
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      (UnCLIPScheduler,)
	def 		_lowercase    (			self    :     Union[str, Any]			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[Any]:
					lowercase_	    =   {
					    '''num_train_timesteps''': 1_0_0_0,
					    '''variance_type''': '''fixed_small_log''',
					    '''clip_sample''': True,
					    '''clip_sample_range''': 1.0,
					    '''prediction_type''': '''epsilon''',
					}

					config.update(**SCREAMING_SNAKE_CASE_			)
					return config
	def 		_lowercase    (			self    :     List[Any]			)			->	Any:
					for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			)			->	Any:
					for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	List[Any]:
					for clip_sample_range in [1, 5, 1_0, 2_0]:
									self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	Any:
					for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	Dict:
					for time_step in [0, 5_0_0, 9_9_9]:
									for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
													if prev_timestep is not None and prev_timestep >= time_step:
																	continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_			,				prev_timestep=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[Any]			)			->	Any:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config(variance_type='''fixed_small_log'''			)
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_54_96_25			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.9_99_49_87			)			) < 1e-5
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config(variance_type='''learned_range'''			)
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   0.5

					assert scheduler._get_variance(1			,				predicted_variance=SCREAMING_SNAKE_CASE_			) - -10.1_71_27_90 < 1e-5
					assert scheduler._get_variance(4_8_7			,				predicted_variance=SCREAMING_SNAKE_CASE_			) - -5.7_99_80_52 < 1e-5
					assert scheduler._get_variance(9_9_9			,				predicted_variance=SCREAMING_SNAKE_CASE_			) - -0.0_01_00_11 < 1e-5
	def 		_lowercase    (			self    :     Optional[int]			)			->	Tuple:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   scheduler.timesteps

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter
					lowercase_	    =   torch.manual_seed(0			)

					for i, t in enumerate(SCREAMING_SNAKE_CASE_			):
									# 1. predict noise residual
									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									# 2. predict previous mean of sample x_t-1
									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				generator=SCREAMING_SNAKE_CASE_			).prev_sample

									lowercase_	    =   pred_prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					assert abs(result_sum.item() - 2_52.2_68_24_95			) < 1e-2
					assert abs(result_mean.item() - 0.3_28_47_43			) < 1e-3
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(2_5			)

					lowercase_	    =   scheduler.timesteps

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter
					lowercase_	    =   torch.manual_seed(0			)

					for i, t in enumerate(SCREAMING_SNAKE_CASE_			):
									# 1. predict noise residual
									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									if i + 1 == timesteps.shape[0]:
													lowercase_	    =   None
									else:
													lowercase_	    =   timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									lowercase_	    =   scheduler.step(
									    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				prev_timestep=SCREAMING_SNAKE_CASE_			,				generator=SCREAMING_SNAKE_CASE_			).prev_sample

									lowercase_	    =   pred_prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					assert abs(result_sum.item() - 2_58.2_04_49_83			) < 1e-2
					assert abs(result_mean.item() - 0.3_36_20_38			) < 1e-3
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Tuple:
					pass




	def 		_lowercase    (			self    :     List[Any]			)			->	Tuple:
					pass






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a         =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 							a		(				snake_case__:   Dict		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return (abs(source - target   ) / target) < 0.0_1




@pytest.mark.integration
def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   _TestCommandArgs(dataset=snake_case__		,  all_configs=snake_case__		,  save_infos=snake_case__   )
				lowercase_	    =   TestCommand(*snake_case__   )
				test_command.run()
				lowercase_	    =   os.path.join(snake_case__		,  '''README.md'''   )
				assert os.path.exists(snake_case__   )
				lowercase_	    =   DatasetInfosDict.from_directory(snake_case__   )
				lowercase_	    =   DatasetInfosDict(
				    {
				        '''default''': DatasetInfo(
				            features=Features(
				                {
				                    '''tokens''': Sequence(Value('''string'''   )   ),
				                    '''ner_tags''': Sequence(
				                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']   )   ),
				                    '''langs''': Sequence(Value('''string'''   )   ),
				                    '''spans''': Sequence(Value('''string'''   )   ),
				                }   )		,  splits=[
				                {
				                    '''name''': '''train''',
				                    '''num_bytes''': 2_351_563,
				                    '''num_examples''': 10_000,
				                },
				                {
				                    '''name''': '''validation''',
				                    '''num_bytes''': 238_418,
				                    '''num_examples''': 1_000,
				                },
				            ]		,  download_size=3_940_680		,  dataset_size=2_589_981		,  )
				    }   )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								lowercase_    ,      lowercase_	    =   getattr(dataset_infos['''default''']		,  snake_case__   ), getattr(expected_dataset_infos['''default''']		,  snake_case__   )
								if key == "num_bytes":
												assert is_apercent_close(snake_case__		,  snake_case__   )
								elif key == "splits":
												assert list(snake_case__   ) == list(snake_case__   )
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes		,  expected[split].num_bytes   )
								else:
												result == expected




