


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


snake_case__				:	Tuple					       =					logging.get_logger(__name__)


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: Any					)     ->    Optional[int]:
   lowerCamelCase_						:   Dict		= set()
   lowerCamelCase_						:   List[Any]		= []

   def parse_line(A__						: str					):
      for line in fp:
         if isinstance(A__						,						A__					):
            lowerCamelCase_						:   Tuple		= 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(A__					) > 0:
               lowerCamelCase_						:   Any		= """\n""".join(A__					)
               # Only keep the warnings specified in `targets`
               if any(f''': {x}: ''' in warning for x in targets					):
                  selected_warnings.add(A__					)
               buffer.clear()
            continue
         else:
            lowerCamelCase_						:   Union[str, Any]		= line.strip()
            buffer.append(A__					)

   if from_gh:
      for filename in os.listdir(A__					):
         lowerCamelCase_						:   Dict		= os.path.join(A__						,						A__					)
         if not os.path.isdir(A__					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(A__					) as fp:
               parse_line(A__					)
   else:
      try:
         with zipfile.ZipFile(A__					) as z:
            for filename in z.namelist():
               if not os.path.isdir(A__					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(A__					) as fp:
                     parse_line(A__					)
      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    __lowerCamelCase   (      A__						: Any						,						A__						: Union[str, Any]					)     ->    Optional[int]:
   lowerCamelCase_						:   str		= set()

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

   return selected_warnings


if __name__ == "__main__":


      def    __lowerCamelCase   (      A__						: List[str]					)     ->    int:
         return values.split(""","""					)

      snake_case__				:	str					       =					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.',
      )

      snake_case__				:	Any					       =					parser.parse_args()

      snake_case__				:	Union[str, Any]					       =					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
            snake_case__				:	str					       =					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
      snake_case__				:	Optional[int]					       =					extract_warnings(args.output_dir, args.targets)
      snake_case__				:	Optional[int]					       =					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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__				:	Union[str, Any]					       =					logging.get_logger(__name__)

snake_case__				:	Tuple					       =					{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"unispeech-sat"



       def __init__(						self				:				List[str]		,				__a				:				Dict=32		,				__a				:				int=768		,				__a				:				int=12		,				__a				:				Tuple=12		,				__a				:				Optional[int]=3_072		,				__a				:				int="gelu"		,				__a				:				Optional[int]=0.1		,				__a				:				Union[str, Any]=0.1		,				__a				:				Any=0.1		,				__a				:				str=0.0		,				__a				:				List[str]=0.0		,				__a				:				int=0.1		,				__a				:				List[Any]=0.1		,				__a				:				Tuple=0.02		,				__a				:				str=1e-5		,				__a				:				Optional[int]="group"		,				__a				:				Any="gelu"		,				__a				:				Optional[Any]=(512, 512, 512, 512, 512, 512, 512)		,				__a				:				Optional[Any]=(5, 2, 2, 2, 2, 2, 2)		,				__a				:				List[Any]=(10, 3, 3, 3, 3, 2, 2)		,				__a				:				Optional[Any]=False		,				__a				:				Optional[int]=128		,				__a				:				Dict=16		,				__a				:				str=False		,				__a				:				List[Any]=True		,				__a				:				Tuple=0.05		,				__a				:				str=10		,				__a				:				Any=2		,				__a				:				Any=0.0		,				__a				:				List[Any]=10		,				__a				:				str=0		,				__a				:				str=320		,				__a				:				int=2		,				__a				:				Optional[Any]=0.1		,				__a				:				Optional[int]=100		,				__a				:				Any=256		,				__a				:				Optional[int]=256		,				__a				:				int=0.1		,				__a				:				Tuple="mean"		,				__a				:				List[str]=False		,				__a				:				Optional[Any]=False		,				__a				:				List[Any]=256		,				__a				:				Dict=(512, 512, 512, 512, 1_500)		,				__a				:				int=(5, 3, 3, 1, 1)		,				__a				:				str=(1, 2, 3, 1, 1)		,				__a				:				str=512		,				__a				:				str=0		,				__a				:				List[Any]=1		,				__a				:				Optional[Any]=2		,				__a				:				Optional[int]=504		,				**__a				:				Tuple		,				)					->Dict:
          super().__init__(**__a		,				pad_token_id=__a		,				bos_token_id=__a		,				eos_token_id=__a							)
          lowerCamelCase_						:   Any		= hidden_size
          lowerCamelCase_						:   Optional[int]		= feat_extract_norm
          lowerCamelCase_						:   Dict		= feat_extract_activation
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   str		= conv_bias
          lowerCamelCase_						:   int		= num_conv_pos_embeddings
          lowerCamelCase_						:   List[Any]		= num_conv_pos_embedding_groups
          lowerCamelCase_						:   str		= len(self.conv_dim							)
          lowerCamelCase_						:   Any		= num_hidden_layers
          lowerCamelCase_						:   List[str]		= intermediate_size
          lowerCamelCase_						:   Tuple		= hidden_act
          lowerCamelCase_						:   List[str]		= num_attention_heads
          lowerCamelCase_						:   Any		= hidden_dropout
          lowerCamelCase_						:   Optional[int]		= attention_dropout
          lowerCamelCase_						:   Tuple		= activation_dropout
          lowerCamelCase_						:   str		= feat_proj_dropout
          lowerCamelCase_						:   Any		= final_dropout
          lowerCamelCase_						:   Optional[Any]		= layerdrop
          lowerCamelCase_						:   str		= layer_norm_eps
          lowerCamelCase_						:   str		= initializer_range
          lowerCamelCase_						:   Optional[Any]		= vocab_size
          lowerCamelCase_						:   Optional[int]		= num_clusters
          lowerCamelCase_						:   List[Any]		= do_stable_layer_norm
          lowerCamelCase_						:   Tuple		= use_weighted_layer_sum

          if (
              (len(self.conv_stride							) != self.num_feat_extract_layers)
              or (len(self.conv_kernel							) != self.num_feat_extract_layers)
              or (len(self.conv_dim							) != self.num_feat_extract_layers)
          ):
             raise ValueError(
                 """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                 """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                 F''' {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,'''
                 F''' `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          lowerCamelCase_						:   List[str]		= apply_spec_augment
          lowerCamelCase_						:   str		= mask_time_prob
          lowerCamelCase_						:   List[Any]		= mask_time_length
          lowerCamelCase_						:   Optional[int]		= mask_time_min_masks
          lowerCamelCase_						:   List[Any]		= mask_feature_prob
          lowerCamelCase_						:   Optional[int]		= mask_feature_length
          lowerCamelCase_						:   Dict		= mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          lowerCamelCase_						:   Dict		= num_codevectors_per_group
          lowerCamelCase_						:   List[str]		= num_codevector_groups
          lowerCamelCase_						:   Union[str, Any]		= contrastive_logits_temperature
          lowerCamelCase_						:   Optional[int]		= feat_quantizer_dropout
          lowerCamelCase_						:   List[Any]		= num_negatives
          lowerCamelCase_						:   Optional[int]		= codevector_dim
          lowerCamelCase_						:   str		= proj_codevector_dim
          lowerCamelCase_						:   List[Any]		= diversity_loss_weight

          # ctc loss
          lowerCamelCase_						:   Optional[int]		= ctc_loss_reduction
          lowerCamelCase_						:   int		= ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          lowerCamelCase_						:   Dict		= classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Optional[int]		= xvector_output_dim




       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[Any]:
          return functools.reduce(operator.mul		,				self.conv_stride		,				1							)





