


from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__snake_case   							=						0

__snake_case   							=						[
    [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],
]

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

__snake_case   							=						tuple[int, int]



class 	__lowerCamelCase			:







       def __init__(			self: str,A_: int,A_: int,A_: int,A_: int,A_: int,A_: Node | None,):






            '''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()
            __UpperCamelCase =			self.g_cost + self.h_cost







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.pos_x - self.goal_x
            __UpperCamelCase =			self.pos_y - self.goal_y
            if HEURISTIC == 1:
                 return abs(A_					) + abs(A_					)
            else:
                 return sqrt(dy**2 + dx**2					)







       def __lt__(			self: int,A_: Node					):






            '''simple docstring'''




            return self.f_cost < other.f_cost



class 	__lowerCamelCase			:







       def __init__(			self: Any,A_: TPosition,A_: TPosition					):






            '''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_9999,A_					)

            __UpperCamelCase =			[self.start]
            __UpperCamelCase =			[]

            __UpperCamelCase =			False







       def 		snake_case_	(			self: Any					):






            '''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:
                      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_					)

            return [self.start.pos]







       def 		snake_case_	(			self: int,A_: Node					):






            '''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 		snake_case_	(			self: Any,A_: Node | None					):






            '''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



class 	__lowerCamelCase			:







       def __init__(			self: List[Any],A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			False







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






            '''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()
                 __UpperCamelCase =			self.fwd_astar.open_nodes.pop(0					)
                 __UpperCamelCase =			self.bwd_astar.open_nodes.pop(0					)

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

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

                 __UpperCamelCase =			current_bwd_node
                 __UpperCamelCase =			current_fwd_node

                 __UpperCamelCase =			{
                     self.fwd_astar: self.fwd_astar.get_successors(A_					),
                     self.bwd_astar: self.bwd_astar.get_successors(A_					),
                 }

                 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(A_					)
                           else:
                                # retrieve the best current path
                                __UpperCamelCase =			astar.open_nodes.pop(
                                    astar.open_nodes.index(A_					)					)

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

            return [self.fwd_astar.start.pos]







       def 		snake_case_	(			self: List[str],A_: Node,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			self.fwd_astar.retrace_path(A_					)
            __UpperCamelCase =			self.bwd_astar.retrace_path(A_					)
            bwd_path.pop()
            bwd_path.reverse()
            __UpperCamelCase =			fwd_path + bwd_path
            return path


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

    __snake_case   							=						time.time()
    __snake_case   							=						AStar(init, goal)
    __snake_case   							=						a_star.search()
    __snake_case   							=						time.time() - start_time
    print(f"""AStar execution time = {end_time:f} seconds""")

    __snake_case   							=						time.time()
    __snake_case   							=						BidirectionalAStar(init, goal)
    __snake_case   							=						time.time() - bd_start_time
    print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")






import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     VideoToVideoSDPipeline
       _lowercase											=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}						) - {"""image""", """width""", """height"""}
       _lowercase											=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}						) - {"""image"""}
       _lowercase											=     PipelineTesterMixin.required_optional_params - {"""latents"""}
       _lowercase											=     False

       # No `output_type`.
       _lowercase											=     frozenset(
           [
               """num_inference_steps""",
               """generator""",
               """latents""",
               """return_dict""",
               """callback""",
               """callback_steps""",
           ]						)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			UNetaDConditionModel(
                block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D'),up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D'),cross_attention_dim=32,attention_head_dim=4,)
            __UpperCamelCase =			DDIMScheduler(
                beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,beta_schedule='scaled_linear',clip_sample=A_,set_alpha_to_one=A_,)
            torch.manual_seed(0					)
            __UpperCamelCase =			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,sample_size=128,)
            torch.manual_seed(0					)
            __UpperCamelCase =			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,hidden_act='gelu',projection_dim=512,)
            __UpperCamelCase =			CLIPTextModel(A_					)
            __UpperCamelCase =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

            __UpperCamelCase =			{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
            }
            return components







       def 		snake_case_	(			self: Union[str, Any],A_: Any,A_: Any=0					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor((1, 3, 3, 32, 32),rng=random.Random(A_					)					).to(A_					)

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'A painting of a squirrel eating a burger',
                'video': video,
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'pt',
            }
            return inputs







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






            '''simple docstring'''




            __UpperCamelCase =			'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase =			self.get_dummy_components()
            __UpperCamelCase =			VideoToVideoSDPipeline(**A_					)
            __UpperCamelCase =			sd_pipe.to(A_					)
            sd_pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			self.get_dummy_inputs(A_					)
            __UpperCamelCase =			'np'
            __UpperCamelCase =			sd_pipe(**A_					).frames
            __UpperCamelCase =			frames[0][-3:, -3:, -1]

            assert frames[0].shape == (32, 32, 3)
            __UpperCamelCase =			np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]					)

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







       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available(),reason='XFormers attention is only available with CUDA and `xformers` installed',)
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_,expected_max_diff=5E-3					)







       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'					)
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            pass







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return super().test_progress_bar()



@slow
@skip_mps
class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL',torch_dtype=torch.floataa					)
            pipe.enable_model_cpu_offload()

            # 10 frames
            __UpperCamelCase =			torch.Generator(device='cpu'					).manual_seed(0					)
            __UpperCamelCase =			torch.randn((1, 10, 3, 1024, 576),generator=A_					)
            __UpperCamelCase =			video.to('cuda'					)

            __UpperCamelCase =			'Spiderman is surfing'

            __UpperCamelCase =			pipe(A_,video=A_,generator=A_,num_inference_steps=3,output_type='pt'					).frames

            __UpperCamelCase =			np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]					)
            assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1E-2


