


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







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

				@property
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				1
					A								=				3
					A								=				(32, 32)

					A								=				floats_tensor((batch_size, num_channels) + sizes				,   rng=random.Random(0   )   ).to(a__   )
					return image

				@property
				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				,   )
					return model

				@property
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					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				,   )
					return model

				@property
				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					torch.manual_seed(0   )
					A								=				RobertaSeriesConfig(
					    hidden_size=32				,   project_dim=32				,   intermediate_size=37				,   layer_norm_eps=1e-05				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=5006				,   )
					return RobertaSeriesModelWithTransformation(a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				Dict:

					def extract(*a__				,   **a__   ):






						class       _UpperCamelCase    :

										"""simple docstring"""







										def __init__(						self   )	->				str:
											A								=				torch.ones([0]   )

										def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[Any]:
											self.pixel_values.to(a__   )
											return self
						return Out()

					return extract

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					A								=				self.dummy_cond_unet
					A								=				PNDMScheduler(skip_prk_steps=a__   )
					A								=				self.dummy_vae
					A								=				self.dummy_text_encoder
					A								=				XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
					A								=				77

					A								=				self.dummy_image.to(a__   )
					A								=				init_image / 2 + 0.5

					# make sure here that pndm scheduler skips prk
					A								=				AltDiffusionImgaImgPipeline(
					    unet=a__				,   scheduler=a__				,   vae=a__				,   text_encoder=a__				,   tokenizer=a__				,   safety_checker=a__				,   feature_extractor=self.dummy_extractor				,   )
					A								=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,   do_normalize=a__   )
					A								=				alt_pipe.to(a__   )
					alt_pipe.set_progress_bar_config(disable=a__   )

					A								=				'''A painting of a squirrel eating a burger'''
					A								=				torch.Generator(device=a__   ).manual_seed(0   )
					A								=				alt_pipe(
					    [prompt]				,   generator=a__				,   guidance_scale=6.0				,   num_inference_steps=2				,   output_type="""np"""				,   image=a__				,   )

					A								=				output.images

					A								=				torch.Generator(device=a__   ).manual_seed(0   )
					A								=				alt_pipe(
					    [prompt]				,   generator=a__				,   guidance_scale=6.0				,   num_inference_steps=2				,   output_type="""np"""				,   image=a__				,   return_dict=a__				,   )[0]

					A								=				image[0, -3:, -3:, -1]
					A								=				image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					A								=				np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5e-3

				@unittest.skipIf(torch_device != """cuda"""				,   """This test requires a GPU"""   )
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.dummy_cond_unet
					A								=				PNDMScheduler(skip_prk_steps=a__   )
					A								=				self.dummy_vae
					A								=				self.dummy_text_encoder
					A								=				XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
					A								=				77

					A								=				self.dummy_image.to(a__   )

					# put models in fp16
					A								=				unet.half()
					A								=				vae.half()
					A								=				bert.half()

					# make sure here that pndm scheduler skips prk
					A								=				AltDiffusionImgaImgPipeline(
					    unet=a__				,   scheduler=a__				,   vae=a__				,   text_encoder=a__				,   tokenizer=a__				,   safety_checker=a__				,   feature_extractor=self.dummy_extractor				,   )
					A								=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,   do_normalize=a__   )
					A								=				alt_pipe.to(a__   )
					alt_pipe.set_progress_bar_config(disable=a__   )

					A								=				'''A painting of a squirrel eating a burger'''
					A								=				torch.manual_seed(0   )
					A								=				alt_pipe(
					    [prompt]				,   generator=a__				,   num_inference_steps=2				,   output_type="""np"""				,   image=a__				,   ).images

					assert image.shape == (1, 32, 32, 3)

				@unittest.skipIf(torch_device != """cuda"""				,   """This test requires a GPU"""   )
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/img2img/sketch-mountains-input.jpg"""   )
					# resize to resolution that is divisible by 8 but not 16 or 32
					A								=				init_image.resize((760, 504)   )

					A								=				'''BAAI/AltDiffusion'''
					A								=				AltDiffusionImgaImgPipeline.from_pretrained(
					    a__				,   safety_checker=a__				,   )
					pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )
					pipe.enable_attention_slicing()

					A								=				'''A fantasy landscape, trending on artstation'''

					A								=				torch.manual_seed(0   )
					A								=				pipe(
					    prompt=a__				,   image=a__				,   strength=0.75				,   guidance_scale=7.5				,   generator=a__				,   output_type="""np"""				,   )
					A								=				output.images[0]

					A								=				image[255:258, 383:386, -1]

					assert image.shape == (504, 760, 3)
					A								=				np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00]   )

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






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

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				int:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/img2img/sketch-mountains-input.jpg"""   )
					A								=				init_image.resize((768, 512)   )
					A								=				load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""   )

					A								=				'''BAAI/AltDiffusion'''
					A								=				AltDiffusionImgaImgPipeline.from_pretrained(
					    a__				,   safety_checker=a__				,   )
					pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )
					pipe.enable_attention_slicing()

					A								=				'''A fantasy landscape, trending on artstation'''

					A								=				torch.manual_seed(0   )
					A								=				pipe(
					    prompt=a__				,   image=a__				,   strength=0.75				,   guidance_scale=7.5				,   generator=a__				,   output_type="""np"""				,   )
					A								=				output.images[0]

					assert image.shape == (512, 768, 3)
					# img2img is flaky across GPUs even in fp32, so using MAE here
					assert np.abs(expected_image - image   ).max() < 1e-2







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() = }''')



