
"""simple docstring"""







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	Any           =     KandinskyVaaPriorPipeline
		A_						:	List[str]           =     ['prompt']
		A_						:	Dict           =     ['prompt', 'negative_prompt']
		A_						:	str           =     [
		    'num_images_per_prompt',
		    'generator',
		    'num_inference_steps',
		    'latents',
		    'negative_prompt',
		    'guidance_scale',
		    'output_type',
		    'return_dict',
		]
		A_						:	Any           =     False

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return self.time_input_dim

		@property
		def 							__lowerCamelCase							(				self			):
			return self.time_input_dim * 4

		@property
		def 							__lowerCamelCase							(				self			):
			return 1_00

		@property
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
			return tokenizer

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Tuple						      =				CLIPTextConfig(
			    bos_token_id=0	,		eos_token_id=2	,		hidden_size=self.text_embedder_hidden_size	,		projection_dim=self.text_embedder_hidden_size	,		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 CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE			)

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)

			__lowerCAmelCase      :						List[str]						      =				{
			    'num_attention_heads': 2,
			    'attention_head_dim': 12,
			    'embedding_dim': self.text_embedder_hidden_size,
			    'num_layers': 1,
			}

			__lowerCAmelCase      :						Tuple						      =				PriorTransformer(**_SCREAMING_SNAKE_CASE			)
			# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
			__lowerCAmelCase      :						Dict						      =				nn.Parameter(torch.ones(model.clip_std.shape			)			)
			return model

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Any						      =				CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size	,		image_size=2_24	,		projection_dim=self.text_embedder_hidden_size	,		intermediate_size=37	,		num_attention_heads=4	,		num_channels=3	,		num_hidden_layers=5	,		patch_size=14	,		)

			__lowerCAmelCase      :						List[str]						      =				CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE			)
			return model

		@property
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				CLIPImageProcessor(
			    crop_size=2_24	,		do_center_crop=_SCREAMING_SNAKE_CASE	,		do_normalize=_SCREAMING_SNAKE_CASE	,		do_resize=_SCREAMING_SNAKE_CASE	,		image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]	,		image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]	,		resample=3	,		size=2_24	,		)

			return image_processor

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.dummy_prior
			__lowerCAmelCase      :						Any						      =				self.dummy_image_encoder
			__lowerCAmelCase      :						Union[str, Any]						      =				self.dummy_text_encoder
			__lowerCAmelCase      :						str						      =				self.dummy_tokenizer
			__lowerCAmelCase      :						Optional[Any]						      =				self.dummy_image_processor

			__lowerCAmelCase      :						str						      =				UnCLIPScheduler(
			    variance_type='fixed_small_log'	,		prediction_type='sample'	,		num_train_timesteps=10_00	,		clip_sample=_SCREAMING_SNAKE_CASE	,		clip_sample_range=10.0	,		)

			__lowerCAmelCase      :						Dict						      =				{
			    'prior': prior,
			    'image_encoder': image_encoder,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'scheduler': scheduler,
			    'image_processor': image_processor,
			}

			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						Dict						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Dict						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				{
			    'prompt': 'horse',
			    'generator': generator,
			    'guidance_scale': 4.0,
			    'num_inference_steps': 2,
			    'output_type': 'np',
			}
			return inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'cpu'

			__lowerCAmelCase      :						List[Any]						      =				self.get_dummy_components()

			__lowerCAmelCase      :						str						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				pipe.to(_SCREAMING_SNAKE_CASE			)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Union[str, Any]						      =				output.image_embeds

			__lowerCAmelCase      :						List[str]						      =				pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)	,		return_dict=_SCREAMING_SNAKE_CASE	,		)[0]

			__lowerCAmelCase      :						List[Any]						      =				image[0, -10:]
			__lowerCAmelCase      :						Any						      =				image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			__lowerCAmelCase      :						List[str]						      =				np.array(
			    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

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

		@skip_mps
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				torch_device == 'cpu'
			__lowerCAmelCase      :						List[str]						      =				True
			__lowerCAmelCase      :						int						      =				False

			self._test_inference_batch_single_identical(
			    test_max_difference=_SCREAMING_SNAKE_CASE	,		relax_max_difference=_SCREAMING_SNAKE_CASE	,		test_mean_pixel_difference=_SCREAMING_SNAKE_CASE	,		)







		@skip_mps
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				torch_device == 'cpu'
			__lowerCAmelCase      :						Optional[Any]						      =				False

			self._test_attention_slicing_forward_pass(
			    test_max_difference=_SCREAMING_SNAKE_CASE	,		test_mean_pixel_difference=_SCREAMING_SNAKE_CASE	,		)

"""simple docstring"""







import os
def    __lowerCAmelCase    (_UpperCamelCase = "input.txt"		):

	with open(os.path.join(os.path.dirname(_UpperCamelCase		)	,  _UpperCamelCase		)		) as input_file:
		__lowerCAmelCase      :						Optional[Any]						      =				[
		    [int(_UpperCamelCase		) for element in line.split(','		)]
		    for line in input_file.readlines()
		]

	__lowerCAmelCase      :						List[str]						      =				len(_UpperCamelCase		)
	__lowerCAmelCase      :						Tuple						      =				len(matrix[0]		)

	__lowerCAmelCase      :						int						      =				[[-1 for _ in range(_UpperCamelCase		)] for _ in range(_UpperCamelCase		)]
	for i in range(_UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				matrix[i][0]

	for j in range(1	,  _UpperCamelCase		):
		for i in range(_UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i - 1][j] + matrix[i][j]		)

		for i in range(rows - 2	,  -1	,  -1		):
			__lowerCAmelCase      :						List[str]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i + 1][j] + matrix[i][j]		)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


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