

import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class  UpperCAmelCase__	( UpperCamelCase_		,  unittest.TestCase ):






  """simple docstring"""


  a     =      DDIMPipeline
  a     =      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  a     =      PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  a     =      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  a     =      False






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__			=					UNetaDModel(
           block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=3			,     out_channels=3			,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			,     )
       SCREAMING_SNAKE_CASE__			=					DDIMScheduler()
       SCREAMING_SNAKE_CASE__			=					{"unet": unet, "scheduler": scheduler}
       return components






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Optional[int]=0      )	->							Optional[Any]:
       if str(__A      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__			=					torch.manual_seed(__A      )
       else:
            SCREAMING_SNAKE_CASE__			=					torch.Generator(device=__A      ).manual_seed(__A      )
       SCREAMING_SNAKE_CASE__			=					{
           "batch_size": 1,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }
       return inputs






  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__			=					"cpu"

       SCREAMING_SNAKE_CASE__			=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__			=					self.pipeline_class(**__A      )
       pipe.to(__A      )
       pipe.set_progress_bar_config(disable=__A      )

       SCREAMING_SNAKE_CASE__			=					self.get_dummy_inputs(__A      )
       SCREAMING_SNAKE_CASE__			=					pipe(**__A      ).images
       SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

       self.assertEqual(image.shape			,     (1, 32, 32, 3)      )
       SCREAMING_SNAKE_CASE__			=					np.array(
           [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]      )
       SCREAMING_SNAKE_CASE__			=					np.abs(image_slice.flatten() - expected_slice      ).max()
       self.assertLessEqual(__A			,     1e-3      )






  def    lowercase_	(							self		:	List[str]      )	->							Union[str, Any]:
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Any:
       super().test_save_load_local(expected_max_difference=3e-3      )






  def    lowercase_	(							self		:	int      )	->							List[str]:
       super().test_save_load_optional_components(expected_max_difference=3e-3      )







  def    lowercase_	(							self		:	Dict      )	->							str:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






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






  """simple docstring"""


  def    lowercase_	(							self		:	int      )	->							Tuple:
       SCREAMING_SNAKE_CASE__			=					"google/ddpm-cifar10-32"

       SCREAMING_SNAKE_CASE__			=					UNetaDModel.from_pretrained(__A      )
       SCREAMING_SNAKE_CASE__			=					DDIMScheduler()

       SCREAMING_SNAKE_CASE__			=					DDIMPipeline(unet=__A			,     scheduler=__A      )
       ddim.to(__A      )
       ddim.set_progress_bar_config(disable=__A      )

       SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__			=					ddim(generator=__A			,     eta=0.0			,     output_type='''numpy'''      ).images

       SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__			=					np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]      )

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







  def    lowercase_	(							self		:	List[str]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__			=					"google/ddpm-ema-bedroom-256"

       SCREAMING_SNAKE_CASE__			=					UNetaDModel.from_pretrained(__A      )
       SCREAMING_SNAKE_CASE__			=					DDIMScheduler.from_pretrained(__A      )

       SCREAMING_SNAKE_CASE__			=					DDIMPipeline(unet=__A			,     scheduler=__A      )
       ddpm.to(__A      )
       ddpm.set_progress_bar_config(disable=__A      )

       SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__			=					ddpm(generator=__A			,     output_type='''numpy'''      ).images

       SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)
       SCREAMING_SNAKE_CASE__			=					np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]      )

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





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    datasets.utils.logging.get_logger(__name__)






class  UpperCAmelCase__	( folder_based_builder.FolderBasedBuilderConfig ):






  """simple docstring"""


  a     =      None
  a     =      None






class  UpperCAmelCase__	( folder_based_builder.FolderBasedBuilder ):






  """simple docstring"""


  a     =      datasets.Audio()
  a     =      "audio"
  a     =      AudioFolderConfig
  a     =      42  # definition at the bottom of the script
  a     =      AudioClassification(audio_column="audio"		,  label_column="label" )



_SCREAMING_SNAKE_CASE  :						str         =    [
    '''.aiff''',
    '''.au''',
    '''.avr''',
    '''.caf''',
    '''.flac''',
    '''.htk''',
    '''.svx''',
    '''.mat4''',
    '''.mat5''',
    '''.mpc2k''',
    '''.ogg''',
    '''.paf''',
    '''.pvf''',
    '''.raw''',
    '''.rf64''',
    '''.sd2''',
    '''.sds''',
    '''.ircam''',
    '''.voc''',
    '''.w64''',
    '''.wav''',
    '''.nist''',
    '''.wavex''',
    '''.wve''',
    '''.xi''',
    '''.mp3''',
    '''.opus''',
]
_SCREAMING_SNAKE_CASE  :						List[Any]         =    AUDIO_EXTENSIONS


