
'''simple docstring'''



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				):

												"""simple docstring"""



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




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)
												_lowerCAmelCase  						=			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""")   ,       )
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_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(42				)
												_lowerCAmelCase  						=			pipe(generator=lowerCamelCase_   ,       steps=4				)
												_lowerCAmelCase  						=			output.audios[0]
												_lowerCAmelCase  						=			output.images[0]

												_lowerCAmelCase  						=			torch.Generator(device=lowerCamelCase_				).manual_seed(42				)
												_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"""				)[:10]
												_lowerCAmelCase  						=			np.frombuffer(image_from_tuple.tobytes()   ,       dtype="""uint8"""				)[:10]
												_lowerCAmelCase  						=			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

												_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(42				)
												_lowerCAmelCase  						=			pipe(raw_audio=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       start_step=5   ,       steps=10				)
												_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"""				)[:10]
												_lowerCAmelCase  						=			np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]				)

												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, 10)				)
												_lowerCAmelCase  						=			pipe(generator=lowerCamelCase_   ,       encoding=lowerCamelCase_				)
												_lowerCAmelCase  						=			output.images[0]
												_lowerCAmelCase  						=			np.frombuffer(image.tobytes()   ,       dtype="""uint8"""				)[:10]
												_lowerCAmelCase  						=			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   UpperCAmelCase_			(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_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(42				)
												_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"""				)[:10]
												_lowerCAmelCase  						=			np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]				)

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


'''simple docstring'''



import heapq as hq
import math
from collections.abc import Iterator




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			str(id_				)
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			{}  # {vertex:distance}




						def __lt__( self   ,       _lowercase				):

												"""simple docstring"""



												return self.key < other.key




						def __repr__( self				):

												"""simple docstring"""



												return self.id




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												self.neighbors.append(_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			weight






def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:Optional[int]		):
						# add the neighbors:
						graph[a - 1].add_neighbor(graph[b - 1]		)
						graph[b - 1].add_neighbor(graph[a - 1]		)
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]			,     __lowerCamelCase		)
						graph[b - 1].add_edge(graph[a - 1]			,     __lowerCamelCase		)


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:Vertex		):
						_lowerCAmelCase  						=			[]
						for u in graph:
												_lowerCAmelCase  						=			math.inf
												_lowerCAmelCase  						=			None
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			graph[:]
						while q:
												_lowerCAmelCase  						=			min(__lowerCamelCase		)
												q.remove(__lowerCamelCase		)
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								_lowerCAmelCase  						=			u
																								_lowerCAmelCase  						=			u.edges[v.id]
						for i in range(1			,     len(__lowerCamelCase		)		):
												a.append((int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)		)
						return a


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:Vertex		):
						for u in graph:
												_lowerCAmelCase  						=			math.inf
												_lowerCAmelCase  						=			None
						_lowerCAmelCase  						=			0

						_lowerCAmelCase  						=			list(__lowerCamelCase		)
						hq.heapify(__lowerCamelCase		)

						while h:
												_lowerCAmelCase  						=			hq.heappop(__lowerCamelCase		)
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								_lowerCAmelCase  						=			u
																								_lowerCAmelCase  						=			u.edges[v.id]
																								hq.heapify(__lowerCamelCase		)

						for i in range(1			,     len(__lowerCamelCase		)		):
												yield (int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)


def A				():
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()
