





"""simple docstring"""


import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Union[str, Any]								=     (VQDiffusionScheduler,)







			def      _a			(			self	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[int]							      = {
									    """num_vec_classes""": 4_0_9_7,
									    """num_train_timesteps""": 1_0_0,
									}

									config.update(**_A						)
									return config







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[int]							      = 4
									UpperCamelCase							:		Optional[int]							      = 8
									UpperCamelCase							:		Any							      = 8

									UpperCamelCase							:		Optional[Any]							      = torch.randint(0	,      _A	,      (batch_size, height * width)						)

									return sample







			@property
			def      _a			(			self						):






									'''simple docstring'''







									assert False







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									def model(_A	,      _A	,      *_A						):
															UpperCamelCase      ,	UpperCamelCase							:		Union[str, Any]							      = sample.shape
															UpperCamelCase							:		int							      = torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
															UpperCamelCase							:		Optional[Any]							      = F.log_softmax(logits.double()	,      dim=1						).float()
															return return_value

									return model







			def      _a			(			self						):






									'''simple docstring'''







									for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_A						)







			def      _a			(			self						):






									'''simple docstring'''







									for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
															self.check_over_configs(num_vec_classes=_A						)







			def      _a			(			self						):






									'''simple docstring'''







									for t in [0, 5_0, 9_9]:
															self.check_over_forward(time_step=_A						)







			def      _a			(			self						):






									'''simple docstring'''







									pass










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__					:				str	= {"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				Optional[int]	= ["""LevitFeatureExtractor"""]
		__magic_name__					:				Any	= ["""LevitImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				List[Any]	= [
		    """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LevitForImageClassification""",
		    """LevitForImageClassificationWithTeacher""",
		    """LevitModel""",
		    """LevitPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_levit import LevitFeatureExtractor
				from .image_processing_levit import LevitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_levit import (
				    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LevitForImageClassification,
				    LevitForImageClassificationWithTeacher,
				    LevitModel,
				    LevitPreTrainedModel,
				)
else:
		import sys

		__magic_name__					:				Tuple	= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



