





'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    (DDPMScheduler,)


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											_snake_case:   Any  					=		{
											    'num_train_timesteps': 10_00,
											    'beta_start': 0.0_001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											    'variance_type': 'fixed_small',
											    'clip_sample': True,
											}

											config.update(**__snake_case       )
											return config


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]       ,     [0.002, 0.02, 0.2, 2]       ):
																	self.check_over_configs(beta_start=__snake_case       ,     beta_end=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											self.check_over_configs(thresholding=__snake_case       )
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__snake_case       ,     prediction_type=__snake_case       ,     sample_max_value=__snake_case       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





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

											assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87       ) - 0.00_979       )       ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99       ) - 0.02       )       ) < 1e-5


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.scheduler_classes[0]
											_snake_case:   Optional[int]  					=		self.get_scheduler_config()
											_snake_case:   List[str]  					=		scheduler_class(**__snake_case       )

											_snake_case:   List[Any]  					=		len(__snake_case       )

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

											for t in reversed(range(__snake_case       )       ):
																	# 1. predict noise residual
																	_snake_case:   List[str]  					=		model(__snake_case       ,     __snake_case       )

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

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_snake_case:   Optional[Any]  					=		pred_prev_sample

											_snake_case:   Union[str, Any]  					=		torch.sum(torch.abs(__snake_case       )       )
											_snake_case:   Union[str, Any]  					=		torch.mean(torch.abs(__snake_case       )       )

											assert abs(result_sum.item() - 258.9_606       ) < 1e-2
											assert abs(result_mean.item() - 0.3_372       ) < 1e-3


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.scheduler_classes[0]
											_snake_case:   int  					=		self.get_scheduler_config(prediction_type='v_prediction'       )
											_snake_case:   List[str]  					=		scheduler_class(**__snake_case       )

											_snake_case:   str  					=		len(__snake_case       )

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

											for t in reversed(range(__snake_case       )       ):
																	# 1. predict noise residual
																	_snake_case:   int  					=		model(__snake_case       ,     __snake_case       )

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

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_snake_case:   List[Any]  					=		pred_prev_sample

											_snake_case:   int  					=		torch.sum(torch.abs(__snake_case       )       )
											_snake_case:   Tuple  					=		torch.mean(torch.abs(__snake_case       )       )

											assert abs(result_sum.item() - 202.0_296       ) < 1e-2
											assert abs(result_mean.item() - 0.2_631       ) < 1e-3


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





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

											_snake_case:   List[Any]  					=		[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__snake_case       )

											_snake_case:   int  					=		scheduler.timesteps

											for i, timestep in enumerate(__snake_case       ):
																	if i == len(__snake_case       ) - 1:
																							_snake_case:   Optional[Any]  					=		-1
																	else:
																							_snake_case:   Union[str, Any]  					=		timesteps[i + 1]

																	_snake_case:   Any  					=		scheduler.previous_timestep(__snake_case       )
																	_snake_case:   str  					=		prev_t.item()

																	self.assertEqual(__snake_case       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





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

											_snake_case:   str  					=		[1_00, 87, 50, 51, 0]

											with self.assertRaises(__snake_case       ,     msg='`custom_timesteps` must be in descending order.'       ):
																	scheduler.set_timesteps(timesteps=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.scheduler_classes[0]
											_snake_case:   Tuple  					=		self.get_scheduler_config()
											_snake_case:   Any  					=		scheduler_class(**__snake_case       )

											_snake_case:   Any  					=		[1_00, 87, 50, 1, 0]
											_snake_case:   Optional[Any]  					=		len(__snake_case       )

											with self.assertRaises(__snake_case       ,     msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
																	scheduler.set_timesteps(num_inference_steps=__snake_case       ,     timesteps=__snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





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

											_snake_case:   Optional[Any]  					=		[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __snake_case       ,     msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,     ):
																	scheduler.set_timesteps(timesteps=__snake_case       )












'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		A   : Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





