



'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


_A				:    str     						={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A				:    str     						={
    '''vocab_file''': {
        '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''unc-nlp/lxmert-base-uncased''': (
            '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

_A				:    Any     						={
    '''unc-nlp/lxmert-base-uncased''': 5_1_2,
}

_A				:    List[str]     						={
    '''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						VOCAB_FILES_NAMES
					A_						    =						PRETRAINED_VOCAB_FILES_MAP
					A_						    =						PRETRAINED_INIT_CONFIGURATION
					A_						    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A_						    =						LxmertTokenizer




					def __init__(  self				:	int     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	Any=None     ,  UpperCamelCase_				:	List[Any]=True     ,  UpperCamelCase_				:	str="[UNK]"     ,  UpperCamelCase_				:	Optional[Any]="[SEP]"     ,  UpperCamelCase_				:	List[str]="[PAD]"     ,  UpperCamelCase_				:	int="[CLS]"     ,  UpperCamelCase_				:	List[Any]="[MASK]"     ,  UpperCamelCase_				:	List[Any]=True     ,  UpperCamelCase_				:	Optional[int]=None     ,  **UpperCamelCase_				:	Optional[Any]     ,  )  ->       Optional[int]:



						'''simple docstring'''





						super().__init__(
						    UpperCamelCase_     ,  tokenizer_file=UpperCamelCase_     ,  do_lower_case=UpperCamelCase_     ,  unk_token=UpperCamelCase_     ,  sep_token=UpperCamelCase_     ,  pad_token=UpperCamelCase_     ,  cls_token=UpperCamelCase_     ,  mask_token=UpperCamelCase_     ,  tokenize_chinese_chars=UpperCamelCase_     ,  strip_accents=UpperCamelCase_     ,  **UpperCamelCase_     ,  )

						_lowercase					:       Optional[int]       =		json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
						if (
						    normalizer_state.get('lowercase'     ,  UpperCamelCase_					) != do_lower_case
						    or normalizer_state.get('strip_accents'     ,  UpperCamelCase_					) != strip_accents
						    or normalizer_state.get('handle_chinese_chars'     ,  UpperCamelCase_					) != tokenize_chinese_chars
						):
							_lowercase					:       str       =		getattr(UpperCamelCase_     ,  normalizer_state.pop('type'					)					)
							_lowercase					:       Dict       =		do_lower_case
							_lowercase					:       Optional[int]       =		strip_accents
							_lowercase					:       List[Any]       =		tokenize_chinese_chars
							_lowercase					:       str       =		normalizer_class(**UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		do_lower_case




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Dict=None					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       int       =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	List[int]     ,  UpperCamelCase_				:	Optional[List[int]] = None					)  ->       List[int]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		[self.sep_token_id]
						_lowercase					:       Optional[Any]       =		[self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep					) * [0]
						return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[str] = None					)  ->       Tuple[str]:



						'''simple docstring'''





						_lowercase					:       str       =		self._tokenizer.model.save(UpperCamelCase_     ,  name=UpperCamelCase_					)
						return tuple(UpperCamelCase_					)





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       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:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

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

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

    # 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 						__UpperCamelCase				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       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(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	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()
