


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= KandinskyInpaintPipeline
				lowerCAmelCase 		= ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
				lowerCAmelCase 		= [
				    'prompt',
				    'negative_prompt',
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				    'mask_image',
				]
				lowerCAmelCase 		= [
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'negative_prompt',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCAmelCase 		= False

				@property
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					return 32

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					return 32

				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.time_input_dim

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return self.time_input_dim * 4

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return 100

				@property
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""   )
					return tokenizer

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					torch.manual_seed(0   )
					A								=				MCLIPConfig(
					    numDims=self.cross_attention_dim				,   transformerDimensions=self.text_embedder_hidden_size				,   hidden_size=self.text_embedder_hidden_size				,   intermediate_size=37				,   num_attention_heads=4				,   num_hidden_layers=5				,   vocab_size=1005				,   )

					A								=				MultilingualCLIP(a__   )
					A								=				text_encoder.eval()

					return text_encoder

				@property
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					torch.manual_seed(0   )

					A								=				{
					    """in_channels""": 9,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 8,
					    """addition_embed_type""": """text_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""": """text_image_proj""",
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": None,
					}

					A								=				UNetaDConditionModel(**a__   )
					return model

				@property
				def      _UpperCAmelCase					(						self   )	->				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   )	->				Dict:
					torch.manual_seed(0   )
					A								=				VQModel(**self.dummy_movq_kwargs   )
					return model

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.dummy_text_encoder
					A								=				self.dummy_tokenizer
					A								=				self.dummy_unet
					A								=				self.dummy_movq

					A								=				DDIMScheduler(
					    num_train_timesteps=1000				,   beta_schedule="""linear"""				,   beta_start=0.0_00_85				,   beta_end=0.0_12				,   clip_sample=a__				,   set_alpha_to_one=a__				,   steps_offset=1				,   prediction_type="""epsilon"""				,   thresholding=a__				,   )

					A								=				{
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components

				def      _UpperCAmelCase					(						self				,   a__				,   a__=0   )	->				List[Any]:
					A								=				floats_tensor((1, self.cross_attention_dim)				,   rng=random.Random(a__   )   ).to(a__   )
					A								=				floats_tensor((1, self.cross_attention_dim)				,   rng=random.Random(seed + 1   )   ).to(a__   )
					# create init_image
					A								=				floats_tensor((1, 3, 64, 64)				,   rng=random.Random(a__   )   ).to(a__   )
					A								=				image.cpu().permute(0				,   2				,   3				,   1   )[0]
					A								=				Image.fromarray(np.uinta(a__   )   ).convert("""RGB"""   ).resize((256, 256)   )
					# create mask
					A								=				np.ones((64, 64)				,   dtype=np.floataa   )
					A								=				0

					if str(a__   ).startswith("""mps"""   ):
						A								=				torch.manual_seed(a__   )
					else:
						A								=				torch.Generator(device=a__   ).manual_seed(a__   )
					A								=				{
					    """prompt""": """horse""",
					    """image""": init_image,
					    """mask_image""": mask,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """generator""": generator,
					    """height""": 64,
					    """width""": 64,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 4.0,
					    """output_type""": """np""",
					}
					return inputs

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				"""cpu"""

					A								=				self.get_dummy_components()

					A								=				self.pipeline_class(**a__   )
					A								=				pipe.to(a__   )

					pipe.set_progress_bar_config(disable=a__   )

					A								=				pipe(**self.get_dummy_inputs(a__   )   )
					A								=				output.images

					A								=				pipe(
					    **self.get_dummy_inputs(a__   )				,   return_dict=a__				,   )[0]

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

					print(f'image.shape {image.shape}'   )

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

					A								=				np.array(
					    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

					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()}'

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3   )






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

				"""simple docstring"""







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

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""   )

					A								=				load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
					A								=				np.ones((768, 768)				,   dtype=np.floataa   )
					A								=				0

					A								=				"""a hat"""

					A								=				KandinskyPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-prior"""				,   torch_dtype=torch.floataa   )
					pipe_prior.to(a__   )

					A								=				KandinskyInpaintPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-inpaint"""				,   torch_dtype=torch.floataa   )
					A								=				pipeline.to(a__   )
					pipeline.set_progress_bar_config(disable=a__   )

					A								=				torch.Generator(device="""cpu"""   ).manual_seed(0   )
					A					,			A								=				pipe_prior(
					    a__				,   generator=a__				,   num_inference_steps=5				,   negative_prompt=""""""				,   ).to_tuple()

					A								=				pipeline(
					    a__				,   image=a__				,   mask_image=a__				,   image_embeds=a__				,   negative_image_embeds=a__				,   generator=a__				,   num_inference_steps=100				,   height=768				,   width=768				,   output_type="""np"""				,   )

					A								=				output.images[0]

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

					assert_mean_pixel_difference(a__				,   a__   )







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() = }''')



