





from __future__ import annotations

__A				:    List[Any]       =   list[tuple[int, int]]

__A				:    Union[str, Any]       =   [
    [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],
]

__A				:    Optional[Any]       =   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class 	A_       :
	def __init__(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				pos_x
				UpperCAmelCase   		=				pos_y
				UpperCAmelCase   		=				(pos_y, pos_x)
				UpperCAmelCase   		=				goal_x
				UpperCAmelCase   		=				goal_y
				UpperCAmelCase   		=				g_cost
				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				self.calculate_heuristic()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				abs(self.pos_x - self.goal_x		)
				UpperCAmelCase   		=				abs(self.pos_y - self.goal_y		)
				return dx + dy







	def __lt__(    self							,					_A		):






				'''simple docstring'''







				return self.f_cost < other.f_cost

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				Node(start[1]							,					start[0]							,					goal[1]							,					goal[0]							,					0							,					_A		)
				UpperCAmelCase   		=				Node(goal[1]							,					goal[0]							,					goal[1]							,					goal[0]							,					9_9_9_9_9							,					_A		)

				UpperCAmelCase   		=				[self.start]
				UpperCAmelCase   		=				[]

				UpperCAmelCase   		=				False
	def      _lowercase				(    self		):






				'''simple docstring'''







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

							if current_node.pos == self.target.pos:
										UpperCAmelCase   		=				True
										return self.retrace_path(_A		)

							self.closed_nodes.append(_A		)
							UpperCAmelCase   		=				self.get_successors(_A		)

							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(_A		)
										else:
													# retrieve the best current path
													UpperCAmelCase   		=				self.open_nodes.pop(self.open_nodes.index(_A		)		)

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

				if not self.reached:
							return [self.start.pos]
				return None
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				[]
				for action in delta:
							UpperCAmelCase   		=				parent.pos_x + action[1]
							UpperCAmelCase   		=				parent.pos_y + action[0]

							if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(_A		) - 1):
										continue

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

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







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				node
				UpperCAmelCase   		=				[]
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)		)
							UpperCAmelCase   		=				current_node.parent
				path.reverse()
				return path


if __name__ == "__main__":
				__A				:    Union[str, Any]       =   (0, 0)
				__A				:    Optional[Any]       =   (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				print("------")

				__A				:    str       =   GreedyBestFirst(init, goal)
				__A				:    int       =   greedy_bf.search()
				if path:
								for pos_x, pos_y in path:
												__A				:    int       =   2

								for elem in grid:
												print(elem)










import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 	A_       :
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				UNetaDConditionModel(
				    sample_size=3_2							,					layers_per_block=1							,					block_out_channels=[3_2, 6_4]							,					down_block_types=[
				        '''ResnetDownsampleBlock2D''',
				        '''SimpleCrossAttnDownBlock2D''',
				    ]							,					mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,					up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,					in_channels=3							,					out_channels=6							,					cross_attention_dim=3_2							,					encoder_hid_dim=3_2							,					attention_head_dim=8							,					addition_embed_type='''text'''							,					addition_embed_type_num_heads=2							,					cross_attention_norm='''group_norm'''							,					resnet_time_scale_shift='''scale_shift'''							,					act_fn='''gelu'''							,					)
				unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					thresholding=_A							,					dynamic_thresholding_ratio=0.95							,					sample_max_value=1.0							,					prediction_type='''epsilon'''							,					variance_type='''learned_range'''							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				IFWatermarker()

				return {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "watermarker": watermarker,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				UNetaDConditionModel(
				    sample_size=3_2							,					layers_per_block=[1, 2]							,					block_out_channels=[3_2, 6_4]							,					down_block_types=[
				        '''ResnetDownsampleBlock2D''',
				        '''SimpleCrossAttnDownBlock2D''',
				    ]							,					mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,					up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,					in_channels=6							,					out_channels=6							,					cross_attention_dim=3_2							,					encoder_hid_dim=3_2							,					attention_head_dim=8							,					addition_embed_type='''text'''							,					addition_embed_type_num_heads=2							,					cross_attention_norm='''group_norm'''							,					resnet_time_scale_shift='''scale_shift'''							,					act_fn='''gelu'''							,					class_embed_type='''timestep'''							,					mid_block_scale_factor=1.4_14							,					time_embedding_act_fn='''gelu'''							,					time_embedding_dim=3_2							,					)
				unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					thresholding=_A							,					dynamic_thresholding_ratio=0.95							,					sample_max_value=1.0							,					prediction_type='''epsilon'''							,					variance_type='''learned_range'''							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				IFWatermarker()

