






"""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							):







      """simple docstring"""






      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     ,A ):
          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     ,A ):
          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     ,A ):
          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     ,A     ,A=None     ,A=None     ,A=None     ,A=None     ,):
          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). '''   ,					__lowercase   ,					)
class  lowerCamelCase__ (	__lowercase			):






    def __init__(			self     ,A ):
          super().__init__(UpperCAmelCase__ )
          UpperCAmelCase          =	config

          UpperCAmelCase          =	BertEmbeddings(UpperCAmelCase__ )
          UpperCAmelCase          =	DeeBertEncoder(UpperCAmelCase__ )
          UpperCAmelCase          =	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     ,A ):
          UpperCAmelCase          =	value






    def     _UpperCamelCase							(			self     ,A ):
          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     ,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:
                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) * -10_000.0

          # 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__ (	__lowercase			):






    def __init__(			self     ,A     ,A ):
          UpperCAmelCase          =	message
          UpperCAmelCase          =	exit_layer  # start from 1!







class  lowerCamelCase__ (	nn.Module			):






    def __init__(			self     ,A ):
          super().__init__()
          UpperCAmelCase          =	BertPooler(UpperCAmelCase__ )
          UpperCAmelCase          =	nn.Dropout(config.hidden_dropout_prob )
          UpperCAmelCase          =	nn.Linear(config.hidden_size     ,config.num_labels )






    def     _UpperCamelCase							(			self     ,A ):
          # Pooler
          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,
    also takes care of multi-layer training. '''   ,					__lowercase   ,					)
class  lowerCamelCase__ (	__lowercase			):






    def __init__(			self     ,A ):
          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     ,A=None     ,A=None     ,A=None     ,A=None     ,A=None     ,A=None     ,A=None     ,A=-1     ,A=False     ,):
          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 json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_UpperCamelCase					      =  {"""UserAgent""": UserAgent().random}

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	script.contents[0]
      UpperCAmelCase          =	json.loads(data[data.find("""{\"config\""""							) : -1]							)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class  lowerCamelCase__ :






    def __init__(			self     ,A ):
          UpperCAmelCase          =	F'''https://www.instagram.com/{username}/'''
          UpperCAmelCase          =	self.get_json()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	requests.get(self.url     ,headers=A ).text
          UpperCAmelCase          =	BeautifulSoup(A     ,"""html.parser""" ).find_all("""script""" )
          try:
                return extract_user_profile(scripts[4] )
          except (json.decoder.JSONDecodeError, KeyError):
                return extract_user_profile(scripts[3] )






    def __repr__(			self ):
          return F'''{self.__class__.__name__}(\'{self.username}\')'''






    def __str__(			self ):
          return F'''{self.fullname} ({self.username}) is {self.biography}'''






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["username"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["full_name"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["biography"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["business_email"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["external_url"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_followed_by"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_follow"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_owner_to_timeline_media"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["profile_pic_url_hd"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["is_verified"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["is_private"]







def _a			(		_snake_case = "github"							):







      """simple docstring"""






      import os

      if os.environ.get("""CI"""							):
            return  # test failing on GitHub Actions
      UpperCAmelCase          =	InstagramUser(_snake_case							)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data				,	_snake_case							)
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 150
      assert instagram_user.number_of_followers > 12_0000
      assert instagram_user.number_of_followings > 15
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith("""https://instagram."""							)
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  _UpperCamelCase					      =  InstagramUser("""github""")
  print(instagram_user)
  print(F"""{instagram_user.number_of_posts = }""")
  print(F"""{instagram_user.number_of_followers = }""")
  print(F"""{instagram_user.number_of_followings = }""")
  print(F"""{instagram_user.email = }""")
  print(F"""{instagram_user.website = }""")
  print(F"""{instagram_user.profile_picture_url = }""")
  print(F"""{instagram_user.is_verified = }""")
  print(F"""{instagram_user.is_private = }""")



