



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 		a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    Any	    =     KandinskyInpaintPipeline
	__magic_name__		:    List[Any]	    =     ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	__magic_name__		:    int	    =     [
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	__magic_name__		:    List[str]	    =     [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	__magic_name__		:    Optional[int]	    =     False


	@property
	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					return 32


	@property
	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

					return 32


	@property
	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

					return self.time_input_dim


	@property
	def 				lowercase__  (self : int      )     ->			Dict:

					"""simple docstring"""

					return self.time_input_dim * 4


	@property
	def 				lowercase__  (self : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					return 100


	@property
	def 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
					return tokenizer


	@property
	def 				lowercase__  (self : int      )     ->			List[Any]:

					"""simple docstring"""

					torch.manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= MCLIPConfig(
					    numDims=self.cross_attention_dim,					transformerDimensions=self.text_embedder_hidden_size,					hidden_size=self.text_embedder_hidden_size,					intermediate_size=37,					num_attention_heads=4,					num_hidden_layers=5,					vocab_size=1005,					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= MultilingualCLIP(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= text_encoder.eval()

					return text_encoder


	@property
	def 				lowercase__  (self : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

					torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						Dict      					= {
					    '''in_channels''': 9,
					    # Out channels is double in channels because predicts mean and variance
					    '''out_channels''': 8,
					    '''addition_embed_type''': '''text_image''',
					    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
					    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
					    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
					    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    '''layers_per_block''': 1,
					    '''encoder_hid_dim''': self.text_embedder_hidden_size,
					    '''encoder_hid_dim_type''': '''text_image_proj''',
					    '''cross_attention_dim''': self.cross_attention_dim,
					    '''attention_head_dim''': 4,
					    '''resnet_time_scale_shift''': '''scale_shift''',
					    '''class_embed_type''': None,
					}

					SCREAMING_SNAKE_CASE  :						str      					= UNetaDConditionModel(**__UpperCAmelCase      )
					return model


	@property
	def 				lowercase__  (self : Dict      )     ->			Any:

					"""simple docstring"""

					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}


	@property
	def 				lowercase__  (self : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					torch.manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Any      					= VQModel(**self.dummy_movq_kwargs      )
					return model


	def 				lowercase__  (self : int      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.dummy_text_encoder
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.dummy_tokenizer
					SCREAMING_SNAKE_CASE  :						str      					= self.dummy_unet
					SCREAMING_SNAKE_CASE  :						int      					= self.dummy_movq

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= DDIMScheduler(
					    num_train_timesteps=1000,					beta_schedule='''linear''',					beta_start=0.0_0085,					beta_end=0.012,					clip_sample=__UpperCAmelCase,					set_alpha_to_one=__UpperCAmelCase,					steps_offset=1,					prediction_type='''epsilon''',					thresholding=__UpperCAmelCase,					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''movq''': movq,
					}

					return components


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict=0      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= floats_tensor((1, self.cross_attention_dim),					rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= floats_tensor((1, self.cross_attention_dim),					rng=random.Random(seed + 1      )      ).to(__UpperCAmelCase      )
					# create init_image
					SCREAMING_SNAKE_CASE  :						Tuple      					= floats_tensor((1, 3, 64, 64),					rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= image.cpu().permute(0,					2,					3,					1      )[0]
					SCREAMING_SNAKE_CASE  :						Any      					= Image.fromarray(np.uinta(__UpperCAmelCase      )      ).convert('''RGB'''      ).resize((256, 256)      )
					# create mask
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.ones((64, 64),					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= 0

					if str(__UpperCAmelCase      ).startswith('''mps'''      ):
									SCREAMING_SNAKE_CASE  :						Any      					= torch.manual_seed(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Dict      					= torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= {
					    '''prompt''': '''horse''',
					    '''image''': init_image,
					    '''mask_image''': mask,
					    '''image_embeds''': image_embeds,
					    '''negative_image_embeds''': negative_image_embeds,
					    '''generator''': generator,
					    '''height''': 64,
					    '''width''': 64,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 4.0,
					    '''output_type''': '''np''',
					}
					return inputs


	def 				lowercase__  (self : Tuple      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= '''cpu'''

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_dummy_components()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pipeline_class(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pipe.to(__UpperCAmelCase      )

					pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= pipe(**self.get_dummy_inputs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Dict      					= output.images

					SCREAMING_SNAKE_CASE  :						str      					= pipe(
					    **self.get_dummy_inputs(__UpperCAmelCase      ),					return_dict=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						Dict      					= image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE  :						str      					= image_from_tuple[0, -3:, -3:, -1]

					print(F'''image.shape {image.shape}'''      )

					assert image.shape == (1, 64, 64, 3)

