



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 				__A					( A_      ):
			UpperCamelCase					:List[Any]     =			['''image_processor''', '''tokenizer''']
			UpperCamelCase					:Tuple     =			'''ViltImageProcessor'''
			UpperCamelCase					:Optional[int]     =			('''BertTokenizer''', '''BertTokenizerFast''')





			def __init__(self							,    __magic_name__=None							,    __magic_name__=None							,    **__magic_name__				):
						lowerCamelCase__ :      str								=		None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""							,    __magic_name__							,    )
									lowerCamelCase__ :      Union[str, Any]								=		kwargs.pop("""feature_extractor"""				)

						lowerCamelCase__ :      List[Any]								=		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__(__magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		self.image_processor





			def __call__(self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = True							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = 0							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = True							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Any								=		self.tokenizer(
						    text=__magic_name__							,    add_special_tokens=__magic_name__							,    padding=__magic_name__							,    truncation=__magic_name__							,    max_length=__magic_name__							,    stride=__magic_name__							,    pad_to_multiple_of=__magic_name__							,    return_token_type_ids=__magic_name__							,    return_attention_mask=__magic_name__							,    return_overflowing_tokens=__magic_name__							,    return_special_tokens_mask=__magic_name__							,    return_offsets_mapping=__magic_name__							,    return_length=__magic_name__							,    verbose=__magic_name__							,    return_tensors=__magic_name__							,    **__magic_name__							,    )
						# add pixel_values + pixel_mask
						lowerCamelCase__ :      List[Any]								=		self.image_processor(__magic_name__							,    return_tensors=__magic_name__				)
						encoding.update(__magic_name__				)

						return encoding





			def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
						return self.tokenizer.batch_decode(*__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
						return self.tokenizer.decode(*__magic_name__							,    **__magic_name__				)





			@property
			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		self.tokenizer.model_input_names
						lowerCamelCase__ :      List[str]								=		self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





			@property
			def        _snake_case    (self				):
						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,    __magic_name__							,    )
						return self.image_processor_class





			@property
			def        _snake_case    (self				):
						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,    __magic_name__							,    )
						return self.image_processor








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



