






"""simple docstring"""







import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class      UpperCamelCase		(__snake_case						,						__snake_case						,						__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : int												=		StableDiffusionInstructPixaPixPipeline
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_SCREAMING_SNAKE_CASE  : Any												=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def      __snake_case      (		self  :List[Any]				)      ->Optional[int]:
										torch.manual_seed(0				)
										lowercase    :      Any       = UNetaDConditionModel(
										    block_out_channels=(32, 64)			, layers_per_block=2			, sample_size=32			, in_channels=8			, out_channels=4			, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			, cross_attention_dim=32			, )
										lowercase    :      int       = PNDMScheduler(skip_prk_steps=__magic_name__				)
										torch.manual_seed(0				)
										lowercase    :      Optional[Any]       = AutoencoderKL(
										    block_out_channels=[32, 64]			, in_channels=3			, out_channels=3			, down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			, latent_channels=4			, )
										torch.manual_seed(0				)
										lowercase    :      List[str]       = CLIPTextConfig(
										    bos_token_id=0			, eos_token_id=2			, hidden_size=32			, intermediate_size=37			, layer_norm_eps=1E-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1_000			, )
										lowercase    :      List[Any]       = CLIPTextModel(__magic_name__				)
										lowercase    :      Any       = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

										lowercase    :      int       = {
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components






						def      __snake_case      (		self  :Any			, __magic_name__  :List[Any]			, __magic_name__  :Optional[Any]=0				)      ->Any:
										lowercase    :      str       = floats_tensor((1, 3, 32, 32)			, rng=random.Random(__magic_name__				)				).to(__magic_name__				)
										lowercase    :      List[str]       = image.cpu().permute(0			, 2			, 3			, 1				)[0]
										lowercase    :      Optional[Any]       = Image.fromarray(np.uinta(__magic_name__				)				).convert("""RGB"""				)
										if str(__magic_name__				).startswith("""mps"""				):
														lowercase    :      Any       = torch.manual_seed(__magic_name__				)
										else:
														lowercase    :      List[Any]       = torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
										lowercase    :      Tuple       = {
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """image""": image,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """image_guidance_scale""": 1,
										    """output_type""": """numpy""",
										}
										return inputs






						def      __snake_case      (		self  :str				)      ->Dict:
										lowercase    :      Tuple       = """cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowercase    :      str       = self.get_dummy_components()
										lowercase    :      Union[str, Any]       = StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
										lowercase    :      Optional[Any]       = sd_pipe.to(__magic_name__				)
										sd_pipe.set_progress_bar_config(disable=__magic_name__				)

										lowercase    :      Dict       = self.get_dummy_inputs(__magic_name__				)
										lowercase    :      Optional[Any]       = sd_pipe(**__magic_name__				).images
										lowercase    :      Dict       = image[0, -3:, -3:, -1]
										assert image.shape == (1, 32, 32, 3)
										lowercase    :      List[str]       = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[Any]:
										lowercase    :      Any       = """cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowercase    :      str       = self.get_dummy_components()
										lowercase    :      Tuple       = StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
										lowercase    :      Dict       = sd_pipe.to(__magic_name__				)
										sd_pipe.set_progress_bar_config(disable=__magic_name__				)

										lowercase    :      Union[str, Any]       = self.get_dummy_inputs(__magic_name__				)
										lowercase    :      Optional[Any]       = """french fries"""
										lowercase    :      Dict       = sd_pipe(**__magic_name__			, negative_prompt=__magic_name__				)
										lowercase    :      Optional[Any]       = output.images
										lowercase    :      List[Any]       = image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowercase    :      List[str]       = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3






						def      __snake_case      (		self  :Union[str, Any]				)      ->Union[str, Any]:
										lowercase    :      Any       = """cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowercase    :      Dict       = self.get_dummy_components()
										lowercase    :      int       = StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
										lowercase    :      str       = sd_pipe.to(__magic_name__				)
										sd_pipe.set_progress_bar_config(disable=__magic_name__				)

										lowercase    :      List[Any]       = self.get_dummy_inputs(__magic_name__				)
										lowercase    :      List[Any]       = [inputs["""prompt"""]] * 2