					SCREAMING_SNAKE_CASE  :						Tuple      					= np.array(
					    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]      )

					assert (
					    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
					), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
					), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






	def 				lowercase__  (self : Any      )     ->			Any:

					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3e-3      )



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


	def 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

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






	def 				lowercase__  (self : str      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.ones((768, 768),					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE  :						int      					= 0

					SCREAMING_SNAKE_CASE  :						Dict      					= '''a hat'''

					SCREAMING_SNAKE_CASE  :						List[Any]      					= KandinskyPriorPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-prior''',					torch_dtype=torch.floataa      )
					pipe_prior.to(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= KandinskyInpaintPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-inpaint''',					torch_dtype=torch.floataa      )
					SCREAMING_SNAKE_CASE  :						Dict      					= pipeline.to(__UpperCAmelCase      )
					pipeline.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.Generator(device='''cpu'''      ).manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pipe_prior(
					    __UpperCAmelCase,					generator=__UpperCAmelCase,					num_inference_steps=5,					negative_prompt='''''',					).to_tuple()

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pipeline(
					    __UpperCAmelCase,					image=__UpperCAmelCase,					mask_image=__UpperCAmelCase,					image_embeds=__UpperCAmelCase,					negative_image_embeds=__UpperCAmelCase,					generator=__UpperCAmelCase,					num_inference_steps=100,					height=768,					width=768,					output_type='''np''',					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(__UpperCAmelCase,					__UpperCAmelCase      )









'''simple docstring'''

from __future__ import annotations

snake_case_			=     [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the reference grid
				SCREAMING_SNAKE_CASE  :						Dict      					= 1
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the action grid

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= init[0]
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= init[1]
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						List[Any]      					= g + heuristic[x][y]  # cost from starting cell to destination cell
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [[f, g, x, y]]

				SCREAMING_SNAKE_CASE  :						List[str]      					= False  # flag that is set when search is complete
				SCREAMING_SNAKE_CASE  :						Any      					= False  # flag set if we can't find expand

				while not found and not resign:
								if len(_SCREAMING_SNAKE_CASE       ) == 0:
												raise ValueError('''Algorithm is unable to find solution'''       )
								else:  # to choose the least costliest action so as to move closer to the goal
												cell.sort()
												cell.reverse()
												SCREAMING_SNAKE_CASE  :						List[str]      					= cell.pop()
												SCREAMING_SNAKE_CASE  :						List[str]      					= next_cell[2]
												SCREAMING_SNAKE_CASE  :						Dict      					= next_cell[3]
												SCREAMING_SNAKE_CASE  :						str      					= next_cell[1]

												if x == goal[0] and y == goal[1]:
																SCREAMING_SNAKE_CASE  :						Dict      					= True
												else:
																for i in range(len(_SCREAMING_SNAKE_CASE       )       ):  # to try out different valid actions
																				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= x + DIRECTIONS[i][0]
																				SCREAMING_SNAKE_CASE  :						Tuple      					= y + DIRECTIONS[i][1]
																				if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE       ) and ya >= 0 and ya < len(grid[0]       ):
																								if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= g + cost
																												SCREAMING_SNAKE_CASE  :						Any      					= ga + heuristic[xa][ya]
																												cell.append([fa, ga, xa, ya]       )
																												SCREAMING_SNAKE_CASE  :						List[Any]      					= 1
																												SCREAMING_SNAKE_CASE  :						Optional[int]      					= i
				SCREAMING_SNAKE_CASE  :						Dict      					= []
				SCREAMING_SNAKE_CASE  :						List[Any]      					= goal[0]
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= goal[1]
				invpath.append([x, y]       )  # we get the reverse path from here
				while x != init[0] or y != init[1]:
								SCREAMING_SNAKE_CASE  :						str      					= x - DIRECTIONS[action[x][y]][0]
								SCREAMING_SNAKE_CASE  :						str      					= y - DIRECTIONS[action[x][y]][1]
								SCREAMING_SNAKE_CASE  :						Tuple      					= xa
								SCREAMING_SNAKE_CASE  :						Dict      					= ya
								invpath.append([x, y]       )

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
								path.append(invpath[len(_SCREAMING_SNAKE_CASE       ) - 1 - i]       )
				return path, action


if __name__ == "__main__":
	snake_case_			=     [
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	snake_case_			=     [0, 0]
	# all coordinates are given in format [y,x]
	snake_case_			=     [len(grid) - 1, len(grid[0]) - 1]
	snake_case_			=     1

	# the cost map which pushes the path closer to the goal
	snake_case_			=     [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			snake_case_			=     abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				snake_case_			=     99

	snake_case_			,				snake_case_			=     search(grid, init, goal, cost, heuristic)

	print("""ACTION MAP""")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])