				return {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "image_noising_scheduler": image_noising_scheduler,
				    "watermarker": watermarker,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_dummy_components()
				UpperCAmelCase   		=				self.pipeline_class(**_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)

				UpperCAmelCase   		=				inputs['''prompt''']
				UpperCAmelCase   		=				inputs['''generator''']
				UpperCAmelCase   		=				inputs['''num_inference_steps''']
				UpperCAmelCase   		=				inputs['''output_type''']

				if "image" in inputs:
							UpperCAmelCase   		=				inputs['''image''']
				else:
							UpperCAmelCase   		=				None

				if "mask_image" in inputs:
							UpperCAmelCase   		=				inputs['''mask_image''']
				else:
							UpperCAmelCase   		=				None

				if "original_image" in inputs:
							UpperCAmelCase   		=				inputs['''original_image''']
				else:
							UpperCAmelCase   		=				None

				UpperCAmelCase		,			UpperCAmelCase   		=				pipe.encode_prompt(_A		)

				# inputs with prompt converted to embeddings
				UpperCAmelCase   		=				{
				    '''prompt_embeds''': prompt_embeds,
				    '''negative_prompt_embeds''': negative_prompt_embeds,
				    '''generator''': generator,
				    '''num_inference_steps''': num_inference_steps,
				    '''output_type''': output_type,
				}

				if image is not None:
							UpperCAmelCase   		=				image

				if mask_image is not None:
							UpperCAmelCase   		=				mask_image

				if original_image is not None:
							UpperCAmelCase   		=				original_image

				# set all optional components to None
				for optional_component in pipe._optional_components:
							setattr(_A							,					_A							,					_A		)

				UpperCAmelCase   		=				pipe(**_A		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(_A		)
							UpperCAmelCase   		=				self.pipeline_class.from_pretrained(_A		)
							pipe_loaded.to(_A		)
							pipe_loaded.set_progress_bar_config(disable=_A		)

				pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				for optional_component in pipe._optional_components:
							self.assertTrue(
							    getattr(_A							,					_A		) is None							,					F"""`{optional_component}` did not stay set to None after loading."""							,					)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)

				UpperCAmelCase   		=				inputs['''generator''']
				UpperCAmelCase   		=				inputs['''num_inference_steps''']
				UpperCAmelCase   		=				inputs['''output_type''']

				# inputs with prompt converted to embeddings
				UpperCAmelCase   		=				{
				    '''prompt_embeds''': prompt_embeds,
				    '''negative_prompt_embeds''': negative_prompt_embeds,
				    '''generator''': generator,
				    '''num_inference_steps''': num_inference_steps,
				    '''output_type''': output_type,
				}

				if image is not None:
							UpperCAmelCase   		=				image

				if mask_image is not None:
							UpperCAmelCase   		=				mask_image

				if original_image is not None:
							UpperCAmelCase   		=				original_image

				UpperCAmelCase   		=				pipe_loaded(**_A		)[0]

				UpperCAmelCase   		=				np.abs(to_np(_A		) - to_np(_A		)		).max()
				self.assertLess(_A							,					1E-4		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_dummy_components()
				UpperCAmelCase   		=				self.pipeline_class(**_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)
				UpperCAmelCase   		=				pipe(**_A		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(_A		)
							UpperCAmelCase   		=				self.pipeline_class.from_pretrained(_A		)
							pipe_loaded.to(_A		)
							pipe_loaded.set_progress_bar_config(disable=_A		)

				pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)
				UpperCAmelCase   		=				pipe_loaded(**_A		)[0]

				UpperCAmelCase   		=				np.abs(to_np(_A		) - to_np(_A		)		).max()
				self.assertLess(_A							,					1E-4		)



