





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Union[str, Any]								=	(UnCLIPScheduler,)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				**lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   {
										    'num_train_timesteps': 10_00,
										    'variance_type': 'fixed_small_log',
										    'clip_sample': True,
										    'clip_sample_range': 1.0,
										    'prediction_type': 'epsilon',
										}

										config.update(**lowerCamelCase_					)
										return config







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										for timesteps in [1, 5, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										for time_step in [0, 5_00, 9_99]:
														for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																		if prev_timestep is not None and prev_timestep >= time_step:
																						continue

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







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.scheduler_classes[0]
										_snake_case :			str  						=   self.get_scheduler_config(variance_type='fixed_small_log'					)
										_snake_case :			str  						=   scheduler_class(**lowerCamelCase_					)

										assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000e-10					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.054_9625					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.999_4987					)					) < 1e-5







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.scheduler_classes[0]
										_snake_case :			Any  						=   self.get_scheduler_config(variance_type='learned_range'					)
										_snake_case :			Dict  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			List[Any]  						=   0.5

										assert scheduler._get_variance(1 ,				predicted_variance=lowerCamelCase_					) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_87 ,				predicted_variance=lowerCamelCase_					) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_99 ,				predicted_variance=lowerCamelCase_					) - -0.001_0011 < 1e-5







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.scheduler_classes[0]
										_snake_case :			str  						=   self.get_scheduler_config()
										_snake_case :			Optional[int]  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			Tuple  						=   scheduler.timesteps

										_snake_case :			Dict  						=   self.dummy_model()
										_snake_case :			Any  						=   self.dummy_sample_deter
										_snake_case :			List[str]  						=   torch.manual_seed(0					)

										for i, t in enumerate(lowerCamelCase_					):
														# 1. predict noise residual
														_snake_case :			Dict  						=   model(lowerCamelCase_ ,				lowerCamelCase_					)

														# 2. predict previous mean of sample x_t-1
														_snake_case :			Union[str, Any]  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				generator=lowerCamelCase_					).prev_sample

														_snake_case :			Union[str, Any]  						=   pred_prev_sample

										_snake_case :			str  						=   torch.sum(torch.abs(lowerCamelCase_					)					)
										_snake_case :			Dict  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_sum.item() - 252.268_2495					) < 1e-2
										assert abs(result_mean.item() - 0.328_4743					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   self.scheduler_classes[0]
										_snake_case :			Optional[int]  						=   self.get_scheduler_config()
										_snake_case :			Tuple  						=   scheduler_class(**lowerCamelCase_					)

										scheduler.set_timesteps(25					)

										_snake_case :			Union[str, Any]  						=   scheduler.timesteps

										_snake_case :			Tuple  						=   self.dummy_model()
										_snake_case :			str  						=   self.dummy_sample_deter
										_snake_case :			List[Any]  						=   torch.manual_seed(0					)

										for i, t in enumerate(lowerCamelCase_					):
														# 1. predict noise residual
														_snake_case :			Any  						=   model(lowerCamelCase_ ,				lowerCamelCase_					)

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

														# 2. predict previous mean of sample x_t-1
														_snake_case :			Any  						=   scheduler.step(
														    lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				prev_timestep=lowerCamelCase_ ,				generator=lowerCamelCase_					).prev_sample

														_snake_case :			Optional[int]  						=   pred_prev_sample

										_snake_case :			Tuple  						=   torch.sum(torch.abs(lowerCamelCase_					)					)
										_snake_case :			Dict  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_sum.item() - 258.204_4983					) < 1e-2
										assert abs(result_mean.item() - 0.336_2038					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										pass












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase_		:				Optional[Any]				=						pytest.mark.integration

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path'	,			['accuracy'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, config_name, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				assert info.config_name == config_name
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, expected'	,			[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				assert expected in config_names

@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'	,			[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    'path, expected_config, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )






