





"""simple docstring"""




import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

_snake_case  :   int						   =  yaml.safe_load(
    '\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: "Dataset Card for X" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: "Table of Contents"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: "Dataset Description"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: "Dataset Summary"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: "Supported Tasks and Leaderboards"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n'
)


_snake_case  :   Optional[Any]						   =  {
    'name': 'root',
    'text': '',
    'is_empty_text': True,
    'subsections': [
        {
            'name': 'Dataset Card for My Dataset',
            'text': '',
            'is_empty_text': True,
            'subsections': [
                {'name': 'Table of Contents', 'text': 'Some text here.', 'is_empty_text': False, 'subsections': []},
                {
                    'name': 'Dataset Description',
                    'text': 'Some text here.',
                    'is_empty_text': False,
                    'subsections': [
                        {
                            'name': 'Dataset Summary',
                            'text': 'Some text here.',
                            'is_empty_text': False,
                            'subsections': [],
                        },
                        {
                            'name': 'Supported Tasks and Leaderboards',
                            'text': '',
                            'is_empty_text': True,
                            'subsections': [],
                        },
                        {'name': 'Languages', 'text': 'Language Text', 'is_empty_text': False, 'subsections': []},
                    ],
                },
            ],
        }
    ],
}


_snake_case  :   Optional[Any]						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'


_snake_case  :   Any						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

_snake_case  :   str						   =  {
    'name': 'root',
    'text': '',
    'is_empty_text': True,
    'subsections': [
        {
            'name': 'Dataset Card for My Dataset',
            'text': '',
            'is_empty_text': True,
            'subsections': [
                {'name': 'Table of Contents', 'text': 'Some text here.', 'is_empty_text': False, 'subsections': []},
                {
                    'name': 'Dataset Description',
                    'text': 'Some text here.',
                    'is_empty_text': False,
                    'subsections': [
                        {
                            'name': 'Dataset Summary',
                            'text': 'Some text here.',
                            'is_empty_text': False,
                            'subsections': [
                                {
                                    'name': 'Extra Ignored Subsection',
                                    'text': '',
                                    'is_empty_text': True,
                                    'subsections': [],
                                }
                            ],
                        },
                        {
                            'name': 'Supported Tasks and Leaderboards',
                            'text': '',
                            'is_empty_text': True,
                            'subsections': [],
                        },
                        {'name': 'Languages', 'text': 'Language Text', 'is_empty_text': False, 'subsections': []},
                    ],
                },
            ],
        }
    ],
}

_snake_case  :   List[Any]						   =  '\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

_snake_case  :   List[str]						   =  (
    'The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'
)

_snake_case  :   Union[str, Any]						   =  '\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

_snake_case  :   Union[str, Any]						   =  (
    'The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'
)

_snake_case  :   str						   =  '\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

_snake_case  :   Dict						   =  'The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'

_snake_case  :   Union[str, Any]						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'
_snake_case  :   Any						   =  'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'


_snake_case  :   str						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n'
_snake_case  :   Optional[int]						   =  'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'

_snake_case  :   List[Any]						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n'

_snake_case  :   List[str]						   =  'The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'


_snake_case  :   List[str]						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n'

_snake_case  :   Optional[int]						   =  'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'

_snake_case  :   List[Any]						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'
_snake_case  :   List[str]						   =  'The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'

_snake_case  :   str						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n'

_snake_case  :   Optional[Any]						   =  'The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'

_snake_case  :   Tuple						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

_snake_case  :   Dict						   =  'The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'

_snake_case  :   List[str]						   =  ''

_snake_case  :   List[str]						   =  'The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'

_snake_case  :   Optional[Any]						   =  '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

_snake_case  :   Optional[Any]						   =  'The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'

@pytest.mark.parametrize(
    'readme_md, expected_dict',     [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ],     )
def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Dict     ):
   assert ReadMe.from_string(lowerCAmelCase_,     lowerCAmelCase_     ).to_dict() == expected_dict

@pytest.mark.parametrize(
    'readme_md, expected_error',     [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ],     )
def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      List[str]     ):
   with pytest.raises(lowerCAmelCase_,     match=re.escape(expected_error.format(path='root'     )     )     ):
      __lowerCAmelCase							     =     ReadMe.from_string(lowerCAmelCase_,     lowerCAmelCase_     )
      readme.validate()

@pytest.mark.parametrize(
    'readme_md, expected_error',     [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ],     )
def   a_   (			lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      int     ):
   with pytest.raises(lowerCAmelCase_,     match=re.escape(expected_error.format(path='root'     )     )     ):
      ReadMe.from_string(lowerCAmelCase_,     lowerCAmelCase_     )

@pytest.mark.parametrize(
    'readme_md,',     [
        (README_MULTIPLE_SAME_HEADING_1),
    ],     )
def   a_   (			lowerCAmelCase_				:      List[Any]     ):
   ReadMe.from_string(lowerCAmelCase_,     lowerCAmelCase_,     suppress_parsing_errors=lowerCAmelCase_     )

@pytest.mark.parametrize(
    'readme_md, expected_dict',     [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ],     )
def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      int     ):
   with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCAmelCase							     =     Path(lowerCAmelCase_     ) / 'README.md'
      with open(lowerCAmelCase_,     'w+'     ) as readme_file:
         readme_file.write(lowerCAmelCase_     )
      __lowerCAmelCase							     =     ReadMe.from_readme(lowerCAmelCase_,     lowerCAmelCase_     ).to_dict()
      assert out["name"] == path
      assert out["text"] == ""
      assert out["is_empty_text"]
      assert out["subsections"] == expected_dict["subsections"]

@pytest.mark.parametrize(
    'readme_md, expected_error',     [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ],     )
def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Optional[int]     ):
   with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCAmelCase							     =     Path(lowerCAmelCase_     ) / 'README.md'
      with open(lowerCAmelCase_,     'w+'     ) as readme_file:
         readme_file.write(lowerCAmelCase_     )
      __lowerCAmelCase							     =     expected_error.format(path=lowerCAmelCase_     )
      with pytest.raises(lowerCAmelCase_,     match=re.escape(lowerCAmelCase_     )     ):
         __lowerCAmelCase							     =     ReadMe.from_readme(lowerCAmelCase_,     lowerCAmelCase_     )
         readme.validate()

@pytest.mark.parametrize(
    'readme_md, expected_error',     [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ],     )
def   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Any     ):
   with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCAmelCase							     =     Path(lowerCAmelCase_     ) / 'README.md'
      with open(lowerCAmelCase_,     'w+'     ) as readme_file:
         readme_file.write(lowerCAmelCase_     )
      __lowerCAmelCase							     =     expected_error.format(path=lowerCAmelCase_     )
      with pytest.raises(lowerCAmelCase_,     match=re.escape(lowerCAmelCase_     )     ):
         ReadMe.from_readme(lowerCAmelCase_,     lowerCAmelCase_     )




@pytest.mark.parametrize(
    'readme_md,',     [
        (README_MULTIPLE_SAME_HEADING_1),
    ],     )
def   a_   (			lowerCAmelCase_				:      int     ):
   with tempfile.TemporaryDirectory() as tmp_dir:
      __lowerCAmelCase							     =     Path(lowerCAmelCase_     ) / 'README.md'
      with open(lowerCAmelCase_,     'w+'     ) as readme_file:
         readme_file.write(lowerCAmelCase_     )
      ReadMe.from_readme(lowerCAmelCase_,     lowerCAmelCase_,     suppress_parsing_errors=lowerCAmelCase_     )








import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[str]					)							->						int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(3_2, 6_4) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return model






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDConditionModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,  cross_attention_dim=1_0 ,  )
     return model






  @property
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     AutoencoderKL(
         sample_size=(1_2_8, 6_4) ,  in_channels=1 ,  out_channels=1 ,  latent_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,  up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,  )
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return vqvae, unet






  @slow
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_unet.config.sample_size[1] ,  y_res=self.dummy_unet.config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDPMScheduler()
     __lowerCAmelCase							     =     AudioDiffusionPipeline(vqvae=lowerCAmelCase_ ,  unet=self.dummy_unet ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4 ,  return_dict=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output[0][0]

     assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
     assert (
         image.height == self.dummy_unet.config.sample_size[0]
         and image.width == self.dummy_unet.config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.frombuffer(image_from_tuple.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDIMScheduler()
     __lowerCAmelCase							     =     self.dummy_vqvae_and_unet
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=dummy_vqvae_and_unet[1] ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     np.random.uniform(-1 ,  1 ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(raw_audio=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  start_step=5 ,  steps=1_0					)
     __lowerCAmelCase							     =     output.images[0]

     assert (
         image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
         and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     self.dummy_unet_condition
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=lowerCAmelCase_ ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     torch.rand((1, 1, 1_0)					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  encoding=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.images[0]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





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

  """simple docstring"""


  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
     assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

