


"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}



def   snake_case						(				_a:     str		,   _a:     str		,   _a:     str		,   _a:     int		,   _a:     Union[str, Any]							)->		Any:
      '''simple docstring'''


      for attribute in key.split('.'							):
            lowerCamelCase__           =		getattr(A__		,   A__							)

      if weight_type is not None:
            lowerCamelCase__           =		getattr(A__		,   A__							).shape
      else:
            lowerCamelCase__           =		hf_pointer.shape

      assert hf_shape == value.shape, (
          F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
          F' {value.shape} for {full_name}'
      )

      if weight_type == "weight":
            lowerCamelCase__           =		value
      elif weight_type == "weight_g":
            lowerCamelCase__           =		value
      elif weight_type == "weight_v":
            lowerCamelCase__           =		value
      elif weight_type == "bias":
            lowerCamelCase__           =		value
      else:
            lowerCamelCase__           =		value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)



def   snake_case						(				_a:     Tuple		,   _a:     Optional[int]		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      lowerCamelCase__           =		fairseq_model.state_dict()

      lowerCamelCase__           =		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCamelCase__           =		False
            if "conv_layers" in name:
                  load_conv_layer(
                      A__		,   A__		,   A__		,   A__		,   hf_model.config.feat_extract_norm == 'group'		,   )
                  lowerCamelCase__           =		True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCamelCase__           =		'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                        if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
                              lowerCamelCase__           =		True
                              if "*" in mapped_key:
                                    lowerCamelCase__           =		name.split(A__							)[0].split('.'							)[-2]
                                    lowerCamelCase__           =		mapped_key.replace('*'		,   A__							)
                              if "weight_g" in name:
                                    lowerCamelCase__           =		'weight_g'
                              elif "weight_v" in name:
                                    lowerCamelCase__           =		'weight_v'
                              elif "weight" in name:
                                    lowerCamelCase__           =		'weight'
                              elif "bias" in name:
                                    lowerCamelCase__           =		'bias'
                              else:
                                    lowerCamelCase__           =		None
                              set_recursively(A__		,   A__		,   A__		,   A__		,   A__							)
                        continue
            if not is_used:
                  unused_weights.append(A__							)

      logger.warning(F'Unused weights: {unused_weights}'							)



def   snake_case						(				_a:     Dict		,   _a:     int		,   _a:     Dict		,   _a:     Optional[Any]		,   _a:     Optional[Any]							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		full_name.split('conv_layers.'							)[-1]
      lowerCamelCase__           =		name.split('.'							)
      lowerCamelCase__           =		int(items[0]							)
      lowerCamelCase__           =		int(items[1]							)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F'{full_name} has size {value.shape}, but'
                      F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                  )
                  lowerCamelCase__           =		value
                  logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F'{full_name} has size {value.shape}, but'
                      F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                  )
                  lowerCamelCase__           =		value
                  logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                      " found."
                  )
                  lowerCamelCase__           =		value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F'{full_name} has size {value.shape}, but'
                      F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                  )
                  lowerCamelCase__           =		value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
      else:
            unused_weights.append(A__							)



def   snake_case						(				_a:     List[str]		,   _a:     Optional[int]							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		SEWConfig()
      if is_finetuned:
            lowerCamelCase__           =		model.wav_encoder.wav_model.cfg
      else:
            lowerCamelCase__           =		model.cfg

      lowerCamelCase__           =		fs_config.conv_bias
      lowerCamelCase__           =		eval(fs_config.conv_feature_layers							)
      lowerCamelCase__           =		[x[0] for x in conv_layers]
      lowerCamelCase__           =		[x[1] for x in conv_layers]
      lowerCamelCase__           =		[x[2] for x in conv_layers]
      lowerCamelCase__           =		'gelu'
      lowerCamelCase__           =		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      lowerCamelCase__           =		0.0
      lowerCamelCase__           =		fs_config.activation_fn.name
      lowerCamelCase__           =		fs_config.encoder_embed_dim
      lowerCamelCase__           =		0.02
      lowerCamelCase__           =		fs_config.encoder_ffn_embed_dim
      lowerCamelCase__           =		1E-5
      lowerCamelCase__           =		fs_config.encoder_layerdrop
      lowerCamelCase__           =		fs_config.encoder_attention_heads
      lowerCamelCase__           =		fs_config.conv_pos_groups
      lowerCamelCase__           =		fs_config.conv_pos
      lowerCamelCase__           =		len(A__							)
      lowerCamelCase__           =		fs_config.encoder_layers
      lowerCamelCase__           =		fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            lowerCamelCase__           =		model.cfg
            lowerCamelCase__           =		fs_config.final_dropout
            lowerCamelCase__           =		fs_config.layerdrop
      lowerCamelCase__           =		fs_config.activation_dropout
      lowerCamelCase__           =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      lowerCamelCase__           =		fs_config.attention_dropout
      lowerCamelCase__           =		fs_config.dropout_input
      lowerCamelCase__           =		fs_config.dropout
      lowerCamelCase__           =		fs_config.mask_channel_length
      lowerCamelCase__           =		fs_config.mask_channel_prob
      lowerCamelCase__           =		fs_config.mask_length
      lowerCamelCase__           =		fs_config.mask_prob

