


"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCamelCase__				:Optional[Any]			   =	get_logger(__name__)






class      A(  enum.Enum		):

		"""simple docstring"""





		A    =							"all_checks"
		A    =							"basic_checks"
		A    =							"no_checks"






class      A(  __lowercase		):

		"""simple docstring"""





		pass






class      A(  __lowercase		):

		"""simple docstring"""





		pass






class      A(  __lowercase		):

		"""simple docstring"""





		pass






class      A(  __lowercase		):

		"""simple docstring"""





		pass





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None		)					->  Tuple:






			if expected_checksums is None:
						logger.info('''Unable to verify checksums.'''		)
						return
			if len(set(snake_case__		) - set(snake_case__		)		) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(snake_case__		) - set(snake_case__		)		)		)
			if len(set(snake_case__		) - set(snake_case__		)		) > 0:
						raise UnexpectedDownloadedFile(str(set(snake_case__		) - set(snake_case__		)		)		)
			_UpperCamelCase					:Dict					     =	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			_UpperCamelCase					:Optional[Any]					     =	''' for ''' + verification_name if verification_name is not None else ''''''
			if len(snake_case__		) > 0:
						raise NonMatchingChecksumError(
						    f"Checksums didn\'t match{for_verification_name}:\n"
						    f"{bad_urls}\n"
						    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''		)
			logger.info('''All the checksums matched successfully''' + for_verification_name		)






class      A(  __lowercase		):

		"""simple docstring"""





		pass






class      A(  __lowercase		):

		"""simple docstring"""





		pass






class      A(  __lowercase		):

		"""simple docstring"""





		pass






class      A(  __lowercase		):

		"""simple docstring"""





		pass





def 				A_       ( snake_case__       ,					snake_case__		)					->  Any:






			if expected_splits is None:
						logger.info('''Unable to verify splits sizes.'''		)
						return
			if len(set(snake_case__		) - set(snake_case__		)		) > 0:
						raise ExpectedMoreSplits(str(set(snake_case__		) - set(snake_case__		)		)		)
			if len(set(snake_case__		) - set(snake_case__		)		) > 0:
						raise UnexpectedSplits(str(set(snake_case__		) - set(snake_case__		)		)		)
			_UpperCamelCase					:Any					     =	[
			    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(snake_case__		) > 0:
						raise NonMatchingSplitsSizesError(str(snake_case__		)		)
			logger.info('''All the splits matched successfully.'''		)





def 				A_       ( snake_case__       ,					snake_case__ = True		)					->  dict:






			if record_checksum:
						_UpperCamelCase					:List[Any]					     =	shaaaa()
						with open(snake_case__       ,					'''rb'''		) as f:
									for chunk in iter(lambda: f.read(1 << 20		)       ,					B''''''		):
												m.update(snake_case__		)
									_UpperCamelCase					:str					     =	m.hexdigest()
			else:
						_UpperCamelCase					:Tuple					     =	None
			return {"num_bytes": os.path.getsize(snake_case__		), "checksum": checksum}





def 				A_       ( snake_case__		)					->  Dict:






			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			int						=		(KDPMaDiscreteScheduler,)
						UpperCamelCase_				:			Optional[int]						=		10

						def 			A_					(      self							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						{
								    """num_train_timesteps""": 11_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**a					)
								return config

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


								'''simple docstring'''

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a					)

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


								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,				[0.0002, 0.002, 0.02]					):
										self.check_over_configs(beta_start=a							,				beta_end=a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=a					)

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


								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config(prediction_type="""v_prediction"""					)
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6934e-07					) < 1e-2
										assert abs(result_mean.item() - 6.1112e-10					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.693_4286_5017_0972e-07					) < 1e-2
										assert abs(result_mean.item() - 0.0002					) < 1e-3

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3







						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps							,				device=a					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter.to(a					) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if str(a					).startswith("""cpu"""					):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3



