




"""simple docstring"""

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


a							      =     logging.get_logger(__name__)






def 		_snake_case	(   _snake_case				:   Tuple							,    _snake_case				:   Any				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  set()
      _A     =  []

      def parse_line(_snake_case				:   Tuple				):
            for line in fp:
                  if isinstance(UpperCamelCase__							,    UpperCamelCase__				):
                        _A     =  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(UpperCamelCase__				) > 0:
                              _A     =  '\n'.join(UpperCamelCase__				)
                              # Only keep the warnings specified in `targets`
                              if any(F''': {x}: ''' in warning for x in targets				):
                                    selected_warnings.add(UpperCamelCase__				)
                              buffer.clear()
                        continue
                  else:
                        _A     =  line.strip()
                        buffer.append(UpperCamelCase__				)

      if from_gh:
            for filename in os.listdir(UpperCamelCase__				):
                  _A     =  os.path.join(UpperCamelCase__							,    UpperCamelCase__				)
                  if not os.path.isdir(UpperCamelCase__				):
                        # read the file
                        if filename != "warnings.txt":
                              continue
                        with open(UpperCamelCase__				) as fp:
                              parse_line(UpperCamelCase__				)
      else:
            try:
                  with zipfile.ZipFile(UpperCamelCase__				) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(UpperCamelCase__				):
                                    # read the file
                                    if filename != "warnings.txt":
                                          continue
                                    with z.open(UpperCamelCase__				) as fp:
                                          parse_line(UpperCamelCase__				)
            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 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   Tuple				)       ->   List[str]:






      '''simple docstring'''


      _A     =  set()

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

      return selected_warnings


if __name__ == "__main__":






       def 		_snake_case	(   _snake_case				:   int				)       ->   List[str]:






             '''simple docstring'''


             return values.split(','				)

       a							      =     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.''',
       )

       a							      =     parser.parse_args()

       a							      =     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
              a							      =     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
       a							      =     extract_warnings(args.output_dir, args.targets)
       a							      =     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)


'''simple docstring'''


from typing import List, Optional, Union

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


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

__lowerCAmelCase						:							Optional[int]    							=						{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}






class     A ( UpperCAmelCase ):
							a_										=   '''time_series_transformer'''
							a_										=   {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''num_hidden_layers''': '''encoder_layers''',
							}





							def __init__(   self					:				str  ,		__a					:				Optional[int] = None  ,		__a					:				Optional[int] = None  ,		__a					:				str = "student_t"  ,		__a					:				str = "nll"  ,		__a					:				int = 1  ,		__a					:				List[int] = [1, 2, 3, 4, 5, 6, 7]  ,		__a					:				Optional[Union[str, bool]] = "mean"  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				Optional[List[int]] = None  ,		__a					:				Optional[List[int]] = None  ,		__a					:				int = 3_2  ,		__a					:				int = 3_2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				bool = True  ,		__a					:				str = "gelu"  ,		__a					:				int = 6_4  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				int = 1_0_0  ,		__a					:				float = 0.0_2  ,		__a					:				Optional[int]=True  ,		**__a					:				str  ,		)				->						int:
								# time series specific configuration
								__UpperCAmelCase      					=      prediction_length
								__UpperCAmelCase      					=      context_length or prediction_length
								__UpperCAmelCase      					=      distribution_output
								__UpperCAmelCase      					=      loss
								__UpperCAmelCase      					=      input_size
								__UpperCAmelCase      					=      num_time_features
								__UpperCAmelCase      					=      lags_sequence
								__UpperCAmelCase      					=      scaling
								__UpperCAmelCase      					=      num_dynamic_real_features
								__UpperCAmelCase      					=      num_static_real_features
								__UpperCAmelCase      					=      num_static_categorical_features
								if cardinality and num_static_categorical_features > 0:
									if len(__a						) != num_static_categorical_features:
										raise ValueError(
										    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''						)
									__UpperCAmelCase      					=      cardinality
								else:
									__UpperCAmelCase      					=      [0]
								if embedding_dimension and num_static_categorical_features > 0:
									if len(__a						) != num_static_categorical_features:
										raise ValueError(
										    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''						)
									__UpperCAmelCase      					=      embedding_dimension
								else:
									__UpperCAmelCase      					=      [min(5_0  ,		(cat + 1) // 2						) for cat in self.cardinality]
								__UpperCAmelCase      					=      num_parallel_samples

								# Transformer architecture configuration
								__UpperCAmelCase      					=      input_size * len(__a						) + self._number_of_features
								__UpperCAmelCase      					=      d_model
								__UpperCAmelCase      					=      encoder_attention_heads
								__UpperCAmelCase      					=      decoder_attention_heads
								__UpperCAmelCase      					=      encoder_ffn_dim
								__UpperCAmelCase      					=      decoder_ffn_dim
								__UpperCAmelCase      					=      encoder_layers
								__UpperCAmelCase      					=      decoder_layers

								__UpperCAmelCase      					=      dropout
								__UpperCAmelCase      					=      attention_dropout
								__UpperCAmelCase      					=      activation_dropout
								__UpperCAmelCase      					=      encoder_layerdrop
								__UpperCAmelCase      					=      decoder_layerdrop

								__UpperCAmelCase      					=      activation_function
								__UpperCAmelCase      					=      init_std

								__UpperCAmelCase      					=      use_cache

								super().__init__(is_encoder_decoder=__a  ,		**__a						)






							@property
							def    snake_case__    (   self					:				List[Any]						)				->						int:
								return (
								    sum(self.embedding_dimension						)
								    + self.num_dynamic_real_features
								    + self.num_time_features
								    + self.num_static_real_features
								    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
								)
