"""simple docstring"""
from typing import List, Optional, Union

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


lowercase__ 	=     logging.get_logger(__name__)

lowercase__ 	=     {
    '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        __snake_case	(					__lowerCAmelCase    ):
      a__				=		"""time_series_transformer"""
      a__				=		{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }



      def __init__(						self   ,      lowercase = None   ,      lowercase = None   ,      lowercase = "student_t"   ,      lowercase = "nll"   ,      lowercase = 1   ,      lowercase = [1, 2, 3, 4, 5, 6, 7]   ,      lowercase = "mean"   ,      lowercase = 0   ,      lowercase = 0   ,      lowercase = 0   ,      lowercase = 0   ,      lowercase = None   ,      lowercase = None   ,      lowercase = 32   ,      lowercase = 32   ,      lowercase = 2   ,      lowercase = 2   ,      lowercase = 2   ,      lowercase = 2   ,      lowercase = True   ,      lowercase = "gelu"   ,      lowercase = 64   ,      lowercase = 0.1   ,      lowercase = 0.1   ,      lowercase = 0.1   ,      lowercase = 0.1   ,      lowercase = 0.1   ,      lowercase = 1_00   ,      lowercase = 0.02   ,      lowercase=True   ,      **lowercase   ,      )     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     prediction_length
        a__:					List[str]      							=     context_length or prediction_length
        a__:					List[str]      							=     distribution_output
        a__:					Tuple      							=     loss
        a__:					str      							=     input_size
        a__:					List[Any]      							=     num_time_features
        a__:					Optional[Any]      							=     lags_sequence
        a__:					Optional[Any]      							=     scaling
        a__:					Any      							=     num_dynamic_real_features
        a__:					Optional[int]      							=     num_static_real_features
        a__:					Optional[Any]      							=     num_static_categorical_features
        if cardinality and num_static_categorical_features > 0:
          if len(lowercase) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`')
          a__:					Dict      							=     cardinality
        else:
          a__:					Dict      							=     [0]
        if embedding_dimension and num_static_categorical_features > 0:
          if len(lowercase) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
          a__:					List[str]      							=     embedding_dimension
        else:
          a__:					Union[str, Any]      							=     [min(50   ,      (cat + 1) // 2) for cat in self.cardinality]
        a__:					Tuple      							=     num_parallel_samples

        # Transformer architecture configuration
        a__:					List[str]      							=     input_size * len(lowercase) + self._number_of_features
        a__:					str      							=     d_model
        a__:					Union[str, Any]      							=     encoder_attention_heads
        a__:					List[str]      							=     decoder_attention_heads
        a__:					str      							=     encoder_ffn_dim
        a__:					Any      							=     decoder_ffn_dim
        a__:					List[Any]      							=     encoder_layers
        a__:					Optional[Any]      							=     decoder_layers

        a__:					Optional[int]      							=     dropout
        a__:					Optional[int]      							=     attention_dropout
        a__:					List[str]      							=     activation_dropout
        a__:					str      							=     encoder_layerdrop
        a__:					Dict      							=     decoder_layerdrop

        a__:					List[str]      							=     activation_function
        a__:					Union[str, Any]      							=     init_std

        a__:					List[Any]      							=     use_cache

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



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        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
        )



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__:					Tuple      							=     {}

  a__:					Tuple      							=     job['started_at']
  a__:					int      							=     job['completed_at']

  a__:					Any      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )

  a__:					str      							=     round((end_datetime - start_datetime).total_seconds() / 60.0       )

  a__:					Any      							=     start
  a__:					Dict      							=     end
  a__:					Optional[int]      							=     duration_in_min

  return job_info







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[int]:
  a__:					Tuple      							=     None
  if token is not None:
    a__:					List[str]      							=     {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:					int      							=     F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
  a__:					Union[str, Any]      							=     requests.get(_SCREAMING_SNAKE_CASE		,					headers=_SCREAMING_SNAKE_CASE       ).json()
  a__:					str      							=     {}

  try:
    job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE       ) for job in result['jobs']}       )
    a__:					Dict      							=     math.ceil((result['total_count'] - 100) / 100       )

    for i in range(_SCREAMING_SNAKE_CASE       ):
      a__:					str      							=     requests.get(url + F'&page={i + 2}'		,					headers=_SCREAMING_SNAKE_CASE       ).json()
      job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE       ) for job in result['jobs']}       )

    return job_time
  except Exception:
    print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'       )

  return {}


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
 lowercase__ 	=     parser.parse_args()

 lowercase__ 	=     get_job_time(args.workflow_run_id)
 lowercase__ 	=     dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

 for k, v in job_time.items():
  print(f"{k}: {v['duration']}")


