



import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[int]		=		LDMTextToImagePipeline
						UpperCAmelCase			:		str		=		TEXT_TO_IMAGE_PARAMS - {
						    'negative_prompt',
						    'negative_prompt_embeds',
						    'cross_attention_kwargs',
						    'prompt_embeds',
						}
						UpperCAmelCase			:		Any		=		PipelineTesterMixin.required_optional_params - {
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						UpperCAmelCase			:		str		=		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase			:		Dict		=		False







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Dict						=				UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,	cross_attention_dim=32       ,	)
											SCREAMING_SNAKE_CASE							:     Any						=				DDIMScheduler(
											    beta_start=0.00085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=snake_case       ,	set_alpha_to_one=snake_case       ,	)
											torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     int						=				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	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				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=1000       ,	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				CLIPTextModel(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

											SCREAMING_SNAKE_CASE							:     int						=				{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vqvae': vae,
											    'bert': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components







						def       lowerCamelCase_				(  self    :				Dict       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				str=0	):



											'''simple docstring'''


											if str(snake_case	).startswith('mps'	):
																SCREAMING_SNAKE_CASE							:     str						=				torch.manual_seed(snake_case	)
											else:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.Generator(device=snake_case	).manual_seed(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cpu'  # ensure determinism for the device-dependent torch.Generator

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_dummy_components()
											SCREAMING_SNAKE_CASE							:     Dict						=				LDMTextToImagePipeline(**snake_case	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_dummy_inputs(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				pipe(**snake_case	).images
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]	)

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


@slow
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]=torch.floataa       ,	snake_case    :				Tuple=0	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.random.RandomState(snake_case	).standard_normal((1, 4, 32, 32)	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.from_numpy(snake_case	).to(device=snake_case       ,	dtype=snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'	).to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     int						=				self.get_inputs(snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				pipe(**snake_case	).images
											SCREAMING_SNAKE_CASE							:     Any						=				image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				np.abs(expected_slice - image_slice	).max()
											assert max_diff < 1E-3


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				Dict=torch.floataa       ,	snake_case    :				List[Any]=0	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				torch.manual_seed(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				np.random.RandomState(snake_case	).standard_normal((1, 4, 32, 32)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.from_numpy(snake_case	).to(device=snake_case       ,	dtype=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 50,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'	).to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				self.get_inputs(snake_case	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				pipe(**snake_case	).images[0]

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				load_numpy(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'	)
											SCREAMING_SNAKE_CASE							:     Dict						=				np.abs(expected_image - image	).max()
											assert max_diff < 1E-3




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_lowerCamelCase    :		List[str]		      =    0

_lowerCamelCase    :		Tuple		      =    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_lowerCamelCase    :		Tuple		      =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_lowerCamelCase    :		Union[str, Any]		      =    tuple[int, int]


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				Node | None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				pos_x
											SCREAMING_SNAKE_CASE							:     str						=				pos_y
											SCREAMING_SNAKE_CASE							:     Any						=				(pos_y, pos_x)
											SCREAMING_SNAKE_CASE							:     List[str]						=				goal_x
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				goal_y
											SCREAMING_SNAKE_CASE							:     int						=				g_cost
											SCREAMING_SNAKE_CASE							:     Any						=				parent
											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.calculate_heuristic()
											SCREAMING_SNAKE_CASE							:     str						=				self.g_cost + self.h_cost







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.pos_x - self.goal_x
											SCREAMING_SNAKE_CASE							:     str						=				self.pos_y - self.goal_y
											if HEURISTIC == 1:
																return abs(snake_case	) + abs(snake_case	)
											else:
																return sqrt(dy**2 + dx**2	)







						def __lt__(  self    :				Any       ,	snake_case    :				Node	):



											'''simple docstring'''


											return self.f_cost < other.f_cost


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				TPosition       ,	snake_case    :				TPosition	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Node(start[1]       ,	start[0]       ,	goal[1]       ,	goal[0]       ,	0       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				Node(goal[1]       ,	goal[0]       ,	goal[1]       ,	goal[0]       ,	99999       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				[self.start]
											SCREAMING_SNAKE_CASE							:     list[Node]						=				[]

