






import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						(VQDiffusionScheduler,)







      def    __lowerCAmelCase      (   self   :		Union[str, Any]				,      **__UpperCAmelCase   :		Dict						)					->Tuple:




       """simple docstring"""


       a							=   {
           '''num_vec_classes''': 4_097,
           '''num_train_timesteps''': 100,
       }

       config.update(**__UpperCAmelCase						)
       return config







      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		int						)					->Optional[Any]:




       """simple docstring"""


       a							=   4
       a							=   8
       a							=   8

       a							=   torch.randint(0				,      __UpperCAmelCase				,      (batch_size, height * width)						)

       return sample







      @property
      def    __lowerCAmelCase      (   self   :		int						)					->Optional[int]:




       """simple docstring"""


       assert False







      def    __lowerCAmelCase      (   self   :		Optional[int]				,      __UpperCAmelCase   :		Any						)					->List[Any]:




       """simple docstring"""


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

       return model







      def    __lowerCAmelCase      (   self   :		Tuple						)					->str:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->Optional[Any]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		str						)					->Optional[int]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Optional[Any]						)					->Tuple:




       """simple docstring"""


       pass










from collections import namedtuple

UpperCAmelCase__            =	namedtuple("from_to", "from_ to")

UpperCAmelCase__            =	{
    "atm": from_to(1, 1),
    "pascal": from_to(0.000_0098, 101325),
    "bar": from_to(0.98_6923, 1.0_1325),
    "kilopascal": from_to(0.0098_6923, 101.325),
    "megapascal": from_to(9.8_6923, 0.10_1325),
    "psi": from_to(0.06_8046, 14.6959),
    "inHg": from_to(0.033_4211, 29.9213),
    "torr": from_to(0.0013_1579, 760),
}
def 		_a					(   a				:float			,							a				:str			,							a				:str				)							->    float:
 if from_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
      + ''', '''.join(a				)				)
 if to_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
      + ''', '''.join(a				)				)
 return (
     value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
 )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


