

import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Optional[int]										=VQModel
  lowerCamelCase   :      Optional[Any]										="sample"







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Tuple=(32, 32)   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						4
     __lowerCAmelCase  :      str    =						3

     __lowerCAmelCase  :      List[Any]    =						floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )

     return {"sample": image}







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Dict:






     """simple docstring"""







     return (3, 32, 32)







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[Any]:






     """simple docstring"""







     return (3, 32, 32)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						{
         """block_out_channels""": [32, 64],
         """in_channels""": 3,
         """out_channels""": 3,
         """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
         """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
         """latent_channels""": 3,
     }
     __lowerCAmelCase  :      int    =						self.dummy_input
     return init_dict, inputs_dict







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






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						VQModel.from_pretrained("""fusing/vqgan-dummy"""	,							output_loading_info=lowerCAmelCase   )
     self.assertIsNotNone(lowerCAmelCase   )
     self.assertEqual(len(loading_info["""missing_keys"""]   )	,							0   )

     model.to(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						model(**self.dummy_input   )

     assert image is not None, "Make sure output is not None"







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






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						VQModel.from_pretrained("""fusing/vqgan-dummy"""   )
     model.to(lowerCAmelCase   ).eval()

     torch.manual_seed(0   )
     if torch.cuda.is_available():
        torch.cuda.manual_seed_all(0   )

     __lowerCAmelCase  :      int    =						torch.randn(1	,							model.config.in_channels	,							model.config.sample_size	,							model.config.sample_size   )
     __lowerCAmelCase  :      Dict    =						image.to(lowerCAmelCase   )
     with torch.no_grad():
        __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase   ).sample

     __lowerCAmelCase  :      Tuple    =						output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     __lowerCAmelCase  :      Dict    =						torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]   )
     # fmt: on
     self.assertTrue(torch.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )





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

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



@dataclass(frozen=a_			)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(default="audio-classification"       ,							metadata={"include_in_asdict_even_if_is_default": True}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"audio": Audio()}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"labels": ClassLabel}			)
  lowerCamelCase   :      str										="audio"
  lowerCamelCase   :      str										="labels"







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Dict   )							->     Dict:






     """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]	,							lowerCAmelCase   ):
        raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
     __lowerCAmelCase  :      Tuple    =						copy.deepcopy(self   )
     __lowerCAmelCase  :      Tuple    =						self.label_schema.copy()
     __lowerCAmelCase  :      Union[str, Any]    =						features[self.label_column]
     __lowerCAmelCase  :      Any    =						label_schema
     return task_template







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Dict[str, str]:






     """simple docstring"""







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


