
"""simple docstring"""







import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Dict   			=      (VQDiffusionScheduler,)







       def  _snake_case      (       self				,**lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       {
              "num_vec_classes": 4097,
              "num_train_timesteps": 100,
          }

          config.update(**lowerCamelCase_		)
          return config







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       4
          _UpperCAmelCase 						=       8
          _UpperCAmelCase 						=       8

          _UpperCAmelCase 						=       torch.randint(0				,lowerCamelCase_				,(batch_size, height * width)		)

          return sample







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          assert False







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          def model(lowerCamelCase_				,lowerCamelCase_				,*lowerCamelCase_		):
             _UpperCAmelCase						,				_UpperCAmelCase 						=       sample.shape
             _UpperCAmelCase 						=       torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)		)
             _UpperCAmelCase 						=       F.log_softmax(logits.double()				,dim=1		).float()
             return return_value

          return model







       def  _snake_case      (       self		):



          '''simple docstring'''

          for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

          for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          pass



"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

