
"""simple docstring"""





from typing import List, Optional, Union

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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Dict						   =		'''informer'''
		__lowercase							:  Union[str, Any]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}




		def __init__(     self							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "student_t"							,  lowerCAmelCase__ = "nll"							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "mean"							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 6_4							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = "gelu"							,  lowerCAmelCase__ = 0.05							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 1_0_0							,  lowerCAmelCase__ = 0.02							,  lowerCAmelCase__=True							,  lowerCAmelCase__ = "prob"							,  lowerCAmelCase__ = 5							,  lowerCAmelCase__ = True							,  **lowerCAmelCase__							,  ):
				# time series specific configuration
				__SCREAMING_SNAKE_CASE            = prediction_length
				__SCREAMING_SNAKE_CASE            = context_length or prediction_length
				__SCREAMING_SNAKE_CASE            = distribution_output
				__SCREAMING_SNAKE_CASE            = loss
				__SCREAMING_SNAKE_CASE            = input_size
				__SCREAMING_SNAKE_CASE            = num_time_features
				__SCREAMING_SNAKE_CASE            = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				__SCREAMING_SNAKE_CASE            = scaling
				__SCREAMING_SNAKE_CASE            = num_dynamic_real_features
				__SCREAMING_SNAKE_CASE            = num_static_real_features
				__SCREAMING_SNAKE_CASE            = num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The cardinality should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = cardinality
				else:
						__SCREAMING_SNAKE_CASE            = [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The embedding dimension should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = embedding_dimension
				else:
						__SCREAMING_SNAKE_CASE            = [min(5_0							,  (cat + 1) // 2) for cat in self.cardinality]

				__SCREAMING_SNAKE_CASE            = num_parallel_samples

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

				__SCREAMING_SNAKE_CASE            = dropout
				__SCREAMING_SNAKE_CASE            = attention_dropout
				__SCREAMING_SNAKE_CASE            = activation_dropout
				__SCREAMING_SNAKE_CASE            = encoder_layerdrop
				__SCREAMING_SNAKE_CASE            = decoder_layerdrop

				__SCREAMING_SNAKE_CASE            = activation_function
				__SCREAMING_SNAKE_CASE            = init_std

				__SCREAMING_SNAKE_CASE            = use_cache

				# Informer
				__SCREAMING_SNAKE_CASE            = attention_type
				__SCREAMING_SNAKE_CASE            = sampling_factor
				__SCREAMING_SNAKE_CASE            = distil

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




		@property
		def 	snake_case_     (     self):
				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
				)








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


__lowerCAmelCase		       =							logging.get_logger(__name__)




def    snake_case_     (  snake_case			,							snake_case     )  ->				List[str]:
       lowercase__:   List[str]  			=   set()
       lowercase__:   List[Any]  			=   []

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

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

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

       return selected_warnings


if __name__ == "__main__":




 def    snake_case_     (  snake_case     )  ->				str:
        return values.split(','     )

 __lowerCAmelCase		       =							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.''',
 )

 __lowerCAmelCase		       =							parser.parse_args()

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