      lowerCamelCase__           =		'Wav2Vec2FeatureExtractor'
      lowerCamelCase__           =		'Wav2Vec2CTCTokenizer'

      return config



@torch.no_grad()
def   snake_case						(				_a:     List[str]		,   _a:     int		,   _a:     Any=None		,   _a:     str=None		,   _a:     Optional[int]=True							)->		Tuple:
      '''simple docstring'''


      if is_finetuned:
            lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]		,   arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
      else:
            lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

      if config_path is not None:
            lowerCamelCase__           =		SEWConfig.from_pretrained(A__							)
      else:
            lowerCamelCase__           =		convert_config(model[0]		,   A__							)
      lowerCamelCase__           =		model[0].eval()

      lowerCamelCase__           =		True if config.feat_extract_norm == 'layer' else False
      lowerCamelCase__           =		WavaVecaFeatureExtractor(
          feature_size=1		,   sampling_rate=16000		,   padding_value=0		,   do_normalize=A__		,   return_attention_mask=A__		,   )

      if is_finetuned:
            if dict_path:
                  lowerCamelCase__           =		Dictionary.load(A__							)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCamelCase__           =		target_dict.pad_index
                  lowerCamelCase__           =		target_dict.bos_index
                  lowerCamelCase__           =		target_dict.pad_index
                  lowerCamelCase__           =		target_dict.bos_index
                  lowerCamelCase__           =		target_dict.eos_index
                  lowerCamelCase__           =		len(target_dict.symbols							)
                  lowerCamelCase__           =		os.path.join(A__		,   'vocab.json'							)
                  if not os.path.isdir(A__							):
                        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A__							)							)
                        return
                  os.makedirs(A__		,   exist_ok=A__							)
                  with open(A__		,   'w'		,   encoding='utf-8'							) as vocab_handle:
                        json.dump(target_dict.indices		,   A__							)
                  lowerCamelCase__           =		WavaVecaCTCTokenizer(
                      A__		,   unk_token=target_dict.unk_word		,   pad_token=target_dict.pad_word		,   bos_token=target_dict.bos_word		,   eos_token=target_dict.eos_word		,   word_delimiter_token='|'		,   do_lower_case=A__		,   )
                  lowerCamelCase__           =		WavaVecaProcessor(feature_extractor=A__		,   tokenizer=A__							)
                  processor.save_pretrained(A__							)

            lowerCamelCase__           =		SEWForCTC(A__							)
      else:
            lowerCamelCase__           =		SEWModel(A__							)
            feature_extractor.save_pretrained(A__							)

      recursively_load_weights(A__		,   A__		,   A__							)

      hf_model.save_pretrained(A__							)


if __name__ == "__main__":
   _snake_case          =							argparse.ArgumentParser()
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
   parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
   parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
   parser.add_argument(
       "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
   )
   _snake_case          =							parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )







"""simple docstring"""


import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_snake_case          =							WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])



def   snake_case						(				_a:     Any							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		test_results.split(' '							)

      lowerCamelCase__           =		0
      lowerCamelCase__           =		0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      lowerCamelCase__           =		expressions[-2] if '=' in expressions[-1] else expressions[-1]

      for i, expression in enumerate(_a							):
            if "failed" in expression:
                  failed += int(expressions[i - 1]							)
            if "passed" in expression:
                  success += int(expressions[i - 1]							)

      return failed, success, time_spent



def   snake_case						(				_a:     Optional[int]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		{}
      lowerCamelCase__           =		None
      lowerCamelCase__           =		False
      for line in failures_short_lines.split('\n'							):
            if re.search(R'_ \[doctest\]'		,   _a							):
                  lowerCamelCase__           =		True
                  lowerCamelCase__           =		line.split(' '							)[2]
            elif in_error and not line.split(' '							)[0].isdigit():
                  lowerCamelCase__           =		line
                  lowerCamelCase__           =		False

      return failures



class   _a      :
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		title

          lowerCamelCase__           =		doc_test_results['time_spent'].split(','				)[0]
          lowerCamelCase__           =		doc_test_results['success']
          lowerCamelCase__           =		doc_test_results['failures']
          lowerCamelCase__           =		self.n_success + self.n_failures

