



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 ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
		@property
		def 						SCREAMING_SNAKE_CASE_					(							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 						SCREAMING_SNAKE_CASE_					(							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 						SCREAMING_SNAKE_CASE_					(							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 						SCREAMING_SNAKE_CASE_					(							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=a_    ,unet=self.dummy_unet    ,mel=a_    ,scheduler=a_						)
						lowerCAmelCase__		  =  pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

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

						lowerCAmelCase__		  =  torch.Generator(device=a_						).manual_seed(42						)
						lowerCAmelCase__		  =  pipe(generator=a_    ,steps=4    ,return_dict=a_						)
						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=a_    ,scheduler=a_						)
						lowerCAmelCase__		  =  pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						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=a_						).manual_seed(42						)
						lowerCAmelCase__		  =  pipe(raw_audio=a_    ,generator=a_    ,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=a_    ,mel=a_    ,scheduler=a_						)
						lowerCAmelCase__		  =  pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						np.random.seed(0						)
						lowerCAmelCase__		  =  torch.rand((1, 1, 10)						)
						lowerCAmelCase__		  =  pipe(generator=a_    ,encoding=a_						)
						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 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  torch_device

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

						lowerCAmelCase__		  =  torch.Generator(device=a_						).manual_seed(42						)
						lowerCAmelCase__		  =  pipe(generator=a_						)
						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





import numpy as np


class 	__snake_case   :
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  (0, 0)
						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
		def __eq__(							self    ,a_						):







						"""simple docstring"""
						return self.position == cell.position



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.position						)



class 	__snake_case   :
		def __init__(							self    ,a_=(5, 5)						):







						"""simple docstring"""
						lowerCAmelCase__		  =  np.zeros(a_						)
						lowerCAmelCase__		  =  world_size[0]
						lowerCAmelCase__		  =  world_size[1]
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.w						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						lowerCAmelCase__		  =  cell.position[0]
						lowerCAmelCase__		  =  cell.position[1]
						lowerCAmelCase__		  =  []
						for n in neughbour_cord:
										lowerCAmelCase__		  =  current_x + n[0]
										lowerCAmelCase__		  =  current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
														lowerCAmelCase__		  =  Cell()
														lowerCAmelCase__		  =  (x, y)
														lowerCAmelCase__		  =  cell
														neighbours.append(a_						)
						return neighbours







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  []
				_open.append(snake_case__					)

				while _open:
								lowerCAmelCase__		  =  np.argmin([n.f for n in _open]					)
								lowerCAmelCase__		  =  _open[min_f]
								_closed.append(_open.pop(snake_case__					)					)
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__					):
												for c in _closed:
																if c == n:
																				continue
												lowerCAmelCase__		  =  current.g + 1
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  n.position
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  goal.position
												lowerCAmelCase__		  =  (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCAmelCase__		  =  n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(snake_case__					)
				lowerCAmelCase__		  =  []
				while current.parent is not None:
								path.append(current.position					)
								lowerCAmelCase__		  =  current.parent
				path.append(current.position					)
				return path[::-1]


if __name__ == "__main__":
	_lowerCAmelCase	:   str    =   Gridworld()
	# Start position and goal
	_lowerCAmelCase	:   List[Any]    =   Cell()
	_lowerCAmelCase	:   List[str]    =   (0, 0)
	_lowerCAmelCase	:   Optional[int]    =   Cell()
	_lowerCAmelCase	:   int    =   (4, 4)
	print(f"""path from {start.position} to {goal.position}""")
	_lowerCAmelCase	:   Optional[int]    =   astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		_lowerCAmelCase	:   int    =   1
	print(world.w)
