





'''simple docstring'''

import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__lowerCAmelCase				:      str						      ={
    "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,
}
__lowerCAmelCase				:      List[Any]						      ="ETAOINSHRDLCUMWFGYPBVKJXQZ"
__lowerCAmelCase				:      Union[str, Any]						      ="ABCDEFGHIJKLMNOPQRSTUVWXYZ"





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    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 		UpperCamelCase							(				_lowerCamelCase   :   tuple			):
    return x[0]





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

    A__							=     {}

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

    A__							=     list(freq_to_letter_str.items()			)
    freq_pairs.sort(key=_lowerCamelCase		,			reverse=_lowerCamelCase			)

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

    return "".join(_lowerCamelCase			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     get_frequency_order(_lowerCamelCase			)
    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()










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