          # Failures and success of the modeling tests
          lowerCamelCase__           =		doc_test_results
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		[self._time_spent]
          lowerCamelCase__           =		0

          for time in time_spent:
                lowerCamelCase__           =		time.split(':'				)

                # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
                if len(SCREAMING_SNAKE_CASE__				) == 1:
                      lowerCamelCase__           =		[0, 0, time_parts[0]]

                lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		int(time_parts[0]				), int(time_parts[1]				), float(time_parts[2]				)
                total_secs += hours * 36_00 + minutes * 60 + seconds

          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
          return F'{int(SCREAMING_SNAKE_CASE__				)}h{int(SCREAMING_SNAKE_CASE__				)}m{int(SCREAMING_SNAKE_CASE__				)}s'
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {
              "type": "section",
              "text": {
                  "type": "plain_text",
                  "text": F'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
                  "emoji": True,
              },
              "accessory": {
                  "type": "button",
                  "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                  "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          return {
              "type": "section",
              "text": {
                  "type": "plain_text",
                  "text": (
                      F'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
                      F' {self.time}.'
                  ),
                  "emoji": True,
              },
              "accessory": {
                  "type": "button",
                  "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                  "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		40
          lowerCamelCase__           =		{k: v['failed'] for k, v in doc_test_results.items() if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)}

          lowerCamelCase__           =		''
          for category, failures in category_failures.items():
                if len(SCREAMING_SNAKE_CASE__				) == 0:
                      continue

                if report != "":
                      report += "\n\n"

                report += F'*{category} failures*:'.ljust(line_length // 2				).rjust(line_length // 2				) + "\n"
                report += "`"
                report += "`\n`".join(SCREAMING_SNAKE_CASE__				)
                report += "`"

          return {
              "type": "section",
              "text": {
                  "type": "mrkdwn",
                  "text": F'The following examples had failures:\n\n\n{report}\n',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[self.header]

          if self.n_failures > 0:
                blocks.append(self.failures				)

          if self.n_failures > 0:
                blocks.extend([self.category_failures]				)

          if self.n_failures == 0:
                blocks.append(self.no_failures				)

          return json.dumps(SCREAMING_SNAKE_CASE__				)
    @staticmethod
    def      _UpperCamelCase       ( ):
          lowerCamelCase__           =		[
              {
                  'type': 'section',
                  'text': {
                      'type': 'plain_text',
                      'text': 'There was an issue running the tests.',
                  },
                  'accessory': {
                      'type': 'button',
                      'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
                      'url': F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                  },
              }
          ]

          print('Sending the following payload'				)
          print(json.dumps({'blocks': json.loads(SCREAMING_SNAKE_CASE__				)}				)				)

          client.chat_postMessage(
              channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					text='There was an issue running the tests.'							,					blocks=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          print('Sending the following payload'				)
          print(json.dumps({'blocks': json.loads(self.payload				)}				)				)

          lowerCamelCase__           =		F'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else 'All tests passed.'

          lowerCamelCase__           =		client.chat_postMessage(
              channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					blocks=self.payload							,					text=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          lowerCamelCase__           =		''
          for key, value in failures.items():
                lowerCamelCase__           =		value[:2_00] + ' [Truncated]' if len(SCREAMING_SNAKE_CASE__				) > 2_50 else value
                failures_text += F'*{key}*\n_{value}_\n\n'

          lowerCamelCase__           =		job_name
          lowerCamelCase__           =		{'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

          if job_link is not None:
                lowerCamelCase__           =		{
                    'type': 'button',
                    'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
                    'url': job_link,
                }

          return [
              {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
              content,
              {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
          ]


    def      _UpperCamelCase       ( self							:					Optional[int]				):
          if self.thread_ts is None:
                raise ValueError('Can only post reply if a post has been made.'				)

          lowerCamelCase__           =		self.doc_test_results.pop('job_link'				)
          self.doc_test_results.pop('failures'				)
          self.doc_test_results.pop('success'				)
          self.doc_test_results.pop('time_spent'				)

