
"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     (UnCLIPScheduler,)




	def       _UpperCamelCase   (			self		,			**UpperCAmelCase_ )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   {
			    'num_train_timesteps': 1000,
			    'variance_type': 'fixed_small_log',
			    'clip_sample': True,
			    'clip_sample_range': 1.0,
			    'prediction_type': 'epsilon',
			}

			config.update(**UpperCAmelCase_ )
			return config




	def       _UpperCamelCase   (			self )		->							List[str]:
			for timesteps in [1, 5, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Any:
			for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							List[str]:
			for clip_sample_range in [1, 5, 10, 20]:
					self.check_over_configs(clip_sample_range=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							str:
			for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Any:
			for time_step in [0, 500, 999]:
					for prev_timestep in [None, 5, 100, 250, 500, 750]:
							if prev_timestep is not None and prev_timestep >= time_step:
									continue

							self.check_over_forward(time_step=UpperCAmelCase_		,			prev_timestep=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      str          =   self.scheduler_classes[0]
			lowerCamelCase					:      Any          =   self.get_scheduler_config(variance_type='fixed_small_log' )
			lowerCamelCase					:      Optional[Any]          =   scheduler_class(**UpperCAmelCase_ )

			assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_0_0_0E-1_0 ) ) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0549625 ) ) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9994987 ) ) < 1E-5




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Dict          =   self.scheduler_classes[0]
			lowerCamelCase					:      Union[str, Any]          =   self.get_scheduler_config(variance_type='learned_range' )
			lowerCamelCase					:      Any          =   scheduler_class(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[int]          =   0.5

			assert scheduler._get_variance(1		,			predicted_variance=UpperCAmelCase_ ) - -10.1712790 < 1E-5
			assert scheduler._get_variance(487		,			predicted_variance=UpperCAmelCase_ ) - -5.7998052 < 1E-5
			assert scheduler._get_variance(999		,			predicted_variance=UpperCAmelCase_ ) - -0.0010011 < 1E-5




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      int          =   self.scheduler_classes[0]
			lowerCamelCase					:      List[str]          =   self.get_scheduler_config()
			lowerCamelCase					:      str          =   scheduler_class(**UpperCAmelCase_ )

			lowerCamelCase					:      List[Any]          =   scheduler.timesteps

			lowerCamelCase					:      int          =   self.dummy_model()
			lowerCamelCase					:      Any          =   self.dummy_sample_deter
			lowerCamelCase					:      Union[str, Any]          =   torch.manual_seed(0 )

			for i, t in enumerate(UpperCAmelCase_ ):
					# 1. predict noise residual
					lowerCamelCase					:      Tuple          =   model(UpperCAmelCase_		,			UpperCAmelCase_ )

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase					:      Optional[Any]          =   scheduler.step(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			generator=UpperCAmelCase_ ).prev_sample

					lowerCamelCase					:      List[Any]          =   pred_prev_sample

			lowerCamelCase					:      int          =   torch.sum(torch.abs(UpperCAmelCase_ ) )
			lowerCamelCase					:      str          =   torch.mean(torch.abs(UpperCAmelCase_ ) )

			assert abs(result_sum.item() - 252.2682495 ) < 1E-2
			assert abs(result_mean.item() - 0.3284743 ) < 1E-3




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Dict          =   self.scheduler_classes[0]
			lowerCamelCase					:      List[Any]          =   self.get_scheduler_config()
			lowerCamelCase					:      Tuple          =   scheduler_class(**UpperCAmelCase_ )

			scheduler.set_timesteps(25 )

			lowerCamelCase					:      int          =   scheduler.timesteps

			lowerCamelCase					:      Union[str, Any]          =   self.dummy_model()
			lowerCamelCase					:      Tuple          =   self.dummy_sample_deter
			lowerCamelCase					:      Union[str, Any]          =   torch.manual_seed(0 )

			for i, t in enumerate(UpperCAmelCase_ ):
					# 1. predict noise residual
					lowerCamelCase					:      int          =   model(UpperCAmelCase_		,			UpperCAmelCase_ )

					if i + 1 == timesteps.shape[0]:
							lowerCamelCase					:      Tuple          =   None
					else:
							lowerCamelCase					:      Union[str, Any]          =   timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase					:      Optional[Any]          =   scheduler.step(
					    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			prev_timestep=UpperCAmelCase_		,			generator=UpperCAmelCase_ ).prev_sample

					lowerCamelCase					:      List[Any]          =   pred_prev_sample

			lowerCamelCase					:      Union[str, Any]          =   torch.sum(torch.abs(UpperCAmelCase_ ) )
			lowerCamelCase					:      str          =   torch.mean(torch.abs(UpperCAmelCase_ ) )

			assert abs(result_sum.item() - 258.2044983 ) < 1E-2
			assert abs(result_mean.item() - 0.3362038 ) < 1E-3




	def       _UpperCamelCase   (			self )		->							Dict:
			pass




	def       _UpperCamelCase   (			self )		->							int:
			pass








"""simple docstring"""

import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_A						=			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_A						=			[file for file in filepaths if file != file.lower()]
if upper_files:
					print(F"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

_A						=			[file for file in filepaths if ' ' in file]
if space_files:
					print(F"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

_A						=			[file for file in filepaths if '-' in file]
if hyphen_files:
					print(F"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

_A						=			[file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

_A						=			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)






