import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowercase									=				False





class 	__lowercase    (			unittest.TestCase       ):




				'''simple docstring'''


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





				@property
				def      A_							(		self    :		List[Any]				):
								return 12





				@property
				def      A_							(		self    :		List[Any]				):
								return 12





				@property
				def      A_							(		self    :		List[str]				):
								return 32





				@property
				def      A_							(		self    :		Optional[int]				):
								torch.manual_seed(0				)
								UpperCamelCase__					=				VQModel(
								    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,							latent_channels=3      ,							num_vq_embeddings=self.num_embed      ,							vq_embed_dim=3      ,							)
								return model





				@property
				def      A_							(		self    :		Optional[int]				):
								UpperCamelCase__					=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
								return tokenizer





				@property
				def      A_							(		self    :		List[Any]				):
								torch.manual_seed(0				)
								UpperCamelCase__					=				CLIPTextConfig(
								    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=self.text_embedder_hidden_size      ,							intermediate_size=37      ,							layer_norm_eps=1E-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1_000      ,							)
								return CLIPTextModel(lowerCAmelCase__				)





				@property
				def      A_							(		self    :		List[Any]				):
								torch.manual_seed(0				)

								UpperCamelCase__					=				12
								UpperCamelCase__					=				12

								UpperCamelCase__					=				{
								    '''attention_bias''': True,
								    '''cross_attention_dim''': 32,
								    '''attention_head_dim''': height * width,
								    '''num_attention_heads''': 1,
								    '''num_vector_embeds''': self.num_embed,
								    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
								    '''norm_num_groups''': 32,
								    '''sample_size''': width,
								    '''activation_fn''': '''geglu-approximate''',
								}

								UpperCamelCase__					=				TransformeraDModel(**lowerCAmelCase__				)
								return model





				def      A_							(		self    :		Any				):
								UpperCamelCase__					=				'''cpu'''

								UpperCamelCase__					=				self.dummy_vqvae
								UpperCamelCase__					=				self.dummy_text_encoder
								UpperCamelCase__					=				self.dummy_tokenizer
								UpperCamelCase__					=				self.dummy_transformer
								UpperCamelCase__					=				VQDiffusionScheduler(self.num_embed				)
								UpperCamelCase__					=				LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase__				)

								UpperCamelCase__					=				VQDiffusionPipeline(
								    vqvae=lowerCAmelCase__      ,							text_encoder=lowerCAmelCase__      ,							tokenizer=lowerCAmelCase__      ,							transformer=lowerCAmelCase__      ,							scheduler=lowerCAmelCase__      ,							learned_classifier_free_sampling_embeddings=lowerCAmelCase__      ,							)
								UpperCamelCase__					=				pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								UpperCamelCase__					=				'''teddy bear playing in the pool'''

								UpperCamelCase__					=				torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
								UpperCamelCase__					=				pipe([prompt]      ,							generator=lowerCAmelCase__      ,							num_inference_steps=2      ,							output_type='''np'''				)
								UpperCamelCase__					=				output.images

								UpperCamelCase__					=				torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
								UpperCamelCase__					=				pipe(
								    [prompt]      ,							generator=lowerCAmelCase__      ,							output_type='''np'''      ,							return_dict=lowerCAmelCase__      ,							num_inference_steps=2				)[0]

								UpperCamelCase__					=				image[0, -3:, -3:, -1]
								UpperCamelCase__					=				image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 24, 24, 3)

								UpperCamelCase__					=				np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2





				def      A_							(		self    :		List[Any]				):
								UpperCamelCase__					=				'''cpu'''

								UpperCamelCase__					=				self.dummy_vqvae
								UpperCamelCase__					=				self.dummy_text_encoder
								UpperCamelCase__					=				self.dummy_tokenizer
								UpperCamelCase__					=				self.dummy_transformer
								UpperCamelCase__					=				VQDiffusionScheduler(self.num_embed				)
								UpperCamelCase__					=				LearnedClassifierFreeSamplingEmbeddings(
								    learnable=lowerCAmelCase__      ,							hidden_size=self.text_embedder_hidden_size      ,							length=tokenizer.model_max_length				)

								UpperCamelCase__					=				VQDiffusionPipeline(
								    vqvae=lowerCAmelCase__      ,							text_encoder=lowerCAmelCase__      ,							tokenizer=lowerCAmelCase__      ,							transformer=lowerCAmelCase__      ,							scheduler=lowerCAmelCase__      ,							learned_classifier_free_sampling_embeddings=lowerCAmelCase__      ,							)
								UpperCamelCase__					=				pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								UpperCamelCase__					=				'''teddy bear playing in the pool'''

								UpperCamelCase__					=				torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
								UpperCamelCase__					=				pipe([prompt]      ,							generator=lowerCAmelCase__      ,							num_inference_steps=2      ,							output_type='''np'''				)
								UpperCamelCase__					=				output.images

								UpperCamelCase__					=				torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
								UpperCamelCase__					=				pipe(
								    [prompt]      ,							generator=lowerCAmelCase__      ,							output_type='''np'''      ,							return_dict=lowerCAmelCase__      ,							num_inference_steps=2				)[0]

								UpperCamelCase__					=				image[0, -3:, -3:, -1]
								UpperCamelCase__					=				image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 24, 24, 3)

								UpperCamelCase__					=				np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2






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




				'''simple docstring'''


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





				def      A_							(		self    :		int				):
								UpperCamelCase__					=				load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''				)

								UpperCamelCase__					=				VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''				)
								UpperCamelCase__					=				pipeline.to(lowerCAmelCase__				)
								pipeline.set_progress_bar_config(disable=lowerCAmelCase__				)

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								UpperCamelCase__					=				torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
								UpperCamelCase__					=				pipeline(
								    '''teddy bear playing in the pool'''      ,							num_images_per_prompt=1      ,							generator=lowerCAmelCase__      ,							output_type='''np'''      ,							)

								UpperCamelCase__					=				output.images[0]

								assert image.shape == (256, 256, 3)
								assert np.abs(expected_image - image				).max() < 2.0





import numpy as np





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[Any]     )  ->Any:
										lowercase         =      (0, 0)
										lowercase         =      None
										lowercase         =      0
										lowercase         =      0
										lowercase         =      0





						def __eq__(			self							:Optional[int]		,    lowerCAmelCase__							:Any     )  ->Optional[int]:
										return self.position == cell.position





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[Any]:
										print(self.position     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Optional[Any]		,    lowerCAmelCase__							:Tuple=(5, 5)     )  ->str:
										lowercase         =      np.zeros(lowerCAmelCase__     )
										lowercase         =      world_size[0]
										lowercase         =      world_size[1]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										print(self.w     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Optional[Any]     )  ->Optional[Any]:
										lowercase         =      [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase         =      cell.position[0]
										lowercase         =      cell.position[1]
										lowercase         =      []
										for n in neughbour_cord:
														lowercase         =      current_x + n[0]
														lowercase         =      current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase         =      Cell()
																		lowercase         =      (x, y)
																		lowercase         =      cell
																		neighbours.append(lowerCAmelCase__     )
										return neighbours




def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      []
				lowercase         =      []
				_open.append(__magic_name__						)

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

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


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