			int											=	(sum(highway_losses[:-1]	),) + outputs
									# exclude the final highway, of course
								else:
									snake_case_		:			Tuple											=	(loss,) + outputs
							if not self.training:
								snake_case_		:			Optional[int]											=	outputs + ((original_entropy, highway_entropy), exit_layer)
								if output_layer >= 0:
									snake_case_		:			Dict											=	(
									    (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)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE      	=		{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		["""LxmertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

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

