


'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ChineseCLIPImageProcessor







class 		_snake_case					(     unittest.TestCase	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=7							,							_lowerCamelCase=3							,							_lowerCamelCase=18							,							_lowerCamelCase=30							,							_lowerCamelCase=400							,							_lowerCamelCase=True							,							_lowerCamelCase=None							,							_lowerCamelCase=True							,							_lowerCamelCase=None							,							_lowerCamelCase=True							,							_lowerCamelCase=[0.48145466, 0.4578275, 0.40821073]							,							_lowerCamelCase=[0.26862954, 0.26130258, 0.27577711]							,							_lowerCamelCase=True							,							):
								UpperCAmelCase__  :					int        =     size if size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase__  :					Optional[int]        =     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								UpperCAmelCase__  :					Union[str, Any]        =     parent
								UpperCAmelCase__  :					str        =     batch_size
								UpperCAmelCase__  :					List[Any]        =     num_channels
								UpperCAmelCase__  :					str        =     image_size
								UpperCAmelCase__  :					Tuple        =     min_resolution
								UpperCAmelCase__  :					str        =     max_resolution
								UpperCAmelCase__  :					Union[str, Any]        =     do_resize
								UpperCAmelCase__  :					int        =     size
								UpperCAmelCase__  :					int        =     do_center_crop
								UpperCAmelCase__  :					Dict        =     crop_size
								UpperCAmelCase__  :					List[str]        =     do_normalize
								UpperCAmelCase__  :					Optional[Any]        =     image_mean
								UpperCAmelCase__  :					Optional[int]        =     image_std
								UpperCAmelCase__  :					List[str]        =     do_convert_rgb




					def 				snake_case__						(      self):
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}




					def 				snake_case__						(      self							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							_lowerCamelCase=False):
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
											UpperCAmelCase__  :					Any        =     []
											for i in range(self.batch_size):
														image_inputs.append(
														    np.random.randint(
														        255							,							size=(self.num_channels, self.max_resolution, self.max_resolution)							,							dtype=np.uinta))
								else:
											UpperCAmelCase__  :					str        =     []
											for i in range(self.batch_size):
														UpperCAmelCase__   ,       UpperCAmelCase__  :					List[Any]        =     np.random.choice(np.arange(self.min_resolution							,							self.max_resolution)							,							2)
														image_inputs.append(np.random.randint(255							,							size=(self.num_channels, width, height)							,							dtype=np.uinta))

								if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											UpperCAmelCase__  :					Tuple        =     [Image.fromarray(np.moveaxis(_lowerCamelCase							,							0							,							-1)) for x in image_inputs]

								if torchify:
											UpperCAmelCase__  :					Union[str, Any]        =     [torch.from_numpy(_lowerCamelCase) for x in image_inputs]

								return image_inputs







@require_torch
@require_vision
class 		_snake_case					(     a__       ,   unittest.TestCase	):
					lowerCAmelCase      :int   =					ChineseCLIPImageProcessor if is_vision_available() else None




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     ChineseCLIPImageProcessingTester(self							,							do_center_crop=_lowerCamelCase)




					@property
					def 				snake_case__						(      self):
								return self.image_processor_tester.prepare_image_processor_dict()




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_resize"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""size"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_center_crop"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""center_crop"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_normalize"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""image_mean"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""image_std"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_convert_rgb"""))




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size							,							{"""height""": 224, """width""": 224})
								self.assertEqual(image_processor.crop_size							,							{"""height""": 18, """width""": 18})

								UpperCAmelCase__  :					int        =     self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84)
								self.assertEqual(image_processor.size							,							{"""shortest_edge""": 42})
								self.assertEqual(image_processor.crop_size							,							{"""height""": 84, """width""": 84})




					def 				snake_case__						(      self):
								pass




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase__  :					Tuple        =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase)
								for image in image_inputs:
											self.assertIsInstance(_lowerCamelCase							,							Image.Image)

								# Test not batched input
								UpperCAmelCase__  :					List[str]        =     image_processing(image_inputs[0]							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)

								# Test batched
								UpperCAmelCase__  :					List[str]        =     image_processing(_lowerCamelCase							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase__  :					List[str]        =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase							,							numpify=_lowerCamelCase)
								for image in image_inputs:
											self.assertIsInstance(_lowerCamelCase							,							np.ndarray)

								# Test not batched input
								UpperCAmelCase__  :					Tuple        =     image_processing(image_inputs[0]							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)

								# Test batched
								UpperCAmelCase__  :					List[Any]        =     image_processing(_lowerCamelCase							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase__  :					Dict        =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase							,							torchify=_lowerCamelCase)
								for image in image_inputs:
											self.assertIsInstance(_lowerCamelCase							,							torch.Tensor)

								# Test not batched input
								UpperCAmelCase__  :					Dict        =     image_processing(image_inputs[0]							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)

								# Test batched
								UpperCAmelCase__  :					Optional[int]        =     image_processing(_lowerCamelCase							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)







@require_torch
@require_vision
class 		_snake_case					(     a__       ,   unittest.TestCase	):
					lowerCAmelCase      :str   =					ChineseCLIPImageProcessor if is_vision_available() else None




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     ChineseCLIPImageProcessingTester(self							,							num_channels=4							,							do_center_crop=_lowerCamelCase)
								UpperCAmelCase__  :					int        =     3




					@property
					def 				snake_case__						(      self):
								return self.image_processor_tester.prepare_image_processor_dict()




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_resize"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""size"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_center_crop"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""center_crop"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_normalize"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""image_mean"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""image_std"""))
								self.assertTrue(hasattr(_lowerCamelCase							,							"""do_convert_rgb"""))




					def 				snake_case__						(      self):
								pass




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase__  :					Optional[int]        =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase)
								for image in image_inputs:
											self.assertIsInstance(_lowerCamelCase							,							Image.Image)

								# Test not batched input
								UpperCAmelCase__  :					Any        =     image_processing(image_inputs[0]							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)

								# Test batched
								UpperCAmelCase__  :					Union[str, Any]        =     image_processing(_lowerCamelCase							,							return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape							,							(
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )							,							)



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     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() = }""")