



"""simple docstring"""

from typing import TYPE_CHECKING

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


A_	:						str				=			{
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_	:						Tuple				=			[
							    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'NystromformerForMaskedLM',
							    'NystromformerForMultipleChoice',
							    'NystromformerForQuestionAnswering',
							    'NystromformerForSequenceClassification',
							    'NystromformerForTokenClassification',
							    'NystromformerLayer',
							    'NystromformerModel',
							    'NystromformerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nystromformer import (
														    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NystromformerForMaskedLM,
														    NystromformerForMultipleChoice,
														    NystromformerForQuestionAnswering,
														    NystromformerForSequenceClassification,
														    NystromformerForTokenClassification,
														    NystromformerLayer,
														    NystromformerModel,
														    NystromformerPreTrainedModel,
														)


else:
							import sys

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










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


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

					return model





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Dict:





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





