
'''simple docstring'''

import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests






def 	_snake_case						(      A_       :    Dict   ):
  """simple docstring"""







  a_      :	List[str]      =							{}

  a_      :	Optional[Any]      =							job["""started_at"""]
  a_      :	Optional[Any]      =							job["""completed_at"""]

  a_      :	List[str]      =							date_parser.parse(A_   )
  a_      :	Dict      =							date_parser.parse(A_   )

  a_      :	int      =							round((end_datetime - start_datetime).total_seconds() / 60.0   )

  a_      :	List[Any]      =							start
  a_      :	Optional[int]      =							end
  a_      :	str      =							duration_in_min

  return job_info






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Optional[int]=None   ):
  """simple docstring"""







  a_      :	Dict      =							None
  if token is not None:
    a_      :	Dict      =							{"""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_      :	Optional[Any]      =							requests.get(A_     ,		headers=A_   ).json()
  a_      :	Union[str, Any]      =							{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(A_   ) for job in result["""jobs"""]}   )
    a_      :	List[str]      =							math.ceil((result["""total_count"""] - 100) / 100   )

    for i in range(A_   ):
      a_      :	Tuple      =							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__":
    __snake_case:  Any								=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
    __snake_case:  List[str]								=			parser.parse_args()

    __snake_case:  Any								=			get_job_time(args.workflow_run_id)
    __snake_case:  Dict								=			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']}""")




'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       for model_result in results.values():
         for batch_size, sequence_length in zip(model_result["""bs"""]							,		model_result["""ss"""] ):
           a_      :	str      =							model_result["""result"""][batch_size][sequence_length]
           self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	List[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sgugger/tiny-distilbert-classification"""
       a_      :	str      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		only_pretrain_model=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		torchscript=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     @unittest.skipIf(torch_device == """cpu"""							,		"""Cant do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		fpaa=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Dict      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Dict      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       # set architectures equal to `None`
       a_      :	Any      =							None
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Any      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Tuple      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     @unittest.skipIf(torch_device == """cpu"""							,		"""Can't do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		fpaa=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[str]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[int]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[str]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	List[str]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tinier_bart"""
       a_      :	Union[str, Any]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Dict      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Optional[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Tuple      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tinier_bart"""
       a_      :	int      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Dict      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							"""sshleifer/tiny-gpt2"""
       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[str]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		save_to_csv=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		inference_time_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" )							,		train_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" )							,		inference_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" )							,		train_time_csv_file=os.path.join(lowerCAmelCase_							,		"""train_time.csv""" )							,		env_info_csv_file=os.path.join(lowerCAmelCase_							,		"""env.csv""" )							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_ )
         benchmark.run()
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""env.csv""" ) ).exists() )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""

       def _check_summary_is_not_empty(lowerCAmelCase_ ):
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""sequential""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""cumulative""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""current""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""total""" ) )

       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[Any]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		log_filename=os.path.join(lowerCAmelCase_							,		"""log.txt""" )							,		log_print=lowerCAmelCase_							,		trace_memory_line_by_line=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Dict      =							PyTorchBenchmark(lowerCAmelCase_ )
         a_      :	Tuple      =							benchmark.run()
         _check_summary_is_not_empty(result.inference_summary )
         _check_summary_is_not_empty(result.train_summary )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""log.txt""" ) ).exists() )