											SCREAMING_SNAKE_CASE							:     int						=				False







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											while self.open_nodes:
																# Open Nodes are sorted using __lt__
																self.open_nodes.sort()
																SCREAMING_SNAKE_CASE							:     Dict						=				self.open_nodes.pop(0	)

																if current_node.pos == self.target.pos:
																					return self.retrace_path(snake_case	)

																self.closed_nodes.append(snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				self.get_successors(snake_case	)

																for child_node in successors:
																					if child_node in self.closed_nodes:
																										continue

																					if child_node not in self.open_nodes:
																										self.open_nodes.append(snake_case	)
																					else:
																										# retrieve the best current path
																										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.open_nodes.pop(self.open_nodes.index(snake_case	)	)

																										if child_node.g_cost < better_node.g_cost:
																															self.open_nodes.append(snake_case	)
																										else:
																															self.open_nodes.append(snake_case	)

											return [self.start.pos]







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Node	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				[]
											for action in delta:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				parent.pos_x + action[1]
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				parent.pos_y + action[0]
																if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(snake_case	) - 1):
																					continue

																if grid[pos_y][pos_x] != 0:
																					continue

																successors.append(
																    Node(
																        snake_case       ,	snake_case       ,	self.target.pos_y       ,	self.target.pos_x       ,	parent.g_cost + 1       ,	snake_case       ,	)	)
											return successors







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Node | None	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				node
											SCREAMING_SNAKE_CASE							:     List[Any]						=				[]
											while current_node is not None:
																path.append((current_node.pos_y, current_node.pos_x)	)
																SCREAMING_SNAKE_CASE							:     Dict						=				current_node.parent
											path.reverse()
											return path


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				TPosition       ,	snake_case    :				TPosition	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				AStar(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				AStar(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				False







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
																self.fwd_astar.open_nodes.sort()
																self.bwd_astar.open_nodes.sort()
																SCREAMING_SNAKE_CASE							:     Dict						=				self.fwd_astar.open_nodes.pop(0	)
																SCREAMING_SNAKE_CASE							:     Any						=				self.bwd_astar.open_nodes.pop(0	)

																if current_bwd_node.pos == current_fwd_node.pos:
																					return self.retrace_bidirectional_path(
																					    snake_case       ,	snake_case	)

																self.fwd_astar.closed_nodes.append(snake_case	)
																self.bwd_astar.closed_nodes.append(snake_case	)

																SCREAMING_SNAKE_CASE							:     str						=				current_bwd_node
																SCREAMING_SNAKE_CASE							:     int						=				current_fwd_node

																SCREAMING_SNAKE_CASE							:     str						=				{
																    self.fwd_astar: self.fwd_astar.get_successors(snake_case	),
																    self.bwd_astar: self.bwd_astar.get_successors(snake_case	),
																}

																for astar in [self.fwd_astar, self.bwd_astar]:
																					for child_node in successors[astar]:
																										if child_node in astar.closed_nodes:
																															continue

																										if child_node not in astar.open_nodes:
																															astar.open_nodes.append(snake_case	)
																										else:
																															# retrieve the best current path
																															SCREAMING_SNAKE_CASE							:     List[Any]						=				astar.open_nodes.pop(
																															    astar.open_nodes.index(snake_case	)	)

																															if child_node.g_cost < better_node.g_cost:
																																				astar.open_nodes.append(snake_case	)
																															else:
																																				astar.open_nodes.append(snake_case	)

											return [self.fwd_astar.start.pos]







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Node       ,	snake_case    :				Node	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.fwd_astar.retrace_path(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.bwd_astar.retrace_path(snake_case	)
											bwd_path.pop()
											bwd_path.reverse()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				fwd_path + bwd_path
											return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			_lowerCamelCase    :		Tuple		      =    (0, 0)
			_lowerCamelCase    :		Dict		      =    (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			_lowerCamelCase    :		Union[str, Any]		      =    time.time()
			_lowerCamelCase    :		List[str]		      =    AStar(init, goal)
			_lowerCamelCase    :		Optional[int]		      =    a_star.search()
			_lowerCamelCase    :		str		      =    time.time() - start_time
			print(f"""AStar execution time = {end_time:f} seconds""")

			_lowerCamelCase    :		Optional[Any]		      =    time.time()
			_lowerCamelCase    :		Union[str, Any]		      =    BidirectionalAStar(init, goal)
			_lowerCamelCase    :		List[Any]		      =    time.time() - bd_start_time
			print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")