

import copy
import os
from typing import Union

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


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """bridgetower_vision_model"""




  def __init__(				self							,   lowerCAmelCase=768							,   lowerCAmelCase=12							,   lowerCAmelCase=3							,   lowerCAmelCase=16							,   lowerCAmelCase=288							,   lowerCAmelCase=1							,   lowerCAmelCase=1e-05							,   lowerCAmelCase=False							,   lowerCAmelCase=True							,   lowerCAmelCase=False							,   **lowerCAmelCase							,   )  -> Dict:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase					)
        _lowercase	       =hidden_size
        _lowercase	       =num_hidden_layers
        _lowercase	       =num_channels
        _lowercase	       =patch_size
        _lowercase	       =image_size
        _lowercase	       =initializer_factor
        _lowercase	       =layer_norm_eps
        _lowercase	       =stop_gradient
        _lowercase	       =share_layernorm
        _lowercase	       =remove_last_layer




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> "PretrainedConfig":
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =cls.get_config_dict(lowerCAmelCase							,   **lowerCAmelCase					)

        if config_dict.get('model_type'					) == "bridgetower":
              _lowercase	       =config_dict['text_config']

        if "model_type" in config_dict and hasattr(cls							,   'model_type'					) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                  F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

        return cls.from_dict(lowerCAmelCase							,   **lowerCAmelCase					)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """bridgetower_text_model"""




  def __init__(				self							,   lowerCAmelCase=50_265							,   lowerCAmelCase=768							,   lowerCAmelCase=12							,   lowerCAmelCase=12							,   lowerCAmelCase=1							,   lowerCAmelCase=3_072							,   lowerCAmelCase="gelu"							,   lowerCAmelCase=0.1							,   lowerCAmelCase=0.1							,   lowerCAmelCase=514							,   lowerCAmelCase=1							,   lowerCAmelCase=1e-05							,   lowerCAmelCase=1							,   lowerCAmelCase=0							,   lowerCAmelCase=2							,   lowerCAmelCase="absolute"							,   lowerCAmelCase=True							,   **lowerCAmelCase							,   )  -> Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase					)

        _lowercase	       =vocab_size
        _lowercase	       =hidden_size
        _lowercase	       =num_hidden_layers
        _lowercase	       =num_attention_heads
        _lowercase	       =hidden_act
        _lowercase	       =initializer_factor
        _lowercase	       =intermediate_size
        _lowercase	       =hidden_dropout_prob
        _lowercase	       =attention_probs_dropout_prob
        _lowercase	       =max_position_embeddings
        _lowercase	       =type_vocab_size
        _lowercase	       =layer_norm_eps
        _lowercase	       =position_embedding_type
        _lowercase	       =use_cache
        _lowercase	       =pad_token_id
        _lowercase	       =bos_token_id
        _lowercase	       =eos_token_id




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> "PretrainedConfig":
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =cls.get_config_dict(lowerCAmelCase							,   **lowerCAmelCase					)

        if config_dict.get('model_type'					) == "bridgetower":
              _lowercase	       =config_dict['text_config']

        if "model_type" in config_dict and hasattr(cls							,   'model_type'					) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                  F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

        return cls.from_dict(lowerCAmelCase							,   **lowerCAmelCase					)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """bridgetower"""




  def __init__(				self							,   lowerCAmelCase=True							,   lowerCAmelCase="gelu"							,   lowerCAmelCase=768							,   lowerCAmelCase=1							,   lowerCAmelCase=1e-05							,   lowerCAmelCase=False							,   lowerCAmelCase="add"							,   lowerCAmelCase=12							,   lowerCAmelCase=6							,   lowerCAmelCase=False							,   lowerCAmelCase=False							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   **lowerCAmelCase							,   )  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =kwargs.pop('text_config_dict'							,   lowerCAmelCase					)
        _lowercase	       =kwargs.pop('vision_config_dict'							,   lowerCAmelCase					)

        super().__init__(**lowerCAmelCase					)
        _lowercase	       =share_cross_modal_transformer_layers
        _lowercase	       =hidden_act
        _lowercase	       =hidden_size
        _lowercase	       =initializer_factor
        _lowercase	       =layer_norm_eps
        _lowercase	       =share_link_tower_layers
        _lowercase	       =link_tower_type
        _lowercase	       =num_attention_heads
        _lowercase	       =num_hidden_layers
        _lowercase	       =tie_word_embeddings
        _lowercase	       =init_layernorm_from_vision_encoder

        if text_config is None:
              _lowercase	       ={}
              logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'					)

        if vision_config is None:
              _lowercase	       ={}
              logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'					)

        _lowercase	       =BridgeTowerTextConfig(**lowerCAmelCase					)
        _lowercase	       =BridgeTowerVisionConfig(**lowerCAmelCase					)




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        return cls(text_config=text_config.to_dict()							,   vision_config=vision_config.to_dict()							,   **lowerCAmelCase					)




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






        _lowercase	       =copy.deepcopy(self.__dict__					)
        _lowercase	       =self.text_config.to_dict()
        _lowercase	       =self.vision_config.to_dict()
        _lowercase	       =self.__class__.model_type
        return output





import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def      a      (			A__	:   Dict     )    ->      str:





      """simple docstring"""



      _lowercase	       ={}

      _lowercase	       =job['started_at']
      _lowercase	       =job['completed_at']

      _lowercase	       =date_parser.parse(A__     )
      _lowercase	       =date_parser.parse(A__     )

      _lowercase	       =round((end_datetime - start_datetime).total_seconds() / 60.0     )

      _lowercase	       =start
      _lowercase	       =end
      _lowercase	       =duration_in_min

      return job_info
def      a      (			A__	:   Dict      ,   A__	:   str=None     )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =None
      if token is not None:
            _lowercase	       ={'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

      _lowercase	       =F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
      _lowercase	       =requests.get(A__      ,   headers=A__     ).json()
      _lowercase	       ={}

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

            for i in range(A__     ):
                  _lowercase	       =requests.get(url + F'''&page={i + 2}'''      ,   headers=A__     ).json()
                  job_time.update({job['name']: extract_time_from_single_job(A__     ) 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']}")