          lowerCamelCase__           =		sorted(self.doc_test_results.items()							,					key=lambda SCREAMING_SNAKE_CASE__				: t[0]				)
          for job, job_result in sorted_dict:
                if len(job_result['failures']				):
                      lowerCamelCase__           =		F'*Num failures* :{len(job_result["failed"]				)} \n'
                      lowerCamelCase__           =		job_result['failures']
                      lowerCamelCase__           =		self.get_reply_blocks(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					text=SCREAMING_SNAKE_CASE__				)

                      print('Sending the following reply'				)
                      print(json.dumps({'blocks': blocks}				)				)

                      client.chat_postMessage(
                          channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					text=F'Results for {job}'							,					blocks=SCREAMING_SNAKE_CASE__							,					thread_ts=self.thread_ts['ts']							,					)

                      time.sleep(1				)





def   snake_case						(				)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		os.environ['GITHUB_RUN_ID']
      lowerCamelCase__           =		F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
      lowerCamelCase__           =		requests.get(_a							).json()
      lowerCamelCase__           =		{}

      try:
            jobs.update({job['name']: job['html_url'] for job in result['jobs']}							)
            lowerCamelCase__           =		math.ceil((result['total_count'] - 100) / 100							)

            for i in range(_a							):
                  lowerCamelCase__           =		requests.get(url + F'&page={i + 2}'							).json()
                  jobs.update({job['name']: job['html_url'] for job in result['jobs']}							)

            return jobs
      except Exception as e:
            print('Unknown error, could not fetch links.'		,   _a							)

      return {}



def   snake_case						(				_a:     str							)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		{}

      if os.path.exists(_a							):
            lowerCamelCase__           =		os.listdir(_a							)
            for file in files:
                  try:
                        with open(os.path.join(_a		,   _a							)		,   encoding='utf-8'							) as f:
                              lowerCamelCase__           =		f.read()
                  except UnicodeDecodeError as e:
                        raise ValueError(F'Could not open {os.path.join(_a		,   _a							)}.'							) from e

      return _artifact



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      class   _a      :
          def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str				):
                lowerCamelCase__           =		name
                lowerCamelCase__           =		[]
          def __str__( self							:					Dict				):
                return self.name


          def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
                self.paths.append({'name': self.name, 'path': path}				)





      lowerCamelCase__           =		{}

      lowerCamelCase__           =		filter(os.path.isdir		,   os.listdir()							)
      for directory in directories:
            lowerCamelCase__           =		directory
            if artifact_name not in _available_artifacts:
                  lowerCamelCase__           =		Artifact(_a							)

                  _available_artifacts[artifact_name].add_path(_a							)

      return _available_artifacts


if __name__ == "__main__":
   _snake_case          =							get_job_links()
   _snake_case          =							retrieve_available_artifacts()

   _snake_case          =							collections.OrderedDict(
       [
           ("*.py", "API Examples"),
           ("*.md", "MD Examples"),
       ]
   )

   # This dict will contain all the information relative to each doc test category:
   # - failed: list of failed tests
   # - failures: dict in the format 'test': 'error_message'
   _snake_case          =							{
       v: {
           "failed": [],
           "failures": {},
       }
       for v in docs.values()
   }

   # Link to the GitHub Action job
   _snake_case          =							github_actions_job_links.get("run_doctests")

   _snake_case          =							available_artifacts["doc_tests_gpu_test_reports"].paths[0]
   _snake_case          =							retrieve_artifact(artifact_path["name"])
   if "stats" in artifact:
      _snake_case      ,					_snake_case      ,					_snake_case          =							handle_test_results(artifact["stats"])
      _snake_case          =							failed
      _snake_case          =							success
      _snake_case          =							time_spent[1:-1] + ", "

      _snake_case          =							extract_first_line_failure(artifact["failures_short"])
      for line in artifact["summary_short"].split("\n"):
         if re.search("FAILED", line):
            _snake_case          =							line.replace("FAILED ", "")
            _snake_case          =							line.split()[0].replace("\n", "")

            if "::" in line:
               _snake_case      ,					_snake_case          =							line.split("::")
            else:
               _snake_case      ,					_snake_case          =							line, line

            for file_regex in docs.keys():
               if fnmatch(file_path, file_regex):
                  _snake_case          =							docs[file_regex]
                  doc_test_results[category]["failed"].append(test)

                  _snake_case          =							all_failures[test] if test in all_failures else "N/A"
                  _snake_case          =							failure
                  break

   _snake_case          =							Message("🤗 Results of the doc tests.", doc_test_results)
   message.post()
   message.post_reply()



