



'''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 						_UpperCamelCase  (							tf.keras.layers.Layer    ):


			'''simple docstring'''

			def __init__(							self     : str							,     lowerCAmelCase__     : Dict[str, int]							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : int = None							,     lowerCAmelCase__     : int = None						):





					"""simple docstring"""






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

			@classmethod
			def UpperCamelCase__					(							cls     : List[Any]							,     lowerCAmelCase__     : GPTaTokenizer							,     *lowerCAmelCase__     : Union[str, Any]							,     **lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		[""" """.join(lowerCAmelCase__						) for m in tokenizer.bpe_ranks.keys()]
					__SCREAMING_SNAKE_CASE       :		Dict           =		tokenizer.get_vocab()
					return cls(lowerCAmelCase__							,     lowerCAmelCase__							,     *lowerCAmelCase__							,     **lowerCAmelCase__						)

			@classmethod
			def UpperCamelCase__					(							cls     : Optional[Any]							,     lowerCAmelCase__     : Union[str, os.PathLike]							,     *lowerCAmelCase__     : List[Any]							,     **lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		GPTaTokenizer.from_pretrained(lowerCAmelCase__							,     *lowerCAmelCase__							,     **lowerCAmelCase__						)
					return cls.from_tokenizer(lowerCAmelCase__							,     *lowerCAmelCase__							,     **lowerCAmelCase__						)

			@classmethod
			def UpperCamelCase__					(							cls     : Union[str, Any]							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					return cls(**lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : 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     : Optional[int]							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : int = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		self.tf_tokenizer(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		tf.ones_like(lowerCAmelCase__						)

					if self.pad_token_id is not None:
							# pad the tokens up to max length
							__SCREAMING_SNAKE_CASE       :		Dict           =		max_length if max_length is not None else self.max_length

							if max_length is not None:
									__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		pad_model_inputs(
									    lowerCAmelCase__							,     max_seq_length=lowerCAmelCase__							,     pad_value=self.pad_token_id						)

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




'''simple docstring'''




UpperCamelCase__       :	Optional[Any]    = [
    (10_00, '''M'''),
    (9_00, '''CM'''),
    (5_00, '''D'''),
    (4_00, '''CD'''),
    (1_00, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		__SCREAMING_SNAKE_CASE       :		str           =		0
		while place < len(_lowerCamelCase					):
				if (place + 1 < len(_lowerCamelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		Any           =		[]
		for arabic, roman in ROMAN:
				((__SCREAMING_SNAKE_CASE)       ,						(__SCREAMING_SNAKE_CASE))       :		str           =		divmod(_lowerCamelCase       ,      _lowerCamelCase					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()