



"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case				:  Tuple =       {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	__snake_case				:  Optional[int] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






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      _lowercase							(    __snake_case      )		->  Dict:
			__lowerCAmelCase :    Optional[int]				=			torch.exp(__snake_case      )
			__lowerCAmelCase :    int				=			torch.sum(__snake_case   ,dim=1      )  # sum of exp(x_i)
			__lowerCAmelCase :    Optional[Any]				=			torch.sum(x * exp_x   ,dim=1      )  # sum of x_i * exp(x_i)
			return torch.log(__snake_case      ) - B / A



class      A__  (    nn.Module				):




	'''simple docstring'''





	def __init__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			List[str]:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase :    Any				=			config.output_attentions
				__lowerCAmelCase :    Optional[Any]				=			config.output_hidden_states
				__lowerCAmelCase :    Tuple				=			nn.ModuleList([BertLayer(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])
				__lowerCAmelCase :    int				=			nn.ModuleList([BertHighway(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])

				__lowerCAmelCase :    List[str]				=			[-1 for _ in range(config.num_hidden_layers)]

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

				"""simple docstring"""

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

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

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			pooler.state_dict()
				for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
										param.copy_(loaded_model[name])







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     )							->			Optional[Any]:

				"""simple docstring"""

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

							__lowerCAmelCase :    Any				=			layer_module(
							    _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     head_mask[i]			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    str				=			layer_outputs[0]

							if self.output_attentions:
										__lowerCAmelCase :    List[Any]				=			all_attentions + (layer_outputs[1],)

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

							__lowerCAmelCase :    List[str]				=			self.highway[i](_SCREAMING_SNAKE_CASE)
							# logits, pooled_output

							if not self.training:
										__lowerCAmelCase :    Union[str, Any]				=			highway_exit[0]
										__lowerCAmelCase :    Dict				=			entropy(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase :    Dict				=			highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
										__lowerCAmelCase :    Dict				=			all_highway_exits + (highway_exit,)

										if highway_entropy < self.early_exit_entropy[i]:
													__lowerCAmelCase :    Any				=			(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
													raise HighwayException(_SCREAMING_SNAKE_CASE			,     i + 1)
							else:
										__lowerCAmelCase :    Optional[Any]				=			all_highway_exits + (highway_exit,)

        # Add last layer
				if self.output_hidden_states:
							__lowerCAmelCase :    Union[str, Any]				=			all_hidden_states + (hidden_states,)

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

				__lowerCAmelCase :    List[str]				=			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). '    ,						__SCREAMING_SNAKE_CASE    ,						)
class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Tuple				=			config

				__lowerCAmelCase :    str				=			BertEmbeddings(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			DeeBertEncoder(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			BertPooler(_SCREAMING_SNAKE_CASE)

				self.init_weights()

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Any:

				"""simple docstring"""

				self.encoder.init_highway_pooler(self.pooler)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			str:

				"""simple docstring"""

				return self.embeddings.word_embeddings

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

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			value

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

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







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=None			,     _SCREAMING_SNAKE_CASE:						int=None			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     )							->			Dict:

				"""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:
							__lowerCAmelCase :    Tuple				=			input_ids.size()
				elif inputs_embeds is not None:
							__lowerCAmelCase :    List[str]				=			inputs_embeds.size()[:-1]
				else:
							raise ValueError("You have to specify either input_ids or inputs_embeds")

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

				if attention_mask is None:
							__lowerCAmelCase :    Union[str, Any]				=			torch.ones(_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE)
				if encoder_attention_mask is None:
							__lowerCAmelCase :    Tuple				=			torch.ones(_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE)
				if token_type_ids is None:
							__lowerCAmelCase :    Union[str, Any]				=			torch.zeros(_SCREAMING_SNAKE_CASE			,     dtype=torch.long			,     device=_SCREAMING_SNAKE_CASE)

				# 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.
				__lowerCAmelCase :    torch.Tensor				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				# 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:
							__lowerCAmelCase :    Union[str, Any]				=			encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
							__lowerCAmelCase :    Optional[Any]				=			encoder_attention_mask[:, None, None, :]

				__lowerCAmelCase :    List[str]				=			encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()).dtype)  # fp16 compatibility
				__lowerCAmelCase :    Union[str, Any]				=			(1.0 - encoder_extended_attention_mask) * -1_0000.0

				# 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]
				__lowerCAmelCase :    Union[str, Any]				=			self.get_head_mask(_SCREAMING_SNAKE_CASE			,     self.config.num_hidden_layers)

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

				__lowerCAmelCase :    Dict				=			(
				    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__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			message
				__lowerCAmelCase :    Union[str, Any]				=			exit_layer  # start from 1!






class      A__  (    nn.Module				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Dict)							->			str:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase :    Optional[int]				=			BertPooler(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			nn.Dropout(config.hidden_dropout_prob)
				__lowerCAmelCase :    Optional[Any]				=			nn.Linear(config.hidden_size			,     config.num_labels)







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			encoder_outputs[0]
				__lowerCAmelCase :    Union[str, Any]				=			self.pooler(_SCREAMING_SNAKE_CASE)
				# "return" pooler_output

				# BertModel
				__lowerCAmelCase :    str				=			(pooler_input, pooler_output) + encoder_outputs[1:]
				# "return" bmodel_output

				# Dropout and classification
				__lowerCAmelCase :    Tuple				=			bmodel_output[1]

				__lowerCAmelCase :    int				=			self.dropout(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			self.classifier(_SCREAMING_SNAKE_CASE)

				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. '    ,						__SCREAMING_SNAKE_CASE    ,						)
class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any])							->			Dict:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			config.num_labels
				__lowerCAmelCase :    Tuple				=			config.num_hidden_layers

				__lowerCAmelCase :    Optional[Any]				=			DeeBertModel(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			nn.Dropout(config.hidden_dropout_prob)
				__lowerCAmelCase :    Dict				=			nn.Linear(config.hidden_size			,     self.config.num_labels)

				self.init_weights()







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

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.num_layers
				try:
							__lowerCAmelCase :    Optional[int]				=			self.bert(
							    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     position_ids=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     inputs_embeds=_SCREAMING_SNAKE_CASE			,     )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							__lowerCAmelCase :    Tuple				=			outputs[1]

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

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

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

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