
"""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 							A		(       snake_case   :Optional[int]	)   ->			str:
	__UpperCamelCase       						= torch.exp(snake_case	)
	__UpperCamelCase       						= torch.sum(snake_case       ,      dim=1	)  # sum of exp(x_i)
	__UpperCamelCase       						= torch.sum(x * exp_x       ,      dim=1	)  # sum of x_i * exp(x_i)
	return torch.log(snake_case	) - B / A

class __lowerCAmelCase	(   nn.Module    ):





					def __init__(			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						super().__init__()
						__UpperCamelCase       						= config.output_attentions
						__UpperCamelCase       						= config.output_hidden_states
						__UpperCamelCase       						= nn.ModuleList([BertLayer(__UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )
						__UpperCamelCase       						= nn.ModuleList([BertHighway(__UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )

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





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




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





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= 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			,  ):

						'''simple docstring'''




						__UpperCamelCase       						= ()
						__UpperCamelCase       						= ()
						__UpperCamelCase       						= ()
						for i, layer_module in enumerate(self.layer   ):
							if self.output_hidden_states:
								__UpperCamelCase       						= all_hidden_states + (hidden_states,)

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

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

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

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

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

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

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

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

						__UpperCamelCase       						= 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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





					def __init__(			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						super().__init__(__UpperCAmelCase   )
						__UpperCamelCase       						= config

						__UpperCamelCase       						= BertEmbeddings(__UpperCAmelCase   )
						__UpperCamelCase       						= DeeBertEncoder(__UpperCAmelCase   )
						__UpperCamelCase       						= BertPooler(__UpperCAmelCase   )

						self.init_weights()





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						self.encoder.init_highway_pooler(self.pooler   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						return self.embeddings.word_embeddings





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= value





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''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 		UpperCAmelCase      (			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

						'''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:
							__UpperCamelCase       						= input_ids.size()
						elif inputs_embeds is not None:
							__UpperCamelCase       						= inputs_embeds.size()[:-1]
						else:
							raise ValueError('You have to specify either input_ids or inputs_embeds'   )

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

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

						__UpperCamelCase       						= encoder_extended_attention_mask.to(
						    dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
						__UpperCamelCase       						= (1.0 - encoder_extended_attention_mask) * -1_0000.0

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

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

						__UpperCamelCase       						= (
						    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	(   __SCREAMING_SNAKE_CASE    ):





					def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= message
						__UpperCamelCase       						= exit_layer  # start from 1!


class __lowerCAmelCase	(   nn.Module    ):





					def __init__(			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						super().__init__()
						__UpperCamelCase       						= BertPooler(__UpperCAmelCase   )
						__UpperCamelCase       						= nn.Dropout(config.hidden_dropout_prob   )
						__UpperCamelCase       						= nn.Linear(config.hidden_size			,  config.num_labels   )







					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= encoder_outputs[0]
						__UpperCamelCase       						= self.pooler(__UpperCAmelCase   )
						# "return" pooler_output

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

						# Dropout and classification
						__UpperCamelCase       						= bmodel_output[1]

						__UpperCamelCase       						= self.dropout(__UpperCAmelCase   )
						__UpperCamelCase       						= self.classifier(__UpperCAmelCase   )

						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. "    ,						__SCREAMING_SNAKE_CASE    ,						)
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





					def __init__(			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						super().__init__(__UpperCAmelCase   )
						__UpperCamelCase       						= config.num_labels
						__UpperCamelCase       						= config.num_hidden_layers

						__UpperCamelCase       						= DeeBertModel(__UpperCAmelCase   )
						__UpperCamelCase       						= nn.Dropout(config.hidden_dropout_prob   )
						__UpperCamelCase       						= 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			,  ):

						'''simple docstring'''




						__UpperCamelCase       						= self.num_layers
						try:
							__UpperCamelCase       						= 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

							__UpperCamelCase       						= outputs[1]

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

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

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

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

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase			:       Union[str, Any]									=				"https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase			:       Union[str, Any]									=				BASE_URL + "/user"

# https://github.com/settings/tokens
UpperCamelCase			:       Optional[int]									=				os.environ.get("USER_TOKEN", "")






def 							A		(       snake_case   :str	)   ->			dict[Any, Any]:
 __UpperCamelCase       						= {
     'Authorization': f'token {auth_token}',
     'Accept': 'application/vnd.github.v3+json',
 }
 return requests.get(snake_case       ,      headers=snake_case	).json()


if __name__ == "__main__":  # pragma: no cover
      if USER_TOKEN:
            for key, value in fetch_github_info(USER_TOKEN).items():
                  print(f'''{key}: {value}''')
      else:
            raise ValueError("'USER_TOKEN' field cannot be empty.")



