import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links




def 	A_ (							snake_case							:   str ,      snake_case							:   List[str]=7 )      ->    int:



						'''simple docstring'''

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

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

						__UpperCamelCase       =			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       =			requests.get(__snake_case ,      headers=__snake_case ).json()

						return result["workflow_runs"]




def 	A_ (							snake_case							:   Dict )      ->    List[Any]:



						'''simple docstring'''

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

						return workflow_run_id




def 	A_ (							snake_case							:   str ,      snake_case							:   int ,      snake_case							:   Any )      ->    Any:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Union[str, Any] ,      snake_case							:   Optional[int] )      ->    Optional[int]:



						'''simple docstring'''

						get_last_daily_ci_artifacts(__snake_case ,      __snake_case ,      __snake_case )

						__UpperCamelCase       =			{}
						for artifact_name in artifact_names:
												__UpperCamelCase       =			os.path.join(__snake_case ,      f"{artifact_name}.zip" )
												if os.path.isfile(__snake_case ):
																		__UpperCamelCase       =			{}
																		with zipfile.ZipFile(__snake_case ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(__snake_case ):
																																				# read the file
																																				with z.open(__snake_case ) as f:
																																										__UpperCamelCase       =			f.read().decode('''UTF-8''' )

						return results






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowercase__							:							Optional[Any]									=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowercase__							:							Dict									=			"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"






class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			tempfile.mkdtemp()
													os.makedirs(os.path.join(self.diffusers_dir     ,	'''schedulers/'''			)			)
													__UpperCamelCase       =			self.diffusers_dir
													shutil.copy(
													    os.path.join(SCREAMING_SNAKE_CASE_     ,	'''src/diffusers/schedulers/scheduling_ddpm.py'''			)     ,	os.path.join(self.diffusers_dir     ,	'''schedulers/scheduling_ddpm.py'''			)     ,	)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			'''src/diffusers'''
													shutil.rmtree(self.diffusers_dir			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			comment + F"\nclass {class_name}(nn.Module):\n" + class_code
													if overwrite_result is not None:
																			__UpperCamelCase       =			comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
													__UpperCamelCase       =			black.Mode(target_versions={black.TargetVersion.PYaa}     ,	line_length=119			)
													__UpperCamelCase       =			black.format_str(SCREAMING_SNAKE_CASE_     ,	mode=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			os.path.join(self.diffusers_dir     ,	'''new_code.py'''			)
													with open(SCREAMING_SNAKE_CASE_     ,	'''w'''     ,	newline='''\n'''			) as f:
																			f.write(SCREAMING_SNAKE_CASE_			)
													if overwrite_result is None:
																			self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE_			)			) == 0			)
													else:
																			check_copies.is_copy_consistent(f.name     ,	overwrite=SCREAMING_SNAKE_CASE_			)
																			with open(SCREAMING_SNAKE_CASE_     ,	'''r'''			) as f:
																									self.assertTrue(f.read()     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''			)
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     ,	'''DDPMSchedulerOutput'''     ,	REFERENCE_CODE + '''\n'''     ,	)

													# With no empty line at the end
													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     ,	'''DDPMSchedulerOutput'''     ,	SCREAMING_SNAKE_CASE_     ,	)

													# Copy consistency with rename
													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''     ,	'''TestSchedulerOutput'''     ,	re.sub('''DDPM'''     ,	'''Test'''     ,	SCREAMING_SNAKE_CASE_			)     ,	)

													# Copy consistency with a really long name
													__UpperCamelCase       =			'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
													self.check_copy_consistency(
													    F"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"     ,	F"{long_class_name}SchedulerOutput"     ,	re.sub('''Bert'''     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)     ,	)

													# Copy consistency with overwrite
													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''     ,	'''TestSchedulerOutput'''     ,	SCREAMING_SNAKE_CASE_     ,	overwrite_result=re.sub('''DDPM'''     ,	'''Test'''     ,	SCREAMING_SNAKE_CASE_			)     ,	)




