
"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     KandinskyVaaImgaImgPipeline
	lowercase_				=     ['image_embeds', 'negative_image_embeds', 'image']
	lowercase_				=     [
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	]
	lowercase_				=     [
	    'generator',
	    'height',
	    'width',
	    'strength',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	lowercase_				=     False




	@property
	def       _UpperCamelCase   (			self )		->							List[Any]:
			return 32




	@property
	def       _UpperCamelCase   (			self )		->							int:
			return 32




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return self.time_input_dim




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return self.time_input_dim * 4




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return 100




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			torch.manual_seed(0 )

			lowerCamelCase					:      str          =   {
			    'in_channels': 4,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 8,
			    'addition_embed_type': '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': 'image_proj',
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': None,
			}

			lowerCamelCase					:      Optional[int]          =   UNetaDConditionModel(**UpperCAmelCase_ )
			return model




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			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       _UpperCamelCase   (			self )		->							Any:
			torch.manual_seed(0 )
			lowerCamelCase					:      str          =   VQModel(**self.dummy_movq_kwargs )
			return model




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      Optional[Any]          =   self.dummy_unet
			lowerCamelCase					:      int          =   self.dummy_movq

			lowerCamelCase					:      Dict          =   {
			    'num_train_timesteps': 1000,
			    'beta_schedule': 'linear',
			    'beta_start': 0.00085,
			    'beta_end': 0.012,
			    'clip_sample': False,
			    'set_alpha_to_one': False,
			    'steps_offset': 0,
			    'prediction_type': 'epsilon',
			    'thresholding': False,
			}

			lowerCamelCase					:      Tuple          =   DDIMScheduler(**UpperCAmelCase_ )

			lowerCamelCase					:      Dict          =   {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							Dict:
			lowerCamelCase					:      Union[str, Any]          =   floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
			lowerCamelCase					:      int          =   floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(seed + 1 ) ).to(
			    UpperCAmelCase_ )
			# create init_image
			lowerCamelCase					:      Tuple          =   floats_tensor((1, 3, 64, 64)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   image.cpu().permute(0		,			2		,			3		,			1 )[0]
			lowerCamelCase					:      Optional[int]          =   Image.fromarray(np.uinta(UpperCAmelCase_ ) ).convert('RGB' ).resize((256, 256) )

			if str(UpperCAmelCase_ ).startswith('mps' ):
					lowerCamelCase					:      Dict          =   torch.manual_seed(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      int          =   torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   {
			    'image': init_image,
			    'image_embeds': image_embeds,
			    'negative_image_embeds': negative_image_embeds,
			    'generator': generator,
			    'height': 64,
			    'width': 64,
			    'num_inference_steps': 10,
			    'guidance_scale': 7.0,
			    'strength': 0.2,
			    'output_type': 'np',
			}
			return inputs




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Optional[int]          =   'cpu'

			lowerCamelCase					:      int          =   self.get_dummy_components()

			lowerCamelCase					:      Optional[int]          =   self.pipeline_class(**UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   pipe.to(UpperCAmelCase_ )

			pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      List[str]          =   pipe(**self.get_dummy_inputs(UpperCAmelCase_ ) )
			lowerCamelCase					:      int          =   output.images

			lowerCamelCase					:      Optional[Any]          =   pipe(
			    **self.get_dummy_inputs(UpperCAmelCase_ )		,			return_dict=UpperCAmelCase_		,			)[0]

			lowerCamelCase					:      Tuple          =   image[0, -3:, -3:, -1]
			lowerCamelCase					:      List[str]          =   image_from_tuple[0, -3:, -3:, -1]

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

			lowerCamelCase					:      Any          =   np.array(
			    [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
			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()}"""






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




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Tuple          =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_img2img_frog.npy' )

			lowerCamelCase					:      Dict          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
			lowerCamelCase					:      Optional[Any]          =   'A red cartoon frog, 4k'

			lowerCamelCase					:      str          =   KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'		,			torch_dtype=torch.floataa )
			pipe_prior.to(UpperCAmelCase_ )

			lowerCamelCase					:      int          =   KandinskyVaaImgaImgPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder'		,			torch_dtype=torch.floataa )
			lowerCamelCase					:      Tuple          =   pipeline.to(UpperCAmelCase_ )

			pipeline.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      List[Any]          =   torch.Generator(device='cpu' ).manual_seed(0 )
			lowerCamelCase					:      Tuple          =   pipe_prior(
			    UpperCAmelCase_		,			generator=UpperCAmelCase_		,			num_inference_steps=5		,			negative_prompt=''		,			).to_tuple()

			lowerCamelCase					:      Any          =   pipeline(
			    image=UpperCAmelCase_		,			image_embeds=UpperCAmelCase_		,			negative_image_embeds=UpperCAmelCase_		,			generator=UpperCAmelCase_		,			num_inference_steps=100		,			height=768		,			width=768		,			strength=0.2		,			output_type='np'		,			)

			lowerCamelCase					:      List[Any]          =   output.images[0]

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

			assert_mean_pixel_difference(UpperCAmelCase_		,			UpperCAmelCase_ )








"""simple docstring"""

from __future__ import annotations

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

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

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

		while not found and not resign:
				if len(a_      ) == 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()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

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

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [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],
					]

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

					# the cost map which pushes the path closer to the goal
					_A						=			[[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])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			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])






