






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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


UpperCamelCase 				=	logging.get_logger(__name__)


if is_vision_available():
		import PIL



class       __UpperCAmelCase		(snake_case_    ):
			__snake_case				:      Dict 						=							["pixel_values"]







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







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  size if size is not None else {'shortest_edge': 224}
									_SCREAMING_SNAKE_CASE	      =  get_size_dict(UpperCAmelCase_      ,	default_to_square=UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_SCREAMING_SNAKE_CASE	      =  get_size_dict(UpperCAmelCase_      ,	default_to_square=UpperCAmelCase_      ,	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 OPENAI_CLIP_MEAN
									_SCREAMING_SNAKE_CASE	      =  image_std if image_std is not None else OPENAI_CLIP_STD
									_SCREAMING_SNAKE_CASE	      =  do_convert_rgb







			def 							UpperCamelCase      (	self:  Union[str, Any]      ,	UpperCAmelCase_:  np.ndarray      ,	UpperCAmelCase_:  Dict[str, int]      ,	UpperCAmelCase_:  PILImageResampling = PILImageResampling.BICUBIC      ,	UpperCAmelCase_:  Optional[Union[str, ChannelDimension]] = None      ,	**UpperCAmelCase_:  Union[str, Any]      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  get_size_dict(UpperCAmelCase_      ,	default_to_square=UpperCAmelCase_     )
									if "shortest_edge" not in size:
															raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'     )
									_SCREAMING_SNAKE_CASE	      =  get_resize_output_image_size(UpperCAmelCase_      ,	size=size["""shortest_edge"""]      ,	default_to_square=UpperCAmelCase_     )
									return resize(UpperCAmelCase_      ,	size=UpperCAmelCase_      ,	resample=UpperCAmelCase_      ,	data_format=UpperCAmelCase_      ,	**UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  List[str]      ,	UpperCAmelCase_:  np.ndarray      ,	UpperCAmelCase_:  Dict[str, int]      ,	UpperCAmelCase_:  Optional[Union[str, ChannelDimension]] = None      ,	**UpperCAmelCase_:  List[str]      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  get_size_dict(UpperCAmelCase_     )
									if "height" not in size or "width" not in size:
															raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'     )
									return center_crop(UpperCAmelCase_      ,	size=(size["""height"""], size["""width"""])      ,	data_format=UpperCAmelCase_      ,	**UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Dict      ,	UpperCAmelCase_:  np.ndarray      ,	UpperCAmelCase_:  Union[int, float]      ,	UpperCAmelCase_:  Optional[Union[str, ChannelDimension]] = None      ,	**UpperCAmelCase_:  List[str]      ,	):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  str      ,	UpperCAmelCase_:  np.ndarray      ,	UpperCAmelCase_:  Union[float, List[float]]      ,	UpperCAmelCase_:  Union[float, List[float]]      ,	UpperCAmelCase_:  Optional[Union[str, ChannelDimension]] = None      ,	**UpperCAmelCase_:  List[Any]      ,	):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Union[str, Any]      ,	UpperCAmelCase_:  ImageInput      ,	UpperCAmelCase_:  bool = None      ,	UpperCAmelCase_:  Dict[str, int] = None      ,	UpperCAmelCase_:  PILImageResampling = None      ,	UpperCAmelCase_:  bool = None      ,	UpperCAmelCase_:  int = None      ,	UpperCAmelCase_:  bool = None      ,	UpperCAmelCase_:  float = None      ,	UpperCAmelCase_:  bool = None      ,	UpperCAmelCase_:  Optional[Union[float, List[float]]] = None      ,	UpperCAmelCase_:  Optional[Union[float, List[float]]] = None      ,	UpperCAmelCase_:  bool = None      ,	UpperCAmelCase_:  Optional[Union[str, TensorType]] = None      ,	UpperCAmelCase_:  Optional[ChannelDimension] = ChannelDimension.FIRST      ,	**UpperCAmelCase_:  str      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  do_resize if do_resize is not None else self.do_resize
									_SCREAMING_SNAKE_CASE	      =  size if size is not None else self.size
									_SCREAMING_SNAKE_CASE	      =  get_size_dict(UpperCAmelCase_      ,	param_name="""size"""      ,	default_to_square=UpperCAmelCase_     )
									_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	      =  crop_size if crop_size is not None else self.crop_size
									_SCREAMING_SNAKE_CASE	      =  get_size_dict(UpperCAmelCase_      ,	param_name="""crop_size"""      ,	default_to_square=UpperCAmelCase_     )
									_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	      =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									_SCREAMING_SNAKE_CASE	      =  make_list_of_images(UpperCAmelCase_     )

									if not valid_images(UpperCAmelCase_     ):
															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:
															raise ValueError("""Size 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."""     )

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															_SCREAMING_SNAKE_CASE	      =  [convert_to_rgb(UpperCAmelCase_     ) for image in images]

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

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

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

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

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

									_SCREAMING_SNAKE_CASE	      =  [to_channel_dimension_format(UpperCAmelCase_      ,	UpperCAmelCase_     ) for image in images]

									_SCREAMING_SNAKE_CASE	      =  {'pixel_values': images}
									return BatchFeature(data=UpperCAmelCase_      ,	tensor_type=UpperCAmelCase_     )


def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):

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

  return "".join(lowerCamelCase	)

 # matching characters
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					len(lowerCamelCase	)

 # transposition
 UpperCamelCase_					:						int =					(
     len([(ca, ca) for ca, ca in zip(lowerCamelCase  ,  lowerCamelCase	) if ca != ca]	) // 2
 )

 if not match_count:
  UpperCamelCase_					:						Union[str, Any] =					0.0
 else:
  UpperCamelCase_					:						str =					(
      1
      / 3
      * (
          match_count / len(lowerCamelCase	)
          + match_count / len(lowerCamelCase	)
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 UpperCamelCase_					:						Dict =					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'))



