
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(lowerCAmelCase__		)
					lowercase						     =						torch.sum(lowerCAmelCase__			,      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(lowerCAmelCase__		) - 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(A__) for _ in range(config.num_hidden_layers)])
												lowercase						     =						nn.ModuleList([BertHighway(A__) for _ in range(config.num_hidden_layers)])

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




							def 			A__				(    self       ,A__):
												if (type(A__) is float) or (type(A__) 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(
																	    A__       ,A__       ,head_mask[i]       ,A__       ,A__)
																	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](A__)
																	# logits, pooled_output

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




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

												lowercase						     =						BertEmbeddings(A__)
												lowercase						     =						DeeBertEncoder(A__)
												lowercase						     =						BertPooler(A__)

												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(A__)





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

												# 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(A__       ,A__       ,A__)

												# 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) * -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]
												lowercase						     =						self.get_head_mask(A__       ,self.config.num_hidden_layers)

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

												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		( SCREAMING_SNAKE_CASE__		):




							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(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,config.num_labels)





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

												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. '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




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

												lowercase						     =						DeeBertModel(A__)
												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(A__)
							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(
																	    A__       ,attention_mask=A__       ,token_type_ids=A__       ,position_ids=A__       ,head_mask=A__       ,inputs_embeds=A__       ,)
																	# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																	lowercase						     =						outputs[1]

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

																	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)






import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	int	 =ComputeEnvironment.AMAZON_SAGEMAKER
							lowercase_	:	Optional[int]	 =True
							lowercase_	:	Any	 ='''ml.p3.2xlarge'''
							lowercase_	:	Any	 ='''accelerate_sagemaker_execution_role'''
							lowercase_	:	Union[str, Any]	 ='''hf-sm'''
							lowercase_	:	Any	 ='''us-east-1'''
							lowercase_	:	List[str]	 =1
							lowercase_	:	Any	 ='''accelerate-sagemaker-1'''
							lowercase_	:	Union[str, Any]	 ='''1.6'''
							lowercase_	:	Any	 ='''4.4'''
							lowercase_	:	Any	 ='''train.py'''
							lowercase_	:	int	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''False''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]
							lowercase_	:	List[Any]	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''--do_test''',
							    '''False''',
							    '''--do_predict''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]







class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												lowercase						     =						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
												assert isinstance(converted_args['''model_name_or_path''']       ,A__)
												assert isinstance(converted_args['''do_train''']       ,A__)
												assert isinstance(converted_args['''epochs''']       ,A__)
												assert isinstance(converted_args['''learning_rate''']       ,A__)
												assert isinstance(converted_args['''max_steps''']       ,A__)

												with pytest.raises(A__):
																	_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args)




