

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links




def 					_lowercase     (				a_				:					List[str]					,a_				:					Dict=7				)				->	int:




					'''simple docstring'''







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

					# The id of a workflow (not of a workflow run)
					__magic_name__							=    '636036'

					__magic_name__							=    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}'

					__magic_name__							=    requests.get(a_					,headers=a_				).json()

					return result["workflow_runs"]




def 					_lowercase     (				a_				:					Any				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    get_daily_ci_runs(a_				)
					__magic_name__							=    None
					for workflow_run in workflow_runs:
										if workflow_run["status"] == "completed":
															__magic_name__							=    workflow_run['id']
															break

					return workflow_run_id




def 					_lowercase     (				a_				:					Any					,a_				:					List[Any]					,a_				:					List[Any]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    get_last_daily_ci_runs(a_				)
					if workflow_run_id is not None:
										__magic_name__							=    get_artifacts_links(worflow_run_id=a_					,token=a_				)
										for artifact_name in artifact_names:
															if artifact_name in artifacts_links:
																				__magic_name__							=    artifacts_links[artifact_name]
																				download_artifact(
																				    artifact_name=a_					,artifact_url=a_					,output_dir=a_					,token=a_				)




def 					_lowercase     (				a_				:					int					,a_				:					str					,a_				:					Optional[int]				)				->	Dict:




					'''simple docstring'''







					get_last_daily_ci_artifacts(a_					,a_					,a_				)

					__magic_name__							=    {}
					for artifact_name in artifact_names:
										__magic_name__							=    os.path.join(a_					,F'{artifact_name}.zip'				)
										if os.path.isfile(a_				):
															__magic_name__							=    {}
															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:
																																			__magic_name__							=    f.read().decode('UTF-8'				)

					return results







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()  , {}      )
						self.assertDictEqual(MockClass(a=2      ).to_kwargs()  , {'a': 2}      )
						self.assertDictEqual(MockClass(a=2  , b=__UpperCamelCase      ).to_kwargs()  , {'a': 2, 'b': True}      )
						self.assertDictEqual(MockClass(a=2  , c=2.25      ).to_kwargs()  , {'a': 2, 'c': 2.25}      )







	@require_cuda
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale  , 1024.0      )
						self.assertEqual(scaler._growth_factor  , 2.0      )

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor  , 0.5      )
						self.assertEqual(scaler._growth_interval  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
						execute_subprocess_async(__UpperCamelCase  , env=os.environ.copy()      )


if __name__ == "__main__":
						A__						=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A__						=							Accelerator(kwargs_handlers=[ddp_scaler])
						A__						=							torch.nn.Linear(100, 200)
						A__						=							accelerator.prepare(model)

						# Check the values changed in kwargs
						A__						=							""
						A__						=							model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




