
import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__magic_name__            =  logging.get_logger(__name__)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				set()
    snake_case__						=				[]

    def parse_line(__lowerCAmelCase							):
        for line in fp:
            if isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							):
                snake_case__						=				line.decode("UTF-8"							)
            if "warnings summary (final)" in line:
                continue
            # This means we are outside the body of a warning
            elif not line.startswith(" "							):
                # process a single warning and move it to `selected_warnings`.
                if len(__lowerCAmelCase							) > 0:
                    snake_case__						=				"\n".join(__lowerCAmelCase							)
                    # Only keep the warnings specified in `targets`
                    if any(F""": {x}: """ in warning for x in targets							):
                        selected_warnings.add(__lowerCAmelCase							)
                    buffer.clear()
                continue
            else:
                snake_case__						=				line.strip()
                buffer.append(__lowerCAmelCase							)

    if from_gh:
        for filename in os.listdir(__lowerCAmelCase							):
            snake_case__						=				os.path.join(__lowerCAmelCase    ,		__lowerCAmelCase							)
            if not os.path.isdir(__lowerCAmelCase							):
                # read the file
                if filename != "warnings.txt":
                    continue
                with open(__lowerCAmelCase							) as fp:
                    parse_line(__lowerCAmelCase							)
    else:
        try:
            with zipfile.ZipFile(__lowerCAmelCase							) as z:
                for filename in z.namelist():
                    if not os.path.isdir(__lowerCAmelCase							):
                        # read the file
                        if filename != "warnings.txt":
                            continue
                        with z.open(__lowerCAmelCase							) as fp:
                            parse_line(__lowerCAmelCase							)
        except Exception:
            logger.warning(
                F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""							)

    return selected_warnings





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				set()

    snake_case__						=				[os.path.join(__lowerCAmelCase    ,		__lowerCAmelCase							) for p in os.listdir(__lowerCAmelCase							) if (p.endswith(".zip"							) or from_gh)]
    for p in paths:
        selected_warnings.update(extract_warnings_from_single_artifact(__lowerCAmelCase    ,		__lowerCAmelCase							)							)

    return selected_warnings


if __name__ == "__main__":





     def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         return values.split(","							)

     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
     parser.add_argument(
         '''--output_dir''',
         type=str,
         required=True,
         help='''Where to store the downloaded artifacts and other result files.''',
     )
     parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
     # optional parameters
     parser.add_argument(
         '''--targets''',
         default='''DeprecationWarning,UserWarning,FutureWarning''',
         type=list_str,
         help='''Comma-separated list of target warning(s) which we want to extract.''',
     )
     parser.add_argument(
         '''--from_gh''',
         action='''store_true''',
         help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
     )

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  args.from_gh
     if from_gh:
          # The artifacts have to be downloaded using `actions/download-artifact@v3`
          pass
     else:
          os.makedirs(args.output_dir, exist_ok=True)

          # get download links
          __magic_name__            =  get_artifacts_links(args.workflow_run_id, token=args.token)
          with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
               json.dump(artifacts, fp, ensure_ascii=False, indent=4)

          # download artifacts
          for idx, (name, url) in enumerate(artifacts.items()):
               print(name)
               print(url)
               print('''=''' * 80)
               download_artifact(name, url, args.output_dir, args.token)
               # Be gentle to GitHub
               time.sleep(1)

    # extract warnings from artifacts
     __magic_name__            =  extract_warnings(args.output_dir, args.targets)
     __magic_name__            =  sorted(selected_warnings)
     with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
          json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				torch.exp(__lowerCAmelCase							)
    snake_case__						=				torch.sum(__lowerCAmelCase    ,		dim=1							)  # sum of exp(x_i)
    snake_case__						=				torch.sum(x * exp_x    ,		dim=1							)  # sum of x_i * exp(x_i)
    return torch.log(__lowerCAmelCase							) - B / A




class 					_SCREAMING_SNAKE_CASE  (      nn.Module ):
      def __init__(			self		,	lowerCamelCase ):
          super().__init__()
          snake_case__						=				config.output_attentions
          snake_case__						=				config.output_hidden_states
          snake_case__						=				nn.ModuleList([BertLayer(lowerCamelCase ) for _ in range(config.num_hidden_layers )] )
          snake_case__						=				nn.ModuleList([BertHighway(lowerCamelCase ) for _ in range(config.num_hidden_layers )] )

          snake_case__						=				[-1 for _ in range(config.num_hidden_layers )]
      def A_			(			self		,	lowerCamelCase ):
          if (type(lowerCamelCase ) is float) or (type(lowerCamelCase ) is int):
              for i in range(len(self.early_exit_entropy ) ):
                  snake_case__						=				x
          else:
              snake_case__						=				x
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				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		,	lowerCamelCase		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	):
          snake_case__						=				()
          snake_case__						=				()
          snake_case__						=				()
          for i, layer_module in enumerate(self.layer ):
              if self.output_hidden_states:
                  snake_case__						=				all_hidden_states + (hidden_states,)

              snake_case__						=				layer_module(
                  lowerCamelCase		,	lowerCamelCase		,	head_mask[i]		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				layer_outputs[0]

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

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

              snake_case__						=				self.highway[i](lowerCamelCase )
              # logits, pooled_output

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

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

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

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

          snake_case__						=				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). '       ,					__UpperCamelCase       ,					)
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase ):
          super().__init__(lowerCamelCase )
          snake_case__						=				config

          snake_case__						=				BertEmbeddings(lowerCamelCase )
          snake_case__						=				DeeBertEncoder(lowerCamelCase )
          snake_case__						=				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		,	lowerCamelCase ):
          snake_case__						=				value
      def A_			(			self		,	lowerCamelCase ):
          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		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=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:
              snake_case__						=				input_ids.size()
          elif inputs_embeds is not None:
              snake_case__						=				inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds" )

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

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

          snake_case__						=				encoder_extended_attention_mask.to(
              dtype=next(self.parameters() ).dtype )  # fp16 compatibility
          snake_case__						=				(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]
          snake_case__						=				self.get_head_mask(lowerCamelCase		,	self.config.num_hidden_layers )

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

          snake_case__						=				(
              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 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				message
          snake_case__						=				exit_layer  # start from 1!





class 					_SCREAMING_SNAKE_CASE  (      nn.Module ):
      def __init__(			self		,	lowerCamelCase ):
          super().__init__()
          snake_case__						=				BertPooler(lowerCamelCase )
          snake_case__						=				nn.Dropout(config.hidden_dropout_prob )
          snake_case__						=				nn.Linear(config.hidden_size		,	config.num_labels )



      def A_			(			self		,	lowerCamelCase ):
          # Pooler
          snake_case__						=				encoder_outputs[0]
          snake_case__						=				self.pooler(lowerCamelCase )
          # "return" pooler_output

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

          # Dropout and classification
          snake_case__						=				bmodel_output[1]

          snake_case__						=				self.dropout(lowerCamelCase )
          snake_case__						=				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. '       ,					__UpperCamelCase       ,					)
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase ):
          super().__init__(lowerCamelCase )
          snake_case__						=				config.num_labels
          snake_case__						=				config.num_hidden_layers

          snake_case__						=				DeeBertModel(lowerCamelCase )
          snake_case__						=				nn.Dropout(config.hidden_dropout_prob )
          snake_case__						=				nn.Linear(config.hidden_size		,	self.config.num_labels )

          self.init_weights()



      @add_start_docstrings_to_model_forward(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=-1		,	lowerCamelCase=False		,	):
          snake_case__						=				self.num_layers
          try:
              snake_case__						=				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

              snake_case__						=				outputs[1]

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

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

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

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



