





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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    ["pixel_values"]






     def __init__(							self		:					int		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					PILImageResampling = PIL.Image.BICUBIC		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					Union[int, float] = 1 / 255		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,**lowerCamelCase__		:					Optional[int]		,)  ->	None:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		size if size is not None else {"""height""": 256, """width""": 256}
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,param_name="""crop_size""" )

           SCREAMING_SNAKE_CASE	=		do_resize
           SCREAMING_SNAKE_CASE	=		size
           SCREAMING_SNAKE_CASE	=		resample
           SCREAMING_SNAKE_CASE	=		do_center_crop
           SCREAMING_SNAKE_CASE	=		crop_size
           SCREAMING_SNAKE_CASE	=		do_rescale
           SCREAMING_SNAKE_CASE	=		rescale_factor
           SCREAMING_SNAKE_CASE	=		do_normalize
           SCREAMING_SNAKE_CASE	=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           SCREAMING_SNAKE_CASE	=		image_std if image_std is not None else IMAGENET_STANDARD_STD






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Dict[str, int]		,lowerCamelCase__		:					PILImageResampling = PIL.Image.BICUBIC		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					str		,)  ->	np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__ )
           if "height" not in size or "width" not in size:
                 raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
           return resize(
               lowerCamelCase__		,size=(size["""height"""], size["""width"""])		,resample=lowerCamelCase__		,data_format=lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Dict[str, int]		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					Optional[int]		,)  ->	np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__ )
           if "height" not in size or "width" not in size:
                 raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
           return center_crop(lowerCamelCase__		,size=(size["""height"""], size["""width"""])		,data_format=lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Union[int, float]		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					Tuple		,)  ->	Dict:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Union[float, List[float]]		,lowerCamelCase__		:					Union[float, List[float]]		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					int		,)  ->	np.ndarray:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					ImageInput		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					Optional[Any]=None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					float = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[str, TensorType]] = None		,lowerCamelCase__		:					ChannelDimension = ChannelDimension.FIRST		,**lowerCamelCase__		:					Optional[int]		,)  ->	PIL.Image.Image:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		do_resize if do_resize is not None else self.do_resize
           SCREAMING_SNAKE_CASE	=		resample if resample is not None else self.resample
           SCREAMING_SNAKE_CASE	=		do_center_crop if do_center_crop is not None else self.do_center_crop
           SCREAMING_SNAKE_CASE	=		do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE	=		rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE	=		do_normalize if do_normalize is not None else self.do_normalize
           SCREAMING_SNAKE_CASE	=		image_mean if image_mean is not None else self.image_mean
           SCREAMING_SNAKE_CASE	=		image_std if image_std is not None else self.image_std

           SCREAMING_SNAKE_CASE	=		size if size is not None else self.size
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		crop_size if crop_size is not None else self.crop_size
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,param_name="""crop_size""" )

           SCREAMING_SNAKE_CASE	=		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_center_crop and crop_size is None:
                 raise ValueError("""Crop size must be specified if do_center_crop 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.""" )

           # All transformations expect numpy arrays.
           SCREAMING_SNAKE_CASE	=		[to_numpy_array(lowerCamelCase__ ) for image in images]

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

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

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

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

           SCREAMING_SNAKE_CASE	=		[to_channel_dimension_format(lowerCamelCase__		,lowerCamelCase__ ) for image in images]

           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": images}
           return BatchFeature(data=lowerCamelCase__		,tensor_type=lowerCamelCase__ )









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		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"""))


