






'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


A:      List[str]  				=				logging.get_logger(__name__)






def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			Optional[Any]:





						"""simple docstring"""
						lowercase_      :   Dict        =			b.T
						lowercase_      :   str        =			np.sum(np.square(a							)						,			axis=1							)
						lowercase_      :   Optional[Any]        =			np.sum(np.square(a							)						,			axis=0							)
						lowercase_      :   Optional[int]        =			np.matmul(a						,			a							)
						lowercase_      :   Any        =			aa[:, None] - 2 * ab + ba[None, :]
						return d






def 	_UpperCAmelCase    (  a		:	Dict						,			a		:	Tuple							)			->			Union[str, Any]:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			x.reshape(-1						,			3							)
						lowercase_      :   Union[str, Any]        =			squared_euclidean_distance(a						,			a							)
						return np.argmin(a						,			axis=1							)






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[Any]        =  ['pixel_values']
	def __init__(							self  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = None  ,      _lowercase = PILImageResampling.BILINEAR  ,      _lowercase = True  ,      _lowercase = True  ,      **_lowercase  ,      )						->  None:
							super().__init__(**_lowercase       )
							lowercase_      :   Optional[Any]        =			size if size is not None else {'height': 256, 'width': 256}
							lowercase_      :   Optional[Any]        =			get_size_dict(_lowercase       )
							lowercase_      :   List[str]        =			np.array(_lowercase       ) if clusters is not None else None
							lowercase_      :   Dict        =			do_resize
							lowercase_      :   Dict        =			size
							lowercase_      :   Optional[int]        =			resample
							lowercase_      :   Dict        =			do_normalize
							lowercase_      :   Tuple        =			do_color_quantize
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase = PILImageResampling.BILINEAR  ,      _lowercase = None  ,      **_lowercase  ,      )						->  np.ndarray:
							lowercase_      :   List[Any]        =			get_size_dict(_lowercase       )
							if "height" not in size or "width" not in size:
													raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"       )
							return resize(
							    _lowercase  ,      size=(size['height'], size['width'])  ,      resample=_lowercase  ,      data_format=_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None  ,      )						->  np.ndarray:
							lowercase_      :   Optional[Any]        =			rescale(image=_lowercase  ,      scale=1 / 1_27.5  ,      data_format=_lowercase       )
							lowercase_      :   Optional[Any]        =			image - 1
							return image







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = ChannelDimension.FIRST  ,      **_lowercase  ,      )						->  PIL.Image.Image:
							lowercase_      :   Optional[Any]        =			do_resize if do_resize is not None else self.do_resize
							lowercase_      :   Optional[Any]        =			size if size is not None else self.size
							lowercase_      :   Any        =			get_size_dict(_lowercase       )
							lowercase_      :   Any        =			resample if resample is not None else self.resample
							lowercase_      :   Tuple        =			do_normalize if do_normalize is not None else self.do_normalize
							lowercase_      :   Tuple        =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
							lowercase_      :   Union[str, Any]        =			clusters if clusters is not None else self.clusters
							lowercase_      :   Dict        =			np.array(_lowercase       )

							lowercase_      :   str        =			make_list_of_images(_lowercase       )

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

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

							if do_color_quantize and clusters is None:
													raise ValueError('Clusters must be specified if do_color_quantize is True.'       )

							# All transformations expect numpy arrays.
							lowercase_      :   Tuple        =			[to_numpy_array(_lowercase       ) for image in images]

							if do_resize:
													lowercase_      :   Dict        =			[self.resize(image=_lowercase  ,      size=_lowercase  ,      resample=_lowercase       ) for image in images]

							if do_normalize:
													lowercase_      :   str        =			[self.normalize(image=_lowercase       ) for image in images]

							if do_color_quantize:
													lowercase_      :   Tuple        =			[to_channel_dimension_format(_lowercase  ,      ChannelDimension.LAST       ) for image in images]
													# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
													lowercase_      :   List[Any]        =			np.array(_lowercase       )
													lowercase_      :   Optional[int]        =			color_quantize(_lowercase  ,      _lowercase       ).reshape(images.shape[:-1]       )

													# flatten to (batch_size, height*width)
													lowercase_      :   Optional[int]        =			images.shape[0]
													lowercase_      :   Optional[Any]        =			images.reshape(_lowercase  ,      -1       )

													# We need to convert back to a list of images to keep consistent behaviour across processors.
													lowercase_      :   List[str]        =			list(_lowercase       )
							else:
													lowercase_      :   List[str]        =			[to_channel_dimension_format(_lowercase  ,      _lowercase       ) for image in images]

							lowercase_      :   int        =			{'input_ids': images}
							return BatchFeature(data=_lowercase  ,      tensor_type=_lowercase       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			float:





						"""simple docstring"""

						def get_matched_characters(a		:	str						,			a		:	str							) -> str:
												lowercase_      :   Union[str, Any]        =			[]
												lowercase_      :   Tuple        =			min(len(_stra							)						,			len(_stra							)							) // 2
												for i, l in enumerate(_stra							):
																		lowercase_      :   Optional[int]        =			int(max(0						,			i - limit							)							)
																		lowercase_      :   Union[str, Any]        =			int(min(i + limit + 1						,			len(_stra							)							)							)
																		if l in _stra[left:right]:
																								matched.append(a							)
																								lowercase_      :   Union[str, Any]        =			f"{_stra[0:_stra.index(a							)]} {_stra[_stra.index(a							) + 1:]}"

												return "".join(a							)

						# matching characters
						lowercase_      :   Union[str, Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[int]        =			len(a							)

						# transposition
						lowercase_      :   Dict        =			(
						    len([(ca, ca) for ca, ca in zip(a						,			a							) if ca != ca]							) // 2
						)

						if not match_count:
												lowercase_      :   List[str]        =			0.0
						else:
												lowercase_      :   Any        =			(
												    1
												    / 3
												    * (
												        match_count / len(a							)
												        + match_count / len(a							)
												        + (match_count - transpositions) / match_count
												    )
												)

						# common prefix up to 4 characters
						lowercase_      :   Optional[Any]        =			0
						for ca, ca in zip(stra[:4]						,			stra[:4]							):
												if ca == ca:
																		prefix_len += 1
												else:
																		break

						return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler("hello", "world"))
