
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__		):
					'''simple docstring'''
					lowercase							=						torch.exp(__UpperCamelCase		)
					lowercase							=						torch.sum(__UpperCamelCase ,			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(__UpperCamelCase		) - B / A







class 				lowercase		(					nn.Module		):




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

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




							def  A__      (				self   ,A__):
												if (type(_lowerCamelCase) is float) or (type(_lowerCamelCase) is int):
																	for i in range(len(self.early_exit_entropy)):
																						lowercase							=						x
												else:
																	lowercase							=						x




							def  A__      (				self   ,A__):
												lowercase							=						pooler.state_dict()
												for highway in self.highway:
																	for name, param in highway.pooler.state_dict().items():
																						param.copy_(loaded_model[name])





							def  A__      (				self   ,A__   ,A__=None   ,A__=None   ,A__=None   ,A__=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(
																	    _lowerCamelCase   ,_lowerCamelCase   ,head_mask[i]   ,_lowerCamelCase   ,_lowerCamelCase)
																	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](_lowerCamelCase)
																	# logits, pooled_output

																	if not self.training:
																						lowercase							=						highway_exit[0]
																						lowercase							=						entropy(_lowerCamelCase)
																						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(_lowerCamelCase   ,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). '''       ,  _lowerCAmelCase       ,  )
class 				lowercase		(					_lowerCAmelCase		):




							def __init__(				self   ,A__):
												super().__init__(_lowerCamelCase)
												lowercase							=						config

												lowercase							=						BertEmbeddings(_lowerCamelCase)
												lowercase							=						DeeBertEncoder(_lowerCamelCase)
												lowercase							=						BertPooler(_lowerCamelCase)

												self.init_weights()




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




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




							def  A__      (				self   ,A__):
												lowercase							=						value




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





							@add_start_docstrings_to_model_forward(_lowerCamelCase)
							def  A__      (				self   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=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(_lowerCamelCase   ,device=_lowerCamelCase)
												if encoder_attention_mask is None:
																	lowercase							=						torch.ones(_lowerCamelCase   ,device=_lowerCamelCase)
												if token_type_ids is None:
																	lowercase							=						torch.zeros(_lowerCamelCase   ,dtype=torch.long   ,device=_lowerCamelCase)

												# 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(_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase)

												# 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_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]
												lowercase							=						self.get_head_mask(_lowerCamelCase   ,self.config.num_hidden_layers)

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

												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 				lowercase		(					_lowerCAmelCase		):




							def __init__(				self   ,A__   ,A__):
												lowercase							=						message
												lowercase							=						exit_layer  # start from 1!







class 				lowercase		(					nn.Module		):




							def __init__(				self   ,A__):
												super().__init__()
												lowercase							=						BertPooler(_lowerCamelCase)
												lowercase							=						nn.Dropout(config.hidden_dropout_prob)
												lowercase							=						nn.Linear(config.hidden_size   ,config.num_labels)





							def  A__      (				self   ,A__):
												lowercase							=						encoder_outputs[0]
												lowercase							=						self.pooler(_lowerCamelCase)
												# "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(_lowerCamelCase)
												lowercase							=						self.classifier(_lowerCamelCase)

												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 				lowercase		(					_lowerCAmelCase		):




							def __init__(				self   ,A__):
												super().__init__(_lowerCamelCase)
												lowercase							=						config.num_labels
												lowercase							=						config.num_hidden_layers

												lowercase							=						DeeBertModel(_lowerCamelCase)
												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(_lowerCamelCase)
							def  A__      (				self   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=None   ,A__=-1   ,A__=False   ,):
												lowercase							=						self.num_layers
												try:
																	lowercase							=						self.bert(
																	    _lowerCamelCase   ,attention_mask=_lowerCamelCase   ,token_type_ids=_lowerCamelCase   ,position_ids=_lowerCamelCase   ,head_mask=_lowerCamelCase   ,inputs_embeds=_lowerCamelCase   ,)
																	# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																	lowercase							=						outputs[1]

																	lowercase							=						self.dropout(_lowerCamelCase)
																	lowercase							=						self.classifier(_lowerCamelCase)
																	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(_lowerCamelCase)
																	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(_lowerCamelCase)
																											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(_lowerCamelCase)

																	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)












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[int],       *_lowerCamelCase :     Union[str, Any],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     warnings.warn(
         '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use SegformerImageProcessor instead.''',       _lowerCamelCase,       )
     super().__init__(*_lowerCamelCase,       **_lowerCamelCase  )




