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

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







@dataclass(frozen=A__	)
class      A__							(     A__	):
				lowercase			    =  field(default='automatic-speech-recognition'						,   metadata={'include_in_asdict_even_if_is_default': True}	)
				lowercase			    =  Features({'audio': Audio()}	)
				lowercase			    =  Features({'transcription': Value('string'	)}	)
				lowercase			    =  "audio"
				lowercase			    =  "transcription"






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[Any]   ):






							'''simple docstring'''







							if self.audio_column not in features:
										raise ValueError(f'''Column {self.audio_column} is not present in features.'''   )
							if not isinstance(features[self.audio_column]    ,     a   ):
										raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''   )
							lowerCAmelCase__      :      Union[str, Any]         =		copy.deepcopy(self   )
							lowerCAmelCase__      :      Union[str, Any]         =		self.input_schema.copy()
							lowerCAmelCase__      :      Tuple         =		features[self.audio_column]
							lowerCAmelCase__      :      Optional[Any]         =		input_schema
							return task_template






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return {self.audio_column: "audio", self.transcription_column: "transcription"}







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







@slow
@require_torch_gpu
class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2