import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		(UnCLIPScheduler,)


						def        __A       (       self	:				str    ,		**UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{
												    '''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        __A       (       self	:				Any						):





												'''simple docstring'''





												for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





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


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





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


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_scheduler_config(variance_type='''fixed_small_log'''						)
												SCREAMING_SNAKE_CASE							:    int							=							scheduler_class(**UpperCamelCase__						)

												assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000E-10						)						) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.054_9625						)						) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.999_4987						)						) < 1E-5


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    Any							=							self.get_scheduler_config(variance_type='''learned_range'''						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							scheduler_class(**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							0.5

												assert scheduler._get_variance(1    ,		predicted_variance=UpperCamelCase__						) - -10.171_2790 < 1E-5
												assert scheduler._get_variance(487    ,		predicted_variance=UpperCamelCase__						) - -5.799_8052 < 1E-5
												assert scheduler._get_variance(999    ,		predicted_variance=UpperCamelCase__						) - -0.001_0011 < 1E-5


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    int							=							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE							:    Tuple							=							scheduler_class(**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    int							=							scheduler.timesteps

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    str							=							self.dummy_sample_deter
												SCREAMING_SNAKE_CASE							:    Any							=							torch.manual_seed(0						)

												for i, t in enumerate(UpperCamelCase__						):
																		# 1. predict noise residual
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		UpperCamelCase__						)

																		# 2. predict previous mean of sample x_t-1
																		SCREAMING_SNAKE_CASE							:    int							=							scheduler.step(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		generator=UpperCamelCase__						).prev_sample

																		SCREAMING_SNAKE_CASE							:    int							=							pred_prev_sample

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.sum(torch.abs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.mean(torch.abs(UpperCamelCase__						)						)

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


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    Any							=							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE							:    List[str]							=							scheduler_class(**UpperCamelCase__						)

												scheduler.set_timesteps(25						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scheduler.timesteps

												SCREAMING_SNAKE_CASE							:    Any							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.dummy_sample_deter
												SCREAMING_SNAKE_CASE							:    Any							=							torch.manual_seed(0						)

												for i, t in enumerate(UpperCamelCase__						):
																		# 1. predict noise residual
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		UpperCamelCase__						)

																		if i + 1 == timesteps.shape[0]:
																								SCREAMING_SNAKE_CASE							:    int							=							None
																		else:
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							timesteps[i + 1]

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

																		SCREAMING_SNAKE_CASE							:    Tuple							=							pred_prev_sample

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.sum(torch.abs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.mean(torch.abs(UpperCamelCase__						)						)

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


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												pass



						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												pass






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





