

"""simple docstring"""



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=UpperCAmelCase__ )
class     a  (				UpperCAmelCase__ ):



    """simple docstring"""



    UpperCAmelCase         =    field(default="audio-classification",		metadata={"include_in_asdict_even_if_is_default": True} )
    UpperCAmelCase         =    Features({"audio": Audio()} )
    UpperCAmelCase         =    Features({"labels": ClassLabel} )
    UpperCAmelCase         =    "audio"
    UpperCAmelCase         =    "labels"

    def  UpperCamelCase		(   self:       int			,  UpperCamelCase:       Optional[int]				):






         """simple docstring"""


         if self.label_column not in features:
              raise ValueError(f"""Column {self.label_column} is not present in features."""				)
         if not isinstance(features[self.label_column]			,  snake_case_				):
              raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""				)
         A__								=     copy.deepcopy(self				)
         A__								=     self.label_schema.copy()
         A__								=     features[self.label_column]
         A__								=     label_schema
         return task_template





    @property
    def  UpperCamelCase		(   self:       Tuple				):






         """simple docstring"""


         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }













"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result