
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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Dict       )  ->     Dict:
						_lowercase					       =					torch.exp(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch.sum(SCREAMING_SNAKE_CASE_		,   dim=1       )  # sum of exp(x_i)
						_lowercase					       =					torch.sum(x * exp_x		,   dim=1       )  # sum of x_i * exp(x_i)
						return torch.log(SCREAMING_SNAKE_CASE_       ) - B / A



class    a_ (			nn.Module    ):






			def __init__(      self				,			__UpperCamelCase  ):
									super().__init__()
									_lowercase					       =					config.output_attentions
									_lowercase					       =					config.output_hidden_states
									_lowercase					       =					nn.ModuleList([BertLayer(__UpperCamelCase  ) for _ in range(config.num_hidden_layers  )]  )
									_lowercase					       =					nn.ModuleList([BertHighway(__UpperCamelCase  ) for _ in range(config.num_hidden_layers  )]  )

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									if (type(__UpperCamelCase  ) is float) or (type(__UpperCamelCase  ) is int):
															for i in range(len(self.early_exit_entropy  )  ):
																					_lowercase					       =					x
									else:
															_lowercase					       =					x






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					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				,			__UpperCamelCase				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			):
									_lowercase					       =					()
									_lowercase					       =					()
									_lowercase					       =					()
									for i, layer_module in enumerate(self.layer  ):
															if self.output_hidden_states:
																					_lowercase					       =					all_hidden_states + (hidden_states,)

															_lowercase					       =					layer_module(
															    __UpperCamelCase				,			__UpperCamelCase				,			head_mask[i]				,			__UpperCamelCase				,			__UpperCamelCase  )
															_lowercase					       =					layer_outputs[0]

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

															_lowercase					       =					(hidden_states,)
															if self.output_hidden_states:
																					_lowercase					       =					current_outputs + (all_hidden_states,)
															if self.output_attentions:
																					_lowercase					       =					current_outputs + (all_attentions,)

															_lowercase					       =					self.highway[i](__UpperCamelCase  )
															# logits, pooled_output

															if not self.training:
																					_lowercase					       =					highway_exit[0]
																					_lowercase					       =					entropy(__UpperCamelCase  )
																					_lowercase					       =					highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																					_lowercase					       =					all_highway_exits + (highway_exit,)

																					if highway_entropy < self.early_exit_entropy[i]:
																											_lowercase					       =					(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																											raise HighwayException(__UpperCamelCase				,			i + 1  )
															else:
																					_lowercase					       =					all_highway_exits + (highway_exit,)

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

									_lowercase					       =					(hidden_states,)
									if self.output_hidden_states:
															_lowercase					       =					outputs + (all_hidden_states,)
									if self.output_attentions:
															_lowercase					       =					outputs + (all_attentions,)

									_lowercase					       =					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    a_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase  )
									_lowercase					       =					config

									_lowercase					       =					BertEmbeddings(__UpperCamelCase  )
									_lowercase					       =					DeeBertEncoder(__UpperCamelCase  )
									_lowercase					       =					BertPooler(__UpperCamelCase  )

									self.init_weights()






			def    UpperCamelCase_				(      self  ):
									self.encoder.init_highway_pooler(self.pooler  )






			def    UpperCamelCase_				(      self  ):
									return self.embeddings.word_embeddings






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					value






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									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				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=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					       =					input_ids.size()
									elif inputs_embeds is not None:
															_lowercase					       =					inputs_embeds.size()[:-1]
									else:
															raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

									_lowercase					       =					input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
															_lowercase					       =					torch.ones(__UpperCamelCase				,			device=__UpperCamelCase  )
									if encoder_attention_mask is None:
															_lowercase					       =					torch.ones(__UpperCamelCase				,			device=__UpperCamelCase  )
									if token_type_ids is None:
															_lowercase					       =					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					       =					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					       =					encoder_attention_mask[:, None, :, :]
									if encoder_attention_mask.dim() == 2:
															_lowercase					       =					encoder_attention_mask[:, None, None, :]

									_lowercase					       =					encoder_extended_attention_mask.to(
									    dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
									_lowercase					       =					(1.0 - encoder_extended_attention_mask) * -1_00_00.0

									# 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					       =					self.get_head_mask(__UpperCamelCase				,			self.config.num_hidden_layers  )

									_lowercase					       =					self.embeddings(
									    input_ids=__UpperCamelCase				,			position_ids=__UpperCamelCase				,			token_type_ids=__UpperCamelCase				,			inputs_embeds=__UpperCamelCase  )
									_lowercase					       =					self.encoder(
									    __UpperCamelCase				,			attention_mask=__UpperCamelCase				,			head_mask=__UpperCamelCase				,			encoder_hidden_states=__UpperCamelCase				,			encoder_attention_mask=__UpperCamelCase				,			)
									_lowercase					       =					encoder_outputs[0]
									_lowercase					       =					self.pooler(__UpperCamelCase  )

									_lowercase					       =					(
									    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_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					message
									_lowercase					       =					exit_layer  # start from 1!







class    a_ (			nn.Module    ):






			def __init__(      self				,			__UpperCamelCase  ):
									super().__init__()
									_lowercase					       =					BertPooler(__UpperCamelCase  )
									_lowercase					       =					nn.Dropout(config.hidden_dropout_prob  )
									_lowercase					       =					nn.Linear(config.hidden_size				,			config.num_labels  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# Pooler
									_lowercase					       =					encoder_outputs[0]
									_lowercase					       =					self.pooler(__UpperCamelCase  )
									# "return" pooler_output

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

									# Dropout and classification
									_lowercase					       =					bmodel_output[1]

									_lowercase					       =					self.dropout(__UpperCamelCase  )
									_lowercase					       =					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    a_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase  )
									_lowercase					       =					config.num_labels
									_lowercase					       =					config.num_hidden_layers

									_lowercase					       =					DeeBertModel(__UpperCamelCase  )
									_lowercase					       =					nn.Dropout(config.hidden_dropout_prob  )
									_lowercase					       =					nn.Linear(config.hidden_size				,			self.config.num_labels  )

									self.init_weights()






			@add_start_docstrings_to_model_forward(__UpperCamelCase  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=-1				,			__UpperCamelCase=False				,			):
									_lowercase					       =					self.num_layers
									try:
															_lowercase					       =					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					       =					outputs[1]

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

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

															# work with highway exits
															_lowercase					       =					[]
															for highway_exit in outputs[-1]:
																					_lowercase					       =					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					       =					MSELoss()
																											_lowercase					       =					loss_fct(highway_logits.view(-1  )				,			labels.view(-1  )  )
																					else:
																											_lowercase					       =					CrossEntropyLoss()
																											_lowercase					       =					loss_fct(highway_logits.view(-1				,			self.num_labels  )				,			labels.view(-1  )  )
																					highway_losses.append(__UpperCamelCase  )

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


A		: Dict								=     {
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: Optional[Any]								=     [
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		A		: List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)