



'''simple docstring'''
import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_=7							):
						_UpperCamelCase					:		Optional[int] 				=   None
						if token is not None:
												_UpperCamelCase					:		str 				=   {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

						# The id of a workflow (not of a workflow run)
						_UpperCamelCase					:		List[Any] 				=   '636036'

						_UpperCamelCase					:		List[str] 				=   f'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'

						_UpperCamelCase					:		Optional[Any] 				=   requests.get(A__  ,  headers=A__							).json()

						return result["workflow_runs"]






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Any 				=   get_daily_ci_runs(A__							)
						_UpperCamelCase					:		Dict 				=   None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		_UpperCamelCase					:		Union[str, Any] 				=   workflow_run['id']
																		break

						return workflow_run_id






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Tuple 				=   get_last_daily_ci_runs(A__							)
						if workflow_run_id is not None:
												_UpperCamelCase					:		List[str] 				=   get_artifacts_links(worflow_run_id=A__  ,  token=A__							)
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								_UpperCamelCase					:		List[Any] 				=   artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=A__  ,  artifact_url=A__  ,  output_dir=A__  ,  token=A__							)






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						get_last_daily_ci_artifacts(A__  ,  A__  ,  A__							)

						_UpperCamelCase					:		int 				=   {}
						for artifact_name in artifact_names:
												_UpperCamelCase					:		Union[str, Any] 				=   os.path.join(A__  ,  f'{artifact_name}.zip'							)
												if os.path.isfile(A__							):
																		_UpperCamelCase					:		List[str] 				=   {}
																		with zipfile.ZipFile(A__							) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(A__							):
																																				# read the file
																																				with z.open(A__							) as f:
																																										_UpperCamelCase					:		int 				=   f.read().decode('UTF-8'							)

						return results







'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

   with open(A__    ,						'wb'    ) as f:
      f.write(json.dumps(A__    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()

