



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


UpperCAmelCase_							:      Tuple      =  False
class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self			,	UpperCAmelCase__=32		):
							set_seed(0		)
							A__							=       UNetaDModel(sample_size=UpperCAmelCase__			,	in_channels=3			,	out_channels=3		)
							A__							=       torch.optim.SGD(model.parameters()			,	lr=0.0_001		)
							return model, optimizer



	@slow
	def 			__A							(       self		):
							A__							=       "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							A__							=       DDPMScheduler(
							    num_train_timesteps=1_000			,	beta_start=0.0_001			,	beta_end=0.02			,	beta_schedule="linear"			,	clip_sample=UpperCAmelCase__			,	)
							A__							=       DDIMScheduler(
							    num_train_timesteps=1_000			,	beta_start=0.0_001			,	beta_end=0.02			,	beta_schedule="linear"			,	clip_sample=UpperCAmelCase__			,	)

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0		)
							A__							=       [torch.randn((4, 3, 32, 32)		).clip(-1			,	1		).to(UpperCAmelCase__		) for _ in range(4		)]
							A__							=       [torch.randn((4, 3, 32, 32)		).to(UpperCAmelCase__		) for _ in range(4		)]
							A__							=       [torch.randint(0			,	1_000			,	(4,)		).long().to(UpperCAmelCase__		) for _ in range(4		)]

							# train with a DDPM scheduler
							A__						,       A__							=       self.get_model_optimizer(resolution=32		)
							model.train().to(UpperCAmelCase__		)
							for i in range(4		):
													optimizer.zero_grad()
													A__							=       ddpm_scheduler.add_noise(clean_images[i]			,	noise[i]			,	timesteps[i]		)
													A__							=       model(UpperCAmelCase__			,	timesteps[i]		).sample
													A__							=       torch.nn.functional.mse_loss(UpperCAmelCase__			,	noise[i]		)
													loss.backward()
													optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							A__						,       A__							=       self.get_model_optimizer(resolution=32		)
							model.train().to(UpperCAmelCase__		)
							for i in range(4		):
													optimizer.zero_grad()
													A__							=       ddim_scheduler.add_noise(clean_images[i]			,	noise[i]			,	timesteps[i]		)
													A__							=       model(UpperCAmelCase__			,	timesteps[i]		).sample
													A__							=       torch.nn.functional.mse_loss(UpperCAmelCase__			,	noise[i]		)
													loss.backward()
													optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(UpperCAmelCase__			,	UpperCAmelCase__			,	atol=1e-5		)		)
							self.assertTrue(torch.allclose(UpperCAmelCase__			,	UpperCAmelCase__			,	atol=1e-5		)		)










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





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

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       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}"""

						A__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		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:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





