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 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        torch.manual_seed(0							)
        _A        =  UNetaDModel(
            sample_size=(32, 64)		,				in_channels=1		,				out_channels=1		,				layers_per_block=2		,				block_out_channels=(1_28, 1_28)		,				down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")		,				up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")		,				)
        return model



 @property
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        torch.manual_seed(0							)
        _A        =  UNetaDConditionModel(
            sample_size=(64, 32)		,				in_channels=1		,				out_channels=1		,				layers_per_block=2		,				block_out_channels=(1_28, 1_28)		,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")		,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")		,				cross_attention_dim=10		,				)
        return model



 @property
 def      UpperCAmelCase							(					self							)  ->					Dict:
        torch.manual_seed(0							)
        _A        =  AutoencoderKL(
            sample_size=(1_28, 64)		,				in_channels=1		,				out_channels=1		,				latent_channels=1		,				layers_per_block=2		,				block_out_channels=(1_28, 1_28)		,				down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")		,				up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")		,				)
        _A        =  UNetaDModel(
            sample_size=(64, 32)		,				in_channels=1		,				out_channels=1		,				layers_per_block=2		,				block_out_channels=(1_28, 1_28)		,				down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")		,				up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")		,				)
        return vqvae, unet



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _A        =  Mel(
            x_res=self.dummy_unet.config.sample_size[1]		,				y_res=self.dummy_unet.config.sample_size[0]		,				)

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

        _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(42							)
        _A        =  pipe(generator=lowerCAmelCase_		,				steps=4							)
        _A        =  output.audios[0]
        _A        =  output.images[0]

        _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(42							)
        _A        =  pipe(generator=lowerCAmelCase_		,				steps=4		,				return_dict=lowerCAmelCase_							)
        _A        =  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]
        )
        _A        =  np.frombuffer(image.tobytes()		,				dtype="""uint8"""							)[:10]
        _A        =  np.frombuffer(image_from_tuple.tobytes()		,				dtype="""uint8"""							)[:10]
        _A        =  np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]							)

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

        _A        =  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]		,				)

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

        np.random.seed(0							)
        _A        =  np.random.uniform(-1		,				1		,				((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)							)
        _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(42							)
        _A        =  pipe(raw_audio=lowerCAmelCase_		,				generator=lowerCAmelCase_		,				start_step=5		,				steps=10							)
        _A        =  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]
        )
        _A        =  np.frombuffer(image.tobytes()		,				dtype="""uint8"""							)[:10]
        _A        =  np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]							)

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

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

        np.random.seed(0							)
        _A        =  torch.rand((1, 1, 10)							)
        _A        =  pipe(generator=lowerCAmelCase_		,				encoding=lowerCAmelCase_							)
        _A        =  output.images[0]
        _A        =  np.frombuffer(image.tobytes()		,				dtype="""uint8"""							)[:10]
        _A        =  np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]							)

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



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







 """simple docstring"""






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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  torch_device

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

        _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(42							)
        _A        =  pipe(generator=lowerCAmelCase_							)
        _A        =  output.audios[0]
        _A        =  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]
        _A        =  np.frombuffer(image.tobytes()		,				dtype="""uint8"""							)[:10]
        _A        =  np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]							)

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





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