										lowercase    :      Optional[int]       = np.array(inputs["""image"""]				).astype(np.floataa				) / 255.0
										lowercase    :      Dict       = torch.from_numpy(__magic_name__				).unsqueeze(0				).to(__magic_name__				)
										lowercase    :      Any       = image / 2 + 0.5
										lowercase    :      Union[str, Any]       = image.permute(0			, 3			, 1			, 2				)
										lowercase    :      Optional[int]       = image.repeat(2			, 1			, 1			, 1				)

										lowercase    :      Tuple       = sd_pipe(**__magic_name__				).images
										lowercase    :      List[Any]       = image[-1, -3:, -3:, -1]

										assert image.shape == (2, 32, 32, 3)
										lowercase    :      Any       = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3






						def      __snake_case      (		self  :str				)      ->List[Any]:
										lowercase    :      str       = """cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowercase    :      Union[str, Any]       = self.get_dummy_components()
										lowercase    :      Dict       = EulerAncestralDiscreteScheduler(
										    beta_start=0.0_0085			, beta_end=0.012			, beta_schedule="""scaled_linear"""				)
										lowercase    :      List[Any]       = StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
										lowercase    :      List[str]       = sd_pipe.to(__magic_name__				)
										sd_pipe.set_progress_bar_config(disable=__magic_name__				)

										lowercase    :      Optional[Any]       = self.get_dummy_inputs(__magic_name__				)
										lowercase    :      Tuple       = sd_pipe(**__magic_name__				).images
										lowercase    :      Union[str, Any]       = image[0, -3:, -3:, -1]

										lowercase    :      Union[str, Any]       = [round(__magic_name__			, 4				) for x in image_slice.flatten().tolist()]
										print(""",""".join([str(__magic_name__				) for x in slice]				)				)

										assert image.shape == (1, 32, 32, 3)
										lowercase    :      int       = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3






						def      __snake_case      (		self  :Optional[int]				)      ->int:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3				)






						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										lowercase    :      int       = self.get_dummy_components()
										lowercase    :      Union[str, Any]       = StableDiffusionInstructPixaPixPipeline(**__magic_name__				)
										lowercase    :      Optional[Any]       = VaeImageProcessor(do_resize=__magic_name__			, do_normalize=__magic_name__				)
										lowercase    :      Optional[Any]       = pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)

										lowercase    :      Dict       = pipe(**self.get_dummy_inputs_by_type(__magic_name__			, input_image_type="""pt"""				)				)[0]

										lowercase    :      Tuple       = components["""vae"""]
										lowercase    :      Any       = self.get_dummy_inputs_by_type(__magic_name__			, input_image_type="""pt"""				)

										for image_param in self.image_latents_params:
														if image_param in inputs.keys():
																		lowercase    :      Union[str, Any]       = vae.encode(inputs[image_param]				).latent_dist.mode()

										lowercase    :      Dict       = pipe(**__magic_name__				)[0]

										lowercase    :      Optional[int]       = np.abs(out - out_latents_inputs				).max()
										self.assertLess(__magic_name__			, 1E-4			, """passing latents as image input generate different result from passing image"""				)


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






						def      __snake_case      (		self  :Dict				)      ->List[str]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






						def      __snake_case      (		self  :Tuple			, __magic_name__  :List[str]=0				)      ->Union[str, Any]:
										lowercase    :      Dict       = torch.manual_seed(__magic_name__				)
										lowercase    :      Tuple       = load_image(
										    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""				)
										lowercase    :      int       = {
										    """prompt""": """turn him into a cyborg""",
										    """image""": image,
										    """generator""": generator,
										    """num_inference_steps""": 3,
										    """guidance_scale""": 7.5,
										    """image_guidance_scale""": 1.0,
										    """output_type""": """numpy""",
										}
										return inputs






						def      __snake_case      (		self  :Optional[Any]				)      ->int:
										lowercase    :      Optional[int]       = StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    """timbrooks/instruct-pix2pix"""			, safety_checker=__magic_name__				)
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

										lowercase    :      Dict       = self.get_inputs()
										lowercase    :      str       = pipe(**__magic_name__				).images
										lowercase    :      Dict       = image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase    :      List[str]       = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]				)

