"""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 (
    center_crop,
    get_resize_output_image_size,
    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__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
	_snake_case   :			Optional[Any]           =		["""pixel_values"""]


	def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								super().__init__(**lowerCamelCase		)
								__a     				=  size if size is not None else {"shortest_edge": 384}
								__a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

								__a     				=  do_resize
								__a     				=  size
								# Default value set here for backwards compatibility where the value in config is None
								__a     				=  crop_pct if crop_pct is not None else 224 / 256
								__a     				=  resample
								__a     				=  do_rescale
								__a     				=  rescale_factor
								__a     				=  do_normalize
								__a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								__a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
								if "shortest_edge" not in size:
															raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
								__a     				=  size["shortest_edge"]

								if shortest_edge < 384:
															# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
															__a     				=  int(shortest_edge / crop_pct		)
															__a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
															__a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
															# then crop to (shortest_edge, shortest_edge)
															return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
								else:
															# warping (no cropping) when evaluated at 384 or larger
															return resize(
															    lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






	def 		a__ (				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  ,						):
								__a     				=  do_resize if do_resize is not None else self.do_resize
								__a     				=  crop_pct if crop_pct is not None else self.crop_pct
								__a     				=  resample if resample is not None else self.resample
								__a     				=  do_rescale if do_rescale is not None else self.do_rescale
								__a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
								__a     				=  do_normalize if do_normalize is not None else self.do_normalize
								__a     				=  image_mean if image_mean is not None else self.image_mean
								__a     				=  image_std if image_std is not None else self.image_std

								__a     				=  size if size is not None else self.size
								__a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

								__a     				=  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_resize and size["shortest_edge"] < 384 and crop_pct is None:
															raise ValueError("crop_pct must be specified if size < 384."		)

								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.
								__a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

								if do_resize:
															__a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

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

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

								__a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

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

"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE__:Any			=	{
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
SCREAMING_SNAKE_CASE__:Optional[int]			=	"""ETAOINSHRDLCUMWFGYPBVKJXQZ"""
SCREAMING_SNAKE_CASE__:Tuple			=	"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def 					_lowerCamelCase(					a							):
       __a     				=  {letter: 0 for letter in string.ascii_uppercase}
       for letter in message.upper():
              if letter in LETTERS:
                     letter_count[letter] += 1

       return letter_count



def 					_lowerCamelCase(					a							):
       return x[0]



def 					_lowerCamelCase(					a							):
       __a     				=  get_letter_count(a							)
       __a     				=  {
           freq: [] for letter, freq in letter_to_freq.items()
       }
       for letter in LETTERS:
              freq_to_letter[letter_to_freq[letter]].append(a							)

       __a     				=  {}

       for freq in freq_to_letter:
              freq_to_letter[freq].sort(key=ETAOIN.find ,  reverse=a							)
              __a     				=  "".join(freq_to_letter[freq]							)

       __a     				=  list(freq_to_letter_str.items()							)
       freq_pairs.sort(key=a ,  reverse=a							)

       __a     				=  [freq_pair[1] for freq_pair in freq_pairs]

       return "".join(a							)



def 					_lowerCamelCase(					a							):
       __a     				=  get_frequency_order(a							)
       __a     				=  0
       for common_letter in ETAOIN[:6]:
              if common_letter in freq_order[:6]:
                     match_score += 1

       for uncommon_letter in ETAOIN[-6:]:
              if uncommon_letter in freq_order[-6:]:
                     match_score += 1

       return match_score


if __name__ == "__main__":
   import doctest

   doctest.testmod()
