


import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= LDMTextToImagePipeline
				lowerCAmelCase 		= TEXT_TO_IMAGE_PARAMS - {
				    'negative_prompt',
				    'negative_prompt_embeds',
				    'cross_attention_kwargs',
				    'prompt_embeds',
				}
				lowerCAmelCase 		= PipelineTesterMixin.required_optional_params - {
				    'num_images_per_prompt',
				    'callback',
				    'callback_steps',
				}
				lowerCAmelCase 		= TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCAmelCase 		= False

				def      _UpperCAmelCase					(						self   )	->				str:
					torch.manual_seed(0   )
					A								=				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				,   )
					A								=				DDIMScheduler(
					    beta_start=0.0_00_85				,   beta_end=0.0_12				,   beta_schedule="""scaled_linear"""				,   clip_sample=a__				,   set_alpha_to_one=a__				,   )
					torch.manual_seed(0   )
					A								=				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				,   )
					torch.manual_seed(0   )
					A								=				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				,   )
					A								=				CLIPTextModel(a__   )
					A								=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

					A								=				{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vqvae""": vae,
					    """bert""": text_encoder,
					    """tokenizer""": tokenizer,
					}
					return components

				def      _UpperCAmelCase					(						self				,   a__				,   a__=0   )	->				Any:
					if str(a__   ).startswith("""mps"""   ):
						A								=				torch.manual_seed(a__   )
					else:
						A								=				torch.Generator(device=a__   ).manual_seed(a__   )
					A								=				{
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 6.0,
					    """output_type""": """numpy""",
					}
					return inputs

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator

					A								=				self.get_dummy_components()
					A								=				LDMTextToImagePipeline(**a__   )
					pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				pipe(**a__   ).images
					A								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 16, 16, 3)
					A								=				np.array([0.61_01, 0.61_56, 0.56_22, 0.48_95, 0.66_61, 0.38_04, 0.57_48, 0.61_36, 0.50_14]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3






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

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def      _UpperCAmelCase					(						self				,   a__				,   a__=torch.floataa				,   a__=0   )	->				int:
					A								=				torch.manual_seed(a__   )
					A								=				np.random.RandomState(a__   ).standard_normal((1, 4, 32, 32)   )
					A								=				torch.from_numpy(a__   ).to(device=a__				,   dtype=a__   )
					A								=				{
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """latents""": latents,
					    """generator""": generator,
					    """num_inference_steps""": 3,
					    """guidance_scale""": 6.0,
					    """output_type""": """numpy""",
					}
					return inputs

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""   ).to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs(a__   )
					A								=				pipe(**a__   ).images
					A								=				image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 256, 256, 3)
					A								=				np.array([0.5_18_25, 0.5_28_50, 0.5_25_43, 0.5_42_58, 0.5_23_04, 0.5_25_69, 0.5_43_63, 0.5_52_76, 0.5_68_78]   )
					A								=				np.abs(expected_slice - image_slice   ).max()
					assert max_diff < 1e-3






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

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def      _UpperCAmelCase					(						self				,   a__				,   a__=torch.floataa				,   a__=0   )	->				str:
					A								=				torch.manual_seed(a__   )
					A								=				np.random.RandomState(a__   ).standard_normal((1, 4, 32, 32)   )
					A								=				torch.from_numpy(a__   ).to(device=a__				,   dtype=a__   )
					A								=				{
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """latents""": latents,
					    """generator""": generator,
					    """num_inference_steps""": 50,
					    """guidance_scale""": 6.0,
					    """output_type""": """numpy""",
					}
					return inputs

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""   ).to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs(a__   )
					A								=				pipe(**a__   ).images[0]

					A								=				load_numpy(
					    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""   )
					A								=				np.abs(expected_image - image   ).max()
					assert max_diff < 1e-3







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


if __name__ == "__main__":
				print(f'''{solution() = }''')



