



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 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				str:

    """simple docstring"""







    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

   @property
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  Optional[Any]					=					UNetaDModel(
        sample_size=(32, 64)				,		in_channels=1				,		out_channels=1				,		layers_per_block=2				,		block_out_channels=(128, 128)				,		down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")				,		up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")				,		)
    return model

   @property
   def      UpperCamelCase_  (					self							:			Tuple)				->				List[Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  Optional[int]					=					UNetaDConditionModel(
        sample_size=(64, 32)				,		in_channels=1				,		out_channels=1				,		layers_per_block=2				,		block_out_channels=(128, 128)				,		down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")				,		up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")				,		cross_attention_dim=10				,		)
    return model

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  Any					=					AutoencoderKL(
        sample_size=(128, 64)				,		in_channels=1				,		out_channels=1				,		latent_channels=1				,		layers_per_block=2				,		block_out_channels=(128, 128)				,		down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")				,		up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")				,		)
    _snake_case    :  Dict					=					UNetaDModel(
        sample_size=(64, 32)				,		in_channels=1				,		out_channels=1				,		layers_per_block=2				,		block_out_channels=(128, 128)				,		down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")				,		up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")				,		)
    return vqvae, unet


   @slow
   def      UpperCamelCase_  (					self							:			int)				->				Tuple:

    """simple docstring"""







    _snake_case    :  List[Any]					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    _snake_case    :  List[Any]					=					Mel(
        x_res=self.dummy_unet.config.sample_size[1]				,		y_res=self.dummy_unet.config.sample_size[0]				,		)

    _snake_case    :  str					=					DDPMScheduler()
    _snake_case    :  str					=					AudioDiffusionPipeline(vqvae=lowerCAmelCase				,		unet=self.dummy_unet				,		mel=lowerCAmelCase				,		scheduler=lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Any					=					torch.Generator(device=lowerCAmelCase).manual_seed(42)
    _snake_case    :  Any					=					pipe(generator=lowerCAmelCase				,		steps=4)
    _snake_case    :  Any					=					output.audios[0]
    _snake_case    :  Any					=					output.images[0]

    _snake_case    :  str					=					torch.Generator(device=lowerCAmelCase).manual_seed(42)
    _snake_case    :  List[str]					=					pipe(generator=lowerCAmelCase				,		steps=4				,		return_dict=lowerCAmelCase)
    _snake_case    :  List[str]					=					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]
    )
    _snake_case    :  List[str]					=					np.frombuffer(image.tobytes()				,		dtype="""uint8""")[:10]
    _snake_case    :  Any					=					np.frombuffer(image_from_tuple.tobytes()				,		dtype="""uint8""")[:10]
    _snake_case    :  Dict					=					np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127])

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

    _snake_case    :  str					=					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]				,		)

    _snake_case    :  Union[str, Any]					=					DDIMScheduler()
    _snake_case    :  Dict					=					self.dummy_vqvae_and_unet
    _snake_case    :  Dict					=					AudioDiffusionPipeline(
        vqvae=self.dummy_vqvae_and_unet[0]				,		unet=dummy_vqvae_and_unet[1]				,		mel=lowerCAmelCase				,		scheduler=lowerCAmelCase)
    _snake_case    :  Tuple					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    np.random.seed(0)
    _snake_case    :  List[Any]					=					np.random.uniform(-1				,		1				,		((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,))
    _snake_case    :  Any					=					torch.Generator(device=lowerCAmelCase).manual_seed(42)
    _snake_case    :  Optional[int]					=					pipe(raw_audio=lowerCAmelCase				,		generator=lowerCAmelCase				,		start_step=5				,		steps=10)
    _snake_case    :  Dict					=					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]
    )
    _snake_case    :  Tuple					=					np.frombuffer(image.tobytes()				,		dtype="""uint8""")[:10]
    _snake_case    :  int					=					np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121])

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

    _snake_case    :  Tuple					=					self.dummy_unet_condition
    _snake_case    :  List[str]					=					AudioDiffusionPipeline(
        vqvae=self.dummy_vqvae_and_unet[0]				,		unet=lowerCAmelCase				,		mel=lowerCAmelCase				,		scheduler=lowerCAmelCase)
    _snake_case    :  Tuple					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    np.random.seed(0)
    _snake_case    :  List[Any]					=					torch.rand((1, 1, 10))
    _snake_case    :  str					=					pipe(generator=lowerCAmelCase				,		encoding=lowerCAmelCase)
    _snake_case    :  Dict					=					output.images[0]
    _snake_case    :  str					=					np.frombuffer(image.tobytes()				,		dtype="""uint8""")[:10]
    _snake_case    :  List[Any]					=					np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111])

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







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



   '''simple docstring'''






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

    """simple docstring"""







    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


   def      UpperCamelCase_  (					self							:			str)				->				Dict:

    """simple docstring"""







    _snake_case    :  str					=					torch_device

    _snake_case    :  Optional[int]					=					DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256""")
    _snake_case    :  Dict					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  List[str]					=					torch.Generator(device=lowerCAmelCase).manual_seed(42)
    _snake_case    :  Optional[Any]					=					pipe(generator=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					output.audios[0]
    _snake_case    :  int					=					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]
    _snake_case    :  Optional[int]					=					np.frombuffer(image.tobytes()				,		dtype="""uint8""")[:10]
    _snake_case    :  Dict					=					np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26])

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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
