



'''simple docstring'''



import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


lowerCAmelCase	:		Tuple				=   '''__DUMMY_TRANSFORMERS_USER__'''
lowerCAmelCase	:		Optional[int]				=   '''Dummy User'''
lowerCAmelCase	:		Dict				=   '''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'''

lowerCAmelCase	:		Tuple				=   '''https://hub-ci.huggingface.co'''
lowerCAmelCase	:		Union[str, Any]				=   CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowerCAmelCase	:		List[Any]				=   CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
lowerCAmelCase	:		List[Any]				=   Path('''~/.huggingface/hub_ci_token''').expanduser()



@pytest.fixture
def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	monkeypatch.setattr(
	    "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"				,		lowerCamelCase					)



@pytest.fixture
def   __lowerCAmelCase					(							lowerCamelCase   :						int					):






	'''simple docstring'''




	monkeypatch.setattr("datasets.config.HF_ENDPOINT"				,		lowerCamelCase					)
	monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"				,		lowerCamelCase					)



@pytest.fixture
def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"				,		lowerCamelCase					)



@pytest.fixture
def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Optional[Any]					):






	'''simple docstring'''




	HfFolder.save_token(lowerCamelCase					)
	yield
	HfFolder.delete_token()



@pytest.fixture(scope="session"					)
def   __lowerCAmelCase					(							):






	'''simple docstring'''




	return HfApi(endpoint=lowerCamelCase					)



@pytest.fixture(scope="session"					)
def   __lowerCAmelCase					(							lowerCamelCase   :						HfApi					):






	'''simple docstring'''




	__lowerCAmelCase												=  HfFolder.get_token()
	HfFolder.save_token(lowerCamelCase					)
	yield CI_HUB_USER_TOKEN
	if previous_token is not None:
		HfFolder.save_token(lowerCamelCase					)



@pytest.fixture
def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	def _cleanup_repo(lowerCamelCase   :						Optional[Any]					):
		hf_api.delete_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"					)

	return _cleanup_repo



@pytest.fixture
def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]					):






	'''simple docstring'''




	@contextmanager
	def _temporary_repo(lowerCamelCase   :						Any					):
		try:
			yield repo_id
		finally:
			cleanup_repo(lowerCamelCase					)

	return _temporary_repo



@pytest.fixture(scope="session"					)
def   __lowerCAmelCase					(							lowerCamelCase   :						HfApi				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Dict					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''repo_txt_data-{int(time.time() * 10e3					)}'''
	__lowerCAmelCase												=  f'''{CI_HUB_USER}/{repo_name}'''
	hf_api.create_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"				,		private=lowerCamelCase					)
	hf_api.upload_file(
	    token=lowerCamelCase				,		path_or_fileobj=str(lowerCamelCase					)				,		path_in_repo="data/text_data.txt"				,		repo_id=lowerCamelCase				,		repo_type="dataset"				,		)
	yield repo_id
	try:
		hf_api.delete_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"					)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass



@pytest.fixture()
def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Optional[int]					):






	'''simple docstring'''




	return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"					)
def   __lowerCAmelCase					(							lowerCamelCase   :						HfApi				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''repo_zipped_txt_data-{int(time.time() * 10e3					)}'''
	__lowerCAmelCase												=  f'''{CI_HUB_USER}/{repo_name}'''
	hf_api.create_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"				,		private=lowerCamelCase					)
	hf_api.upload_file(
	    token=lowerCamelCase				,		path_or_fileobj=str(lowerCamelCase					)				,		path_in_repo="data.zip"				,		repo_id=lowerCamelCase				,		repo_type="dataset"				,		)
	yield repo_id
	try:
		hf_api.delete_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"					)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass



@pytest.fixture()
def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Any					):






	'''simple docstring'''




	return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"					)
def   __lowerCAmelCase					(							lowerCamelCase   :						HfApi				,		lowerCamelCase   :						Any				,		lowerCamelCase   :						Any					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''repo_zipped_img_data-{int(time.time() * 10e3					)}'''
	__lowerCAmelCase												=  f'''{CI_HUB_USER}/{repo_name}'''
	hf_api.create_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"				,		private=lowerCamelCase					)
	hf_api.upload_file(
	    token=lowerCamelCase				,		path_or_fileobj=str(lowerCamelCase					)				,		path_in_repo="data.zip"				,		repo_id=lowerCamelCase				,		repo_type="dataset"				,		)
	yield repo_id
	try:
		hf_api.delete_repo(lowerCamelCase				,		token=lowerCamelCase				,		repo_type="dataset"					)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass






@pytest.fixture()
def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	return hf_private_dataset_repo_zipped_img_data_




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						self.assertEqual(output_a.shape			,				sample.shape     )
						self.assertEqual(output_a.shape			,				output_a.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )