


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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    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


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







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""pixel_values"""]






							def __init__(      self			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = PILImageResampling.BICUBIC			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = 1 / 2_5_5			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
										SCREAMING_SNAKE_CASE_		:     Any					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					do_resize
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					resample
										SCREAMING_SNAKE_CASE_		:     str					=					do_rescale
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					rescale_factor
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_normalize
										SCREAMING_SNAKE_CASE_		:     Any					=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										SCREAMING_SNAKE_CASE_		:     int					=					image_std if image_std is not None else OPENAI_CLIP_STD
										SCREAMING_SNAKE_CASE_		:     Any					=					do_convert_rgb






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = PILImageResampling.BICUBIC			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )
										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()}'''       )
										SCREAMING_SNAKE_CASE_		:     Any					=					(size['height'], size['width'])
										return resize(lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = ChannelDimension.FIRST			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE_		:     Tuple					=					resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_rescale if do_rescale is not None else self.do_rescale
										SCREAMING_SNAKE_CASE_		:     int					=					rescale_factor if rescale_factor is not None else self.rescale_factor
										SCREAMING_SNAKE_CASE_		:     Any					=					do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE_		:     int					=					image_mean if image_mean is not None else self.image_mean
										SCREAMING_SNAKE_CASE_		:     str					=					image_std if image_std is not None else self.image_std
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										SCREAMING_SNAKE_CASE_		:     List[str]					=					size if size is not None else self.size
										SCREAMING_SNAKE_CASE_		:     List[str]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					make_list_of_images(lowerCAmelCase__       )

										if not valid_images(lowerCAmelCase__       ):
													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_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 std must be specified if do_normalize is True.'       )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[convert_to_rgb(lowerCAmelCase__       ) for image in images]

										# All transformations expect numpy arrays.
										SCREAMING_SNAKE_CASE_		:     Any					=					[to_numpy_array(lowerCAmelCase__       ) for image in images]

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

										if do_rescale:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[self.rescale(image=lowerCAmelCase__			,						scale=lowerCAmelCase__       ) for image in images]

										if do_normalize:
													SCREAMING_SNAKE_CASE_		:     Dict					=					[self.normalize(image=lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     Any					=					[to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     str					=					BatchFeature(data={'pixel_values': images}			,						tensor_type=lowerCAmelCase__       )

										return encoded_outputs









def 						a__			(				A__,      A__				):

			def get_matched_characters(A__,      A__				) -> str:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[]
						SCREAMING_SNAKE_CASE_		:     Any					=					min(len(_stra				),      len(_stra				)				) // 2
						for i, l in enumerate(_stra				):
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(max(0,      i - limit				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(min(i + limit + 1,      len(_stra				)				)				)
									if l in _stra[left:right]:
												matched.append(A__				)
												SCREAMING_SNAKE_CASE_		:     List[str]					=					F'''{_stra[0:_stra.index(A__				)]} {_stra[_stra.index(A__				) + 1:]}'''

						return "".join(A__				)

			# matching characters
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					len(A__				)

			# transposition
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    len([(ca, ca) for ca, ca in zip(A__,      A__				) if ca != ca]				) // 2
			)

			if not match_count:
						SCREAMING_SNAKE_CASE_		:     Dict					=					0.0
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
						    1
						    / 3
						    * (
						        match_count / len(A__				)
						        + match_count / len(A__				)
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			SCREAMING_SNAKE_CASE_		:     List[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'))





