





def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case						,			_snake_case :			Optional[Any]  						=   len(__SCREAMING_SNAKE_CASE ), len(grid[0] )
				if (
				    min(__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE ) < 0
				    or row == row_length
				    or col == col_length
				    or (row, col) in visit
				    or grid[row][col] == 1
				):
								return 0
				if row == row_length - 1 and col == col_length - 1:
								return 1

				visit.add((row, col) )

				_snake_case :			Optional[Any]  						=   0
				count += depth_first_search(__SCREAMING_SNAKE_CASE	,			row + 1	,			__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE )
				count += depth_first_search(__SCREAMING_SNAKE_CASE	,			row - 1	,			__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE )
				count += depth_first_search(__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE	,			col + 1	,			__SCREAMING_SNAKE_CASE )
				count += depth_first_search(__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE	,			col - 1	,			__SCREAMING_SNAKE_CASE )

				visit.remove((row, col) )
				return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()













import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







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







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   1
										_snake_case :			str  						=   3
										_snake_case :			List[str]  						=   (32, 32)

										_snake_case :			int  						=   floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0					)					).to(lowerCamelCase_					)
										return image







						@property
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			Union[str, Any]  						=   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 ,				)
										return model







						@property
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			Optional[Any]  						=   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 ,				)
										return model







						@property
						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			List[str]  						=   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=10_00 ,				)
										return CLIPTextModel(lowerCamelCase_					)







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										def extract(*lowerCamelCase_			:		Optional[int] ,				**lowerCamelCase_			:		str					):


														class 			lowercase				:



																				"""simple docstring"""

																				def __init__(    self			:		Tuple					):





																								'''simple docstring'''







																								_snake_case :			List[str]  						=   torch.ones([0]					)







																				def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		Tuple					):





																								'''simple docstring'''







																								self.pixel_values.to(lowerCamelCase_					)
																								return self



														return Out()

										return extract







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case :			int  						=   self.dummy_cond_unet
										_snake_case :			str  						=   DDIMScheduler(
										    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule='scaled_linear' ,				clip_sample=lowerCamelCase_ ,				set_alpha_to_one=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   self.dummy_vae
										_snake_case :			Optional[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[Any]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

										# make sure here that pndm scheduler skips prk
										_snake_case :			Union[str, Any]  						=   StableDiffusionPipeline(
										    unet=lowerCamelCase_ ,				scheduler=lowerCamelCase_ ,				vae=lowerCamelCase_ ,				text_encoder=lowerCamelCase_ ,				tokenizer=lowerCamelCase_ ,				safety_checker=lowerCamelCase_ ,				feature_extractor=self.dummy_extractor ,				)
										_snake_case :			str  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			List[str]  						=   'A painting of a squirrel eating a burger'

										_snake_case :			Optional[int]  						=   torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
										_snake_case :			Optional[int]  						=   sd_pipe([prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np'					)
										_snake_case :			Union[str, Any]  						=   output.images

										_snake_case :			List[str]  						=   torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np' ,				return_dict=lowerCamelCase_ ,				)[0]

										_snake_case :			Tuple  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[Any]  						=   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_snake_case :			Optional[int]  						=   np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]					)

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







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case :			List[str]  						=   self.dummy_cond_unet
										_snake_case :			List[str]  						=   PNDMScheduler(skip_prk_steps=lowerCamelCase_					)
										_snake_case :			int  						=   self.dummy_vae
										_snake_case :			List[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[int]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

										# make sure here that pndm scheduler skips prk
										_snake_case :			Any  						=   StableDiffusionPipeline(
										    unet=lowerCamelCase_ ,				scheduler=lowerCamelCase_ ,				vae=lowerCamelCase_ ,				text_encoder=lowerCamelCase_ ,				tokenizer=lowerCamelCase_ ,				safety_checker=lowerCamelCase_ ,				feature_extractor=self.dummy_extractor ,				)
										_snake_case :			Union[str, Any]  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			str  						=   'A painting of a squirrel eating a burger'
										_snake_case :			Optional[int]  						=   torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
										_snake_case :			Tuple  						=   sd_pipe([prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np'					)

										_snake_case :			Optional[Any]  						=   output.images

										_snake_case :			Optional[Any]  						=   torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
										_snake_case :			Tuple  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np' ,				return_dict=lowerCamelCase_ ,				)[0]

