




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						(							lowerCamelCase_ :     str  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      torch.exp(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      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							):






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

							SCREAMING_SNAKE_CASE_   : Dict					  =      [-1 for _ in range(config.num_hidden_layers )]






				def 		snake_case	(			self      ,snake_case__ ):
							if (type(snake_case__ ) is float) or (type(snake_case__ ) is int):
										for i in range(len(self.early_exit_entropy ) ):
													SCREAMING_SNAKE_CASE_   : str					  =      x
							else:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      x






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      pooler.state_dict()
							for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name] )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ()
							for i, layer_module in enumerate(self.layer ):
										if self.output_hidden_states:
													SCREAMING_SNAKE_CASE_   : Any					  =      all_hidden_states + (hidden_states,)

										SCREAMING_SNAKE_CASE_   : int					  =      layer_module(
										    snake_case__      ,snake_case__      ,head_mask[i]      ,snake_case__      ,snake_case__ )
										SCREAMING_SNAKE_CASE_   : Any					  =      layer_outputs[0]

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

										SCREAMING_SNAKE_CASE_   : Tuple					  =      (hidden_states,)
										if self.output_hidden_states:
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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](snake_case__ )
										# logits, pooled_output

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

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

        # Add last layer
							if self.output_hidden_states:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      all_hidden_states + (hidden_states,)

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

							SCREAMING_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). "						,     lowerCamelCase_						,     )
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__ ):
							super().__init__(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config

							SCREAMING_SNAKE_CASE_   : int					  =      BertEmbeddings(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      DeeBertEncoder(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      BertPooler(snake_case__ )

							self.init_weights()






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






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






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      value






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






				@add_start_docstrings_to_model_forward(snake_case__ )
				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=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:
										SCREAMING_SNAKE_CASE_   : Dict					  =      input_ids.size()
							elif inputs_embeds is not None:
										SCREAMING_SNAKE_CASE_   : str					  =      inputs_embeds.size()[:-1]
							else:
										raise ValueError('You have to specify either input_ids or inputs_embeds' )

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

							if attention_mask is None:
										SCREAMING_SNAKE_CASE_   : int					  =      torch.ones(snake_case__      ,device=snake_case__ )
							if encoder_attention_mask is None:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.ones(snake_case__      ,device=snake_case__ )
							if token_type_ids is None:
										SCREAMING_SNAKE_CASE_   : int					  =      torch.zeros(snake_case__      ,dtype=torch.long      ,device=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.
							SCREAMING_SNAKE_CASE_   : torch.Tensor					  =      self.get_extended_attention_mask(snake_case__      ,snake_case__      ,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:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										SCREAMING_SNAKE_CASE_   : str					  =      encoder_attention_mask[:, None, None, :]

							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_extended_attention_mask.to(
							    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
							SCREAMING_SNAKE_CASE_   : 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_   : List[Any]					  =      self.get_head_mask(snake_case__      ,self.config.num_hidden_layers )

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

							SCREAMING_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 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      message
							SCREAMING_SNAKE_CASE_   : int					  =      exit_layer  # start from 1!







class 				lowerCAmelCase_       (					nn.Module							):






				def __init__(			self      ,snake_case__ ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      BertPooler(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      nn.Dropout(config.hidden_dropout_prob )
							SCREAMING_SNAKE_CASE_   : Any					  =      nn.Linear(config.hidden_size      ,config.num_labels )






				def 		snake_case	(			self      ,snake_case__ ):
							# Pooler
							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_outputs[0]
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.pooler(snake_case__ )
							# "return" pooler_output

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

							# Dropout and classification
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      bmodel_output[1]

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.dropout(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.classifier(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. "						,     lowerCamelCase_						,     )
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__ ):
							super().__init__(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      config.num_labels
							SCREAMING_SNAKE_CASE_   : int					  =      config.num_hidden_layers

							SCREAMING_SNAKE_CASE_   : int					  =      DeeBertModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : str					  =      nn.Dropout(config.hidden_dropout_prob )
							SCREAMING_SNAKE_CASE_   : str					  =      nn.Linear(config.hidden_size      ,self.config.num_labels )

							self.init_weights()






				@add_start_docstrings_to_model_forward(snake_case__ )
				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=-1      ,snake_case__=False      ,):
							SCREAMING_SNAKE_CASE_   : str					  =      self.num_layers
							try:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.bert(
										    snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,position_ids=snake_case__      ,head_mask=snake_case__      ,inputs_embeds=snake_case__      ,)
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										SCREAMING_SNAKE_CASE_   : str					  =      outputs[1]

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

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

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

										if train_highway:
													SCREAMING_SNAKE_CASE_   : str					  =      (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_   : Union[str, Any]					  =      outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													SCREAMING_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)







'''simple docstring'''
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_attention_mask:
						lowercase						:		str   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		DistilBertConfig(
					    vocab_size=self.vocab_size							,      dim=self.hidden_size							,      n_layers=self.num_hidden_layers							,      n_heads=self.num_attention_heads							,      hidden_dim=self.intermediate_size							,      hidden_act=self.hidden_act							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      tie_weights_=a_							,      )

					return config, input_ids, attention_mask


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,      a_							,      atol=1e-4							)							)


