



'''simple docstring'''

import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer



class 		lowerCamelCase__      (						tf.keras.layers.Layer     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Dict[str, int]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	int = None     ,  UpperCamelCase_				:	int = None					)  ->       Any:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Tuple       =		pad_token_id
						_lowercase					:       int       =		max_length
						_lowercase					:       List[Any]       =		vocab
						_lowercase					:       Tuple       =		merges
						_lowercase					:       int       =		BytePairTokenizer(UpperCamelCase_     ,  UpperCamelCase_     ,  sequence_length=UpperCamelCase_					)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Any     ,  UpperCamelCase_				:	GPTaTokenizer     ,  *UpperCamelCase_				:	List[str]     ,  **UpperCamelCase_				:	Union[str, Any]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		[' '.join(UpperCamelCase_					) for m in tokenizer.bpe_ranks.keys()]
						_lowercase					:       int       =		tokenizer.get_vocab()
						return cls(UpperCamelCase_     ,  UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	int     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  *UpperCamelCase_				:	Union[str, Any]     ,  **UpperCamelCase_				:	Dict					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       int       =		GPTaTokenizer.from_pretrained(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)
						return cls.from_tokenizer(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Any					)  ->       List[str]:



						'''simple docstring'''





						return cls(**UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						return {
						    "vocab": self.vocab,
						    "merges": self.merges,
						    "max_length": self.max_length,
						    "pad_token_id": self.pad_token_id,
						}




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



						'''simple docstring'''





						_lowercase					:       Any       =		self.tf_tokenizer(UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		tf.ones_like(UpperCamelCase_					)

						if self.pad_token_id is not None:
							# pad the tokens up to max length
							_lowercase					:       Optional[int]       =		max_length if max_length is not None else self.max_length

							if max_length is not None:
								_lowercase  ,    _lowercase					:       List[Any]       =		pad_model_inputs(
								    UpperCamelCase_     ,  max_seq_length=UpperCamelCase_     ,  pad_value=self.pad_token_id					)

						return {"attention_mask": attention_mask, "input_ids": input_ids}





'''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()