										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]
										_snake_case :			Any  						=   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_snake_case :			str  						=   np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]					)

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







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   StableDiffusionPipeline.from_pretrained(
										    'hf-internal-testing/tiny-stable-diffusion-lms-pipe' ,				safety_checker=lowerCamelCase_					)
										assert isinstance(lowerCamelCase_ ,				lowerCamelCase_					)
										assert isinstance(pipe.scheduler ,				lowerCamelCase_					)
										assert pipe.safety_checker is None

										_snake_case :			Dict  						=   pipe('example prompt' ,				num_inference_steps=2					).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(lowerCamelCase_					)
														_snake_case :			Any  						=   StableDiffusionPipeline.from_pretrained(lowerCamelCase_					)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_snake_case :			Union[str, Any]  						=   pipe('example prompt' ,				num_inference_steps=2					).images[0]
										assert image is not None







						@unittest.skipIf(torch_device != 'cuda' ,				'This test requires a GPU'					)
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.dummy_cond_unet
										_snake_case :			Optional[Any]  						=   PNDMScheduler(skip_prk_steps=lowerCamelCase_					)
										_snake_case :			Any  						=   self.dummy_vae
										_snake_case :			Optional[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[int]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

										# put models in fp16
										_snake_case :			str  						=   unet.half()
										_snake_case :			Union[str, Any]  						=   vae.half()
										_snake_case :			Dict  						=   bert.half()

										# make sure here that pndm scheduler skips prk
										_snake_case :			List[str]  						=   StableDiffusionPipeline(
										    unet=lowerCamelCase_ ,				scheduler=lowerCamelCase_ ,				vae=lowerCamelCase_ ,				text_encoder=lowerCamelCase_ ,				tokenizer=lowerCamelCase_ ,				safety_checker=lowerCamelCase_ ,				feature_extractor=self.dummy_extractor ,				)
										_snake_case :			List[str]  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Tuple  						=   'A painting of a squirrel eating a burger'
										_snake_case :			Optional[int]  						=   sd_pipe([prompt] ,				num_inference_steps=2 ,				output_type='np'					).images

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



@nightly
@require_torch_gpu
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=lowerCamelCase_					)
										_snake_case :			List[str]  						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
										_snake_case :			Any  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   (
										    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
										    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
										    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
										    ' children from bahnhof zoo, detailed '
										)
										_snake_case :			List[str]  						=   40_03_66_03_46
										_snake_case :			int  						=   7

										# without safety guidance (sld_guidance_scale = 0)
										_snake_case :			Union[str, Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]
										_snake_case :			Optional[int]  						=   [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

										assert image.shape == (1, 5_12, 5_12, 3)

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

										# without safety guidance (strong configuration)
										_snake_case :			Tuple  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			int  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			Tuple  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[Any]  						=   [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

										assert image.shape == (1, 5_12, 5_12, 3)

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







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			str  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=lowerCamelCase_					)
										_snake_case :			Tuple  						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
										_snake_case :			Any  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   'padme amidala taking a bath artwork, safe for work, no nudity'
										_snake_case :			Optional[Any]  						=   27_34_97_17_55
										_snake_case :			Union[str, Any]  						=   7

										_snake_case :			Dict  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Tuple  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			Any  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			str  						=   [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

										assert image.shape == (1, 5_12, 5_12, 3)

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

										_snake_case :			Optional[Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			List[str]  						=   image[0, -3:, -3:, -1]
										_snake_case :			Union[str, Any]  						=   [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

										assert image.shape == (1, 5_12, 5_12, 3)

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







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					)
										_snake_case :			Optional[int]  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			List[Any]  						=   (
										    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
										    ' leyendecker'
										)
										_snake_case :			Union[str, Any]  						=   10_44_35_52_34
										_snake_case :			Dict  						=   12

										_snake_case :			Optional[int]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			Optional[int]  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			Optional[int]  						=   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]					)

										assert image.shape == (1, 5_12, 5_12, 3)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-7

										_snake_case :			List[Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			List[str]  						=   image[0, -3:, -3:, -1]
										_snake_case :			int  						=   np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]					)
										assert image.shape == (1, 5_12, 5_12, 3)

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






