





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard


@slow
@require_flax
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							def  A_     ( self     :					List[Any]						)				->			str:





														"""simple docstring"""
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()






							def  A_     ( self     :					List[Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						FlaxControlNetModel.from_pretrained(
														    "lllyasviel/sd-controlnet-canny",			from_pt=_UpperCAmelCase,			dtype=jnp.bfloataa						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    "runwayml/stable-diffusion-v1-5",			controlnet=_UpperCAmelCase,			from_pt=_UpperCAmelCase,			dtype=jnp.bfloataa						)
														SCREAMING_SNAKE_CASE__       :      Any     =						controlnet_params

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						'bird'
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						jax.device_count()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pipe.prepare_text_inputs([prompts] * num_samples						)

														SCREAMING_SNAKE_CASE__       :      Any     =						load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						pipe.prepare_image_inputs([canny_image] * num_samples						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						jax.random.PRNGKey(0						)
														SCREAMING_SNAKE_CASE__       :      str     =						jax.random.split(_UpperCAmelCase,			jax.device_count()						)

														SCREAMING_SNAKE_CASE__       :      str     =						replicate(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						shard(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						shard(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						pipe(
														    prompt_ids=_UpperCAmelCase,			image=_UpperCAmelCase,			params=_UpperCAmelCase,			prng_seed=_UpperCAmelCase,			num_inference_steps=5_0,			jit=_UpperCAmelCase,			).images
														assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

														SCREAMING_SNAKE_CASE__       :      Any     =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

														SCREAMING_SNAKE_CASE__       :      Dict     =						jnp.asarray(jax.device_get(image_slice.flatten()						)						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						jnp.array(
														    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]						)
														print(F'''output_slice: {output_slice}'''						)
														assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






							def  A_     ( self     :					Any						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						FlaxControlNetModel.from_pretrained(
														    "lllyasviel/sd-controlnet-openpose",			from_pt=_UpperCAmelCase,			dtype=jnp.bfloataa						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    "runwayml/stable-diffusion-v1-5",			controlnet=_UpperCAmelCase,			from_pt=_UpperCAmelCase,			dtype=jnp.bfloataa						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						controlnet_params

														SCREAMING_SNAKE_CASE__       :      List[str]     =						'Chef in the kitchen'
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						jax.device_count()
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						pipe.prepare_text_inputs([prompts] * num_samples						)

														SCREAMING_SNAKE_CASE__       :      Tuple     =						load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"						)
														SCREAMING_SNAKE_CASE__       :      Any     =						pipe.prepare_image_inputs([pose_image] * num_samples						)

														SCREAMING_SNAKE_CASE__       :      Any     =						jax.random.PRNGKey(0						)
														SCREAMING_SNAKE_CASE__       :      Any     =						jax.random.split(_UpperCAmelCase,			jax.device_count()						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						replicate(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						shard(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						shard(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Any     =						pipe(
														    prompt_ids=_UpperCAmelCase,			image=_UpperCAmelCase,			params=_UpperCAmelCase,			prng_seed=_UpperCAmelCase,			num_inference_steps=5_0,			jit=_UpperCAmelCase,			).images
														assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

														SCREAMING_SNAKE_CASE__       :      Dict     =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
														SCREAMING_SNAKE_CASE__       :      str     =						images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

														SCREAMING_SNAKE_CASE__       :      Dict     =						jnp.asarray(jax.device_get(image_slice.flatten()						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						jnp.array(
														    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]						)
														print(F'''output_slice: {output_slice}'''						)
														assert jnp.abs(output_slice - expected_slice						).max() < 1E-2













from __future__ import annotations

import time

_lowerCamelCase :    Tuple  							= list[tuple[int, int]]

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


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Node | None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_y
														SCREAMING_SNAKE_CASE__       :      str     =						(pos_y, pos_x)
														SCREAMING_SNAKE_CASE__       :      Dict     =						goal_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						goal_y
														SCREAMING_SNAKE_CASE__       :      str     =						parent


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					tuple[int, int],			_UpperCAmelCase     :					tuple[int, int]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						Node(start[1],			start[0],			goal[1],			goal[0],			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						Node(goal[1],			goal[0],			goal[1],			goal[0],			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[self.start]
														SCREAMING_SNAKE_CASE__       :      str     =						False






							def  A_     ( self     :					Dict						)				->			Path | None:





														"""simple docstring"""
														while self.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.node_queue.pop(0						)

																					if current_node.pos == self.target.pos:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						True
																												return self.retrace_path(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_successors(_UpperCAmelCase						)

																					for node in successors:
																												self.node_queue.append(_UpperCAmelCase						)

														if not self.reached:
																					return [self.start.pos]
														return None






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Node						)				->			list[Node]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						[]
														for action in delta:
																					SCREAMING_SNAKE_CASE__       :      str     =						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(_UpperCAmelCase						) - 1):
																												continue

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

																					successors.append(
																					    Node(_UpperCAmelCase,			_UpperCAmelCase,			self.target.pos_y,			self.target.pos_x,			_UpperCAmelCase						)						)
														return successors






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Node | None						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
														while current_node is not None:
																					path.append((current_node.pos_y, current_node.pos_x)						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_node.parent
														path.reverse()
														return path


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						False






							def  A_     ( self     :					List[str]						)				->			Path | None:





														"""simple docstring"""
														while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.fwd_bfs.node_queue.pop(0						)
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.bwd_bfs.node_queue.pop(0						)

																					if current_bwd_node.pos == current_fwd_node.pos:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						True
																												return self.retrace_bidirectional_path(
																												    _UpperCAmelCase,			_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						current_bwd_node
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_fwd_node

																					SCREAMING_SNAKE_CASE__       :      List[str]     =						{
																					    self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCAmelCase						),
																					    self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCAmelCase						),
																					}

																					for bfs in [self.fwd_bfs, self.bwd_bfs]:
																												for node in successors[bfs]:
																																			bfs.node_queue.append(_UpperCAmelCase						)

														if not self.reached:
																					return [self.fwd_bfs.start.pos]
														return None






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Node,			_UpperCAmelCase     :					Node						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.fwd_bfs.retrace_path(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						self.bwd_bfs.retrace_path(_UpperCAmelCase						)
														bwd_path.pop()
														bwd_path.reverse()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						fwd_path + bwd_path
														return path


if __name__ == "__main__":
		# all coordinates are given in format [y,x]
		import doctest

		doctest.testmod()
		_lowerCamelCase :    Optional[Any]  							= (0, 0)
		_lowerCamelCase :    Dict  							= (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		_lowerCamelCase :    Dict  							= time.time()
		_lowerCamelCase :    List[Any]  							= BreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bfs.search()
		_lowerCamelCase :    Dict  							= time.time() - start_bfs_time

		print('''Unidirectional BFS computation time : ''', bfs_time)

		_lowerCamelCase :    str  							= time.time()
		_lowerCamelCase :    Dict  							= BidirectionalBreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bd_bfs.search()
		_lowerCamelCase :    Optional[Any]  							= time.time() - start_bd_bfs_time

		print('''Bidirectional BFS computation time : ''', bd_bfs_time)






