



"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  SCREAMING_SNAKE_CASE__   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   ['''pixel_values''']





	def __init__(			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any] = True    ,					_SCREAMING_SNAKE_CASE							:					Any = None    ,					_SCREAMING_SNAKE_CASE							:					Tuple = True    ,					_SCREAMING_SNAKE_CASE							:					Tuple = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any] = PILImageResampling.BICUBIC    ,					_SCREAMING_SNAKE_CASE							:					Any = True    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any] = 1 / 255    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any] = True    ,					_SCREAMING_SNAKE_CASE							:					List[Any] = None    ,					_SCREAMING_SNAKE_CASE							:					int = None    ,					**_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					)	->  None:





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    crop_size if crop_size is not None else {'height': 256, 'width': 256}
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    get_size_dict(_SCREAMING_SNAKE_CASE    ,					param_name='crop_size'						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    size if size is not None else {'height': 224, 'width': 224}
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    get_size_dict(_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Any 							=    do_center_crop
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    crop_size
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    do_resize
					SCREAMING_SNAKE_CASE	:       Tuple 							=    size
					SCREAMING_SNAKE_CASE	:       Tuple 							=    resample
					SCREAMING_SNAKE_CASE	:       int 							=    do_rescale
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    rescale_factor
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    do_normalize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					SCREAMING_SNAKE_CASE	:       Tuple 							=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Tuple = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int] = None    ,					**_SCREAMING_SNAKE_CASE							:					Any    ,					)	->  np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.size if size is None else size
					SCREAMING_SNAKE_CASE	:       Tuple 							=    get_size_dict(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    get_size_dict(_SCREAMING_SNAKE_CASE    ,					param_name='crop_size'						)

					SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       List[str] 							=    get_image_size(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       int 							=    min(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (size['height'] / crop_size['height']) * min_dim
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    (size['width'] / crop_size['width']) * min_dim
					return center_crop(_SCREAMING_SNAKE_CASE    ,					size=(cropped_height, cropped_width)    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					str = PIL.Image.BICUBIC    ,					_SCREAMING_SNAKE_CASE							:					List[str] = None    ,					**_SCREAMING_SNAKE_CASE							:					str    ,					)	->  np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    get_size_dict(_SCREAMING_SNAKE_CASE						)
					if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"""						)
					return resize(
					    _SCREAMING_SNAKE_CASE    ,					size=(size['height'], size['width'])    ,					resample=_SCREAMING_SNAKE_CASE    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Dict = None    ,					**_SCREAMING_SNAKE_CASE							:					List[str]    ,					)	->  Dict:





					"""simple docstring"""


					return rescale(_SCREAMING_SNAKE_CASE    ,					scale=_SCREAMING_SNAKE_CASE    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					Dict = None    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					)	->  np.ndarray:





					"""simple docstring"""


					return normalize(_SCREAMING_SNAKE_CASE    ,					mean=_SCREAMING_SNAKE_CASE    ,					std=_SCREAMING_SNAKE_CASE    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					int = None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any] = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any] = None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any] = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int] = None    ,					_SCREAMING_SNAKE_CASE							:					str = None    ,					_SCREAMING_SNAKE_CASE							:					List[str] = None    ,					_SCREAMING_SNAKE_CASE							:					Any = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int] = None    ,					_SCREAMING_SNAKE_CASE							:					Dict = None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any] = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any] = ChannelDimension.FIRST    ,					**_SCREAMING_SNAKE_CASE							:					str    ,					)	->  PIL.Image.Image:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    do_center_crop if do_center_crop is not None else self.do_center_crop
					SCREAMING_SNAKE_CASE	:       int 							=    crop_size if crop_size is not None else self.crop_size
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    get_size_dict(_SCREAMING_SNAKE_CASE    ,					param_name='crop_size'						)
					SCREAMING_SNAKE_CASE	:       int 							=    do_resize if do_resize is not None else self.do_resize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    size if size is not None else self.size
					SCREAMING_SNAKE_CASE	:       Dict 							=    get_size_dict(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    resample if resample is not None else self.resample
					SCREAMING_SNAKE_CASE	:       List[str] 							=    do_rescale if do_rescale is not None else self.do_rescale
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    rescale_factor if rescale_factor is not None else self.rescale_factor
					SCREAMING_SNAKE_CASE	:       Dict 							=    do_normalize if do_normalize is not None else self.do_normalize
					SCREAMING_SNAKE_CASE	:       str 							=    image_mean if image_mean is not None else self.image_mean
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    image_std if image_std is not None else self.image_std

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    make_list_of_images(_SCREAMING_SNAKE_CASE						)

					if not valid_images(_SCREAMING_SNAKE_CASE						):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

					if do_center_crop and crop_size is None:
									raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'						)

					if do_resize and size is None:
									raise ValueError('Size must be specified if do_resize is True.'						)

					if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

					if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'						)

					# All transformations expect numpy arrays.
					SCREAMING_SNAKE_CASE	:       List[str] 							=    [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

					if do_center_crop:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.center_crop(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					size=_SCREAMING_SNAKE_CASE						) for image in images]

					if do_resize:
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    [self.resize(image=_SCREAMING_SNAKE_CASE    ,					size=_SCREAMING_SNAKE_CASE    ,					resample=_SCREAMING_SNAKE_CASE						) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE	:       Dict 							=    [self.rescale(image=_SCREAMING_SNAKE_CASE    ,					scale=_SCREAMING_SNAKE_CASE						) for image in images]

					if do_normalize:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.normalize(image=_SCREAMING_SNAKE_CASE    ,					mean=_SCREAMING_SNAKE_CASE    ,					std=_SCREAMING_SNAKE_CASE						) for image in images]

					SCREAMING_SNAKE_CASE	:       str 							=    [to_channel_dimension_format(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) for image in images]

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {'pixel_values': images}
					return BatchFeature(data=_SCREAMING_SNAKE_CASE    ,					tensor_type=_SCREAMING_SNAKE_CASE						)










"""simple docstring"""

def    __snake_case       (  __A			:	str       ,		__A			:	str    )    ->					int:






				'''simple docstring'''
				# The longer word should come first
				if len(__A    ) < len(__A    ):
								return levenshtein_distance(__A       ,		__A    )

				if len(__A    ) == 0:
								return len(__A    )

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    list(range(len(__A    ) + 1    )    )

				for i, ca in enumerate(__A    ):
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [i + 1]

								for j, ca in enumerate(__A    ):
												# Calculate insertions, deletions and substitutions
												SCREAMING_SNAKE_CASE	:       str 							=    previous_row[j + 1] + 1
												SCREAMING_SNAKE_CASE	:       List[str] 							=    current_row[j] + 1
												SCREAMING_SNAKE_CASE	:       Any 							=    previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(__A       ,		__A       ,		__A    )    )

								# Store the previous row
								SCREAMING_SNAKE_CASE	:       Tuple 							=    current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
							A_	:						Optional[int]				=			input('Enter the first word:\n').strip()
							A_	:						Optional[Any]				=			input('Enter the second word:\n').strip()

							A_	:						str				=			levenshtein_distance(first_word, second_word)
							print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')





