

"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_UpperCamelCase		    =						logging.get_logger(__name__)






class  __a				(	snake_case__					):





							"""simple docstring"""


							__UpperCamelCase				:			str          =	'speech-encoder-decoder'
							__UpperCamelCase				:			List[str]          =	True






							def __init__(      self ,				**snake_case      ):





								"""simple docstring"""

								super().__init__(**__snake_case      )
								if "encoder" not in kwargs or "decoder" not in kwargs:
									raise ValueError(
									    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
									    F""" `decoder` sub-configurations are passed, but only {kwargs}"""      )

								lowerCAmelCase__      :							Optional[int]    =							kwargs.pop("encoder"      )
								lowerCAmelCase__      :							Optional[Any]    =							encoder_config.pop("model_type"      )
								lowerCAmelCase__      :							List[str]    =							kwargs.pop("decoder"      )
								lowerCAmelCase__      :							int    =							decoder_config.pop("model_type"      )

								lowerCAmelCase__      :							List[str]    =							AutoConfig.for_model(__snake_case ,				**__snake_case      )
								lowerCAmelCase__      :							List[str]    =							AutoConfig.for_model(__snake_case ,				**__snake_case      )
								lowerCAmelCase__      :							List[str]    =							True






							@classmethod
							def  SCREAMING_SNAKE_CASE_  (      cls ,				snake_case ,				snake_case ,				**snake_case      ):





								"""simple docstring"""

								logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"      )
								lowerCAmelCase__      :							Dict    =							True
								lowerCAmelCase__      :							Tuple    =							True

								return cls(encoder=encoder_config.to_dict() ,				decoder=decoder_config.to_dict() ,				**__snake_case      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							str    =							copy.deepcopy(self.__dict__      )
								lowerCAmelCase__      :							List[str]    =							self.encoder.to_dict()
								lowerCAmelCase__      :							Union[str, Any]    =							self.decoder.to_dict()
								lowerCAmelCase__      :							int    =							self.__class__.model_type
								return output









"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Optional[int]          =	(VQDiffusionScheduler,)






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							{
            "num_vec_classes": 4_097,
            "num_train_timesteps": 100,
        }

        config.update(**snake_case      )
        return config






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							4
        lowerCAmelCase__      :							Optional[Any]    =							8
        lowerCAmelCase__      :							Union[str, Any]    =							8

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

        return sample






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        assert False






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        def model(snake_case ,				snake_case ,				*snake_case      ):
         lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							sample.shape
         lowerCAmelCase__      :							Dict    =							torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
         lowerCAmelCase__      :							int    =							F.log_softmax(logits.double() ,				dim=1      ).float()
         return return_value

        return model






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






