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



class       lowerCAmelCase_		(nn.Module    ):


				"""simple docstring"""



				def __init__(self  ,			SCREAMING_SNAKE_CASE__       )   ->						Optional[int]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		:			str	 =  config.output_attentions
											SCREAMING_SNAKE_CASE__		:			Dict	 =  config.output_hidden_states
											SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  nn.ModuleList([BertLayer(_UpperCamelCase       ) for _ in range(config.num_hidden_layers       )]       )
											SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  nn.ModuleList([BertHighway(_UpperCamelCase       ) for _ in range(config.num_hidden_layers       )]       )

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



				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__       )   ->						int:



											"""simple docstring"""




											if (type(_UpperCamelCase       ) is float) or (type(_UpperCamelCase       ) is int):
																		for i in range(len(self.early_exit_entropy       )       ):
																									SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  x
											else:
																		SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  x



				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__       )   ->						Optional[Any]:



											"""simple docstring"""




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



				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			)   ->						List[str]:



											"""simple docstring"""




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

																		SCREAMING_SNAKE_CASE__		:			int	 =  layer_module(
																		    _UpperCamelCase  ,			_UpperCamelCase  ,			head_mask[i]  ,			_UpperCamelCase  ,			_UpperCamelCase       )
																		SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  layer_outputs[0]

																		if self.output_attentions:
																									SCREAMING_SNAKE_CASE__		:			str	 =  all_attentions + (layer_outputs[1],)

																		SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  (hidden_states,)
																		if self.output_hidden_states:
																									SCREAMING_SNAKE_CASE__		:			Tuple	 =  current_outputs + (all_hidden_states,)
																		if self.output_attentions:
																									SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  current_outputs + (all_attentions,)

																		SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  self.highway[i](_UpperCamelCase       )
																		# logits, pooled_output

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

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

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

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

											SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  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). '''					,   __a					,   )
class       lowerCAmelCase_		(__a    ):


				"""simple docstring"""



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



											"""simple docstring"""




											super().__init__(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			Tuple	 =  config

											SCREAMING_SNAKE_CASE__		:			List[str]	 =  BertEmbeddings(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			List[Any]	 =  DeeBertEncoder(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			List[str]	 =  BertPooler(_UpperCamelCase       )

											self.init_weights()



				def       __magic_name__       (self       )   ->						Optional[Any]:



											"""simple docstring"""




											self.encoder.init_highway_pooler(self.pooler       )



				def       __magic_name__       (self       )   ->						Dict:



											"""simple docstring"""




											return self.embeddings.word_embeddings



				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__       )   ->						Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			List[Any]	 =  value



				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__       )   ->						List[str]:



											"""simple docstring"""




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



				@add_start_docstrings_to_model_forward(_UpperCamelCase       )
				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			)   ->						Tuple:



											"""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:
																		SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  input_ids.size()
											elif inputs_embeds is not None:
																		SCREAMING_SNAKE_CASE__		:			List[Any]	 =  inputs_embeds.size()[:-1]
											else:
																		raise ValueError("""You have to specify either input_ids or inputs_embeds"""       )

											SCREAMING_SNAKE_CASE__		:			Tuple	 =  input_ids.device if input_ids is not None else inputs_embeds.device

											if attention_mask is None:
																		SCREAMING_SNAKE_CASE__		:			List[str]	 =  torch.ones(_UpperCamelCase  ,			device=_UpperCamelCase       )
											if encoder_attention_mask is None:
																		SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  torch.ones(_UpperCamelCase  ,			device=_UpperCamelCase       )
											if token_type_ids is None:
																		SCREAMING_SNAKE_CASE__		:			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.
											SCREAMING_SNAKE_CASE__		:			str	 =  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:
																		SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  encoder_attention_mask[:, None, :, :]
											if encoder_attention_mask.dim() == 2:
																		SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  encoder_attention_mask[:, None, None, :]

											SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  encoder_extended_attention_mask.to(
											    dtype=next(self.parameters()       ).dtype       )  # fp16 compatibility
											SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  (1.0 - encoder_extended_attention_mask) * -10000.0

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

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

											SCREAMING_SNAKE_CASE__		:			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       lowerCAmelCase_		(__a    ):


				"""simple docstring"""



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



											"""simple docstring"""




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






class       lowerCAmelCase_		(nn.Module    ):


				"""simple docstring"""



				def __init__(self  ,			SCREAMING_SNAKE_CASE__       )   ->						str:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  BertPooler(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			Any	 =  nn.Dropout(config.hidden_dropout_prob       )
											SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  nn.Linear(config.hidden_size  ,			config.num_labels       )



				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__       )   ->						Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			int	 =  encoder_outputs[0]
											SCREAMING_SNAKE_CASE__		:			Any	 =  self.pooler(_UpperCamelCase       )
											# "return" pooler_output

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

											# Dropout and classification
											SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  bmodel_output[1]

											SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  self.dropout(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			Tuple	 =  self.classifier(_UpperCamelCase       )

											return logits, pooled_output






@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''					,   __a					,   )
class       lowerCAmelCase_		(__a    ):


				"""simple docstring"""



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



											"""simple docstring"""




											super().__init__(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			Tuple	 =  config.num_labels
											SCREAMING_SNAKE_CASE__		:			Any	 =  config.num_hidden_layers

											SCREAMING_SNAKE_CASE__		:			Dict	 =  DeeBertModel(_UpperCamelCase       )
											SCREAMING_SNAKE_CASE__		:			Any	 =  nn.Dropout(config.hidden_dropout_prob       )
											SCREAMING_SNAKE_CASE__		:			Dict	 =  nn.Linear(config.hidden_size  ,			self.config.num_labels       )

											self.init_weights()



				@add_start_docstrings_to_model_forward(_UpperCamelCase       )
				def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=None  ,			SCREAMING_SNAKE_CASE__=-1  ,			SCREAMING_SNAKE_CASE__=False  ,			)   ->						Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		:			Dict	 =  self.num_layers
											try:
																		SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  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

																		SCREAMING_SNAKE_CASE__		:			List[Any]	 =  outputs[1]

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

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

																		# work with highway exits
																		SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  []
																		for highway_exit in outputs[-1]:
																									SCREAMING_SNAKE_CASE__		:			str	 =  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
																																SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  MSELoss()
																																SCREAMING_SNAKE_CASE__		:			Tuple	 =  loss_fct(highway_logits.view(-1       )  ,			labels.view(-1       )       )
																									else:
																																SCREAMING_SNAKE_CASE__		:			Tuple	 =  CrossEntropyLoss()
																																SCREAMING_SNAKE_CASE__		:			Dict	 =  loss_fct(highway_logits.view(-1  ,			self.num_labels       )  ,			labels.view(-1       )       )
																									highway_losses.append(_UpperCamelCase       )

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






from math import ceil






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(0       ,				__lowerCAmelCase							)							)

			lowerCAmelCase_         =      [item for sublist in list(device_map.values()							) for item in sublist]

			# Duplicate check
			lowerCAmelCase_         =      []
			for i in device_map_blocks:
						if device_map_blocks.count(__lowerCAmelCase							) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(__lowerCAmelCase							)
    # Missing blocks
			lowerCAmelCase_         =      [i for i in blocks if i not in device_map_blocks]
			lowerCAmelCase_         =      [i for i in device_map_blocks if i not in blocks]

			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
						    " These attention blocks were specified more than once: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
						    "blocks to a device on the device_map: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(__lowerCAmelCase							)							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(__lowerCAmelCase							)							)
			lowerCAmelCase_         =      int(ceil(n_layers / len(__lowerCAmelCase							)							)							)
			lowerCAmelCase_         =      [layers[i : i + n_blocks] for i in range(0       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)]

			return dict(zip(__lowerCAmelCase       ,				__lowerCAmelCase							)							)