										assert np.abs(expected_slice - image_slice				).max() < 1E-3






						def      __snake_case      (		self  :Optional[int]				)      ->List[Any]:
										lowercase    :      Union[str, Any]       = StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    """timbrooks/instruct-pix2pix"""			, safety_checker=__magic_name__				)
										lowercase    :      str       = LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

										lowercase    :      List[Any]       = self.get_inputs()
										lowercase    :      List[Any]       = pipe(**__magic_name__				).images
										lowercase    :      Tuple       = image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase    :      Tuple       = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]				)

										assert np.abs(expected_slice - image_slice				).max() < 1E-3






						def      __snake_case      (		self  :Union[str, Any]				)      ->Any:
										lowercase    :      List[str]       = StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    """timbrooks/instruct-pix2pix"""			, safety_checker=__magic_name__				)
										lowercase    :      Tuple       = DDIMScheduler.from_config(pipe.scheduler.config				)
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

										lowercase    :      int       = self.get_inputs()
										lowercase    :      List[str]       = pipe(**__magic_name__				).images
										lowercase    :      Dict       = image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase    :      Optional[Any]       = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]				)

										assert np.abs(expected_slice - image_slice				).max() < 1E-3






						def      __snake_case      (		self  :Dict				)      ->int:
										lowercase    :      Tuple       = 0

										def callback_fn(__magic_name__  :int			, __magic_name__  :int			, __magic_name__  :torch.FloatTensor				) -> None:
														lowercase    :      Optional[Any]       = True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 1:
																		lowercase    :      str       = latents.detach().cpu().numpy()
																		assert latents.shape == (1, 4, 64, 64)
																		lowercase    :      List[Any]       = latents[0, -3:, -3:, -1]
																		lowercase    :      Optional[Any]       = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]				)

																		assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2
														elif step == 2:
																		lowercase    :      Any       = latents.detach().cpu().numpy()
																		assert latents.shape == (1, 4, 64, 64)
																		lowercase    :      int       = latents[0, -3:, -3:, -1]
																		lowercase    :      str       = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]				)

																		assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2

										lowercase    :      Any       = False

										lowercase    :      List[Any]       = StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    """timbrooks/instruct-pix2pix"""			, safety_checker=__magic_name__			, torch_dtype=torch.floataa				)
										lowercase    :      List[str]       = pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

										lowercase    :      int       = self.get_inputs()
										pipe(**__magic_name__			, callback=__magic_name__			, callback_steps=1				)
										assert callback_fn.has_been_called
										assert number_of_steps == 3






						def      __snake_case      (		self  :List[Any]				)      ->Union[str, Any]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowercase    :      Union[str, Any]       = StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    """timbrooks/instruct-pix2pix"""			, safety_checker=__magic_name__			, torch_dtype=torch.floataa				)
										lowercase    :      str       = pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing(1				)
										pipe.enable_sequential_cpu_offload()

										lowercase    :      Optional[Any]       = self.get_inputs()
										lowercase    :      Optional[Any]       = pipe(**__magic_name__				)

										lowercase    :      int       = torch.cuda.max_memory_allocated()
										# make sure that less than 2.2 GB is allocated
										assert mem_bytes < 2.2 * 10**9






						def      __snake_case      (		self  :Optional[int]				)      ->Optional[int]:
										lowercase    :      List[str]       = self.get_inputs()
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowercase    :      Optional[Any]       = inputs["""image"""].resize((504, 504)				)

										lowercase    :      List[str]       = """timbrooks/instruct-pix2pix"""
										lowercase    :      str       = StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    __magic_name__			, safety_checker=__magic_name__			, )
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

										lowercase    :      Optional[int]       = pipe(**__magic_name__				)
										lowercase    :      Tuple       = output.images[0]

										lowercase    :      int       = image[255:258, 383:386, -1]

										assert image.shape == (504, 504, 3)
										lowercase    :      str       = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-3








"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






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






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										print(self.position				)


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






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


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

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

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


if __name__ == "__main__":
				_lowerCAmelCase            =	Gridworld()
				# Start position and goal
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(0, 0)
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(4, 4)
				print(F'path from {start.position} to {goal.position}')
				_lowerCAmelCase            =	astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								_lowerCAmelCase            =	1
				print(world.w)
