"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
		a__:					List[str]      							=     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)       )

		a__:					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()



"""simple docstring"""
import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     UNetaDModel(
            block_out_channels=(32, 64)   ,      layers_per_block=2   ,      sample_size=32   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[Any]      							=     VQModel(
            block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=3   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[int]      							=     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(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

        a__:					Union[str, Any]      							=     LDMPipeline(unet=lowercase   ,      vqvae=lowercase   ,      scheduler=lowercase)
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance







@slow
@require_torch
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance


