
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 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, logging


_lowerCamelCase        =logging.get_logger(__name__)





class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   List[str]   	=      ["""pixel_values"""]
      def __init__(				self   ,  __magic_name__ = True   ,  __magic_name__ = None   ,  __magic_name__ = PILImageResampling.BILINEAR   ,  __magic_name__ = True   ,  __magic_name__ = 1 / 2_5_5   ,  __magic_name__ = True   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  **__magic_name__   ,  ):
       super().__init__(**__magic_name__				)
       lowerCamelCase		:     int          =				size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       lowerCamelCase		:     Tuple          =				get_size_dict(__magic_name__				)
       lowerCamelCase		:     Any          =				do_resize
       lowerCamelCase		:     Tuple          =				do_rescale
       lowerCamelCase		:     Union[str, Any]          =				do_normalize
       lowerCamelCase		:     Union[str, Any]          =				size
       lowerCamelCase		:     Union[str, Any]          =				resample
       lowerCamelCase		:     Optional[int]          =				rescale_factor
       lowerCamelCase		:     int          =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase		:     Dict          =				image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = PILImageResampling.BILINEAR   ,  __magic_name__ = None   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     Tuple          =				get_size_dict(__magic_name__				)
       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(
           __magic_name__   ,  size=(size["""height"""], size["""width"""])   ,  resample=__magic_name__   ,  data_format=__magic_name__   ,  **__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = None   ,  **__magic_name__				):
       return rescale(__magic_name__   ,  scale=__magic_name__   ,  data_format=__magic_name__   ,  **__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = None   ,  **__magic_name__   ,  ):
       return normalize(__magic_name__   ,  mean=__magic_name__   ,  std=__magic_name__   ,  data_format=__magic_name__   ,  **__magic_name__				)






      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = ChannelDimension.FIRST   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     Optional[Any]          =				do_resize if do_resize is not None else self.do_resize
       lowerCamelCase		:     List[str]          =				do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase		:     int          =				do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase		:     str          =				resample if resample is not None else self.resample
       lowerCamelCase		:     List[str]          =				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase		:     List[Any]          =				image_mean if image_mean is not None else self.image_mean
       lowerCamelCase		:     int          =				image_std if image_std is not None else self.image_std

       lowerCamelCase		:     Tuple          =				size if size is not None else self.size
       lowerCamelCase		:     Optional[Any]          =				get_size_dict(__magic_name__				)

       lowerCamelCase		:     Tuple          =				make_list_of_images(__magic_name__				)

       if not valid_images(__magic_name__				):
        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_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

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

       if do_resize:
        lowerCamelCase		:     Optional[int]          =				[self.resize(image=__magic_name__   ,  size=__magic_name__   ,  resample=__magic_name__				) for image in images]

       if do_rescale:
        lowerCamelCase		:     Optional[Any]          =				[self.rescale(image=__magic_name__   ,  scale=__magic_name__				) for image in images]

       if do_normalize:
        lowerCamelCase		:     Optional[int]          =				[self.normalize(image=__magic_name__   ,  mean=__magic_name__   ,  std=__magic_name__				) for image in images]

       lowerCamelCase		:     List[Any]          =				[to_channel_dimension_format(__magic_name__   ,  __magic_name__				) for image in images]

       lowerCamelCase		:     Dict          =				{"""pixel_values""": images}
       return BatchFeature(data=__magic_name__   ,  tensor_type=__magic_name__				)






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # Creating a dictionary containing count of each word
 for word in sentence.split():
  occurrence[word] += 1
 return occurrence


if __name__ == "__main__":
      for word, count in word_occurrence("""INPUT STRING""").items():
            print(f'''{word}: {count}''')




