"""simple docstring"""







import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

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



				def __init__(self							: int      ,    _A							: List      ,    _A							: bool      ,    _A							: int = None      ,    _A							: int = None      ,    _A							: int = None      ,    _A							: str = "longest"      ,    _A							: bool = True      ,    _A							: int = 5_12      ,    _A							: int = None      ,    _A							: bool = True      ,    _A							: bool = True      ,    _A							: bool = True      ,    **_A							: List[str]      ,    )       ->      Optional[Any]:
					super().__init__()
					if use_fast_bert_tokenizer:
						__snake_case     :				List[str] 						=					FastBertTokenizer(
						    _A      ,    token_out_type=tf.intaa      ,    lower_case_nfd_strip_accents=_A      ,    **_A)
					else:
						__snake_case     :				Optional[Any] 						=					tf.lookup.StaticVocabularyTable(
						    tf.lookup.KeyValueTensorInitializer(
						        keys=_A      ,    key_dtype=tf.string      ,    values=tf.range(tf.size(_A      ,    out_type=tf.intaa)      ,    dtype=tf.intaa)      ,    value_dtype=tf.intaa      ,    )      ,    num_oov_buckets=1      ,    )
						__snake_case     :				Optional[Any] 						=					BertTokenizerLayer(
						    _A      ,    token_out_type=tf.intaa      ,    lower_case=_A      ,    **_A)

					__snake_case     :				Dict 						=					vocab_list
					__snake_case     :				Optional[int] 						=					do_lower_case
					__snake_case     :				Optional[Any] 						=					cls_token_id or vocab_list.index('[CLS]')
					__snake_case     :				List[str] 						=					sep_token_id or vocab_list.index('[SEP]')
					__snake_case     :				List[str] 						=					pad_token_id or vocab_list.index('[PAD]')
					__snake_case     :				str 						=					ShrinkLongestTrimmer(max_length - 3      ,    axis=1)  # Allow room for special tokens
					__snake_case     :				Any 						=					max_length
					__snake_case     :				Union[str, Any] 						=					padding
					__snake_case     :				Tuple 						=					truncation
					__snake_case     :				List[Any] 						=					pad_to_multiple_of
					__snake_case     :				Union[str, Any] 						=					return_token_type_ids
					__snake_case     :				str 						=					return_attention_mask



				@classmethod
				def    _lowercase	(cls							: str      ,    _A							: "PreTrainedTokenizerBase"      ,    **_A							: Optional[Any])       ->      Optional[Any]:  # noqa: F821
					__snake_case     :				Any 						=					kwargs.pop('do_lower_case'      ,    _A)
					__snake_case     :				Tuple 						=					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
					__snake_case     :				Tuple 						=					kwargs.pop('cls_token_id'      ,    _A)
					__snake_case     :				List[str] 						=					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
					__snake_case     :				Dict 						=					kwargs.pop('sep_token_id'      ,    _A)
					__snake_case     :				Union[str, Any] 						=					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
					__snake_case     :				str 						=					kwargs.pop('pad_token_id'      ,    _A)
					__snake_case     :				Optional[int] 						=					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

					__snake_case     :				List[Any] 						=					tokenizer.get_vocab()
					__snake_case     :				List[str] 						=					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]      ,    key=lambda _A: x[1])
					__snake_case     :				str 						=					[entry[0] for entry in vocab]
					return cls(
					    vocab_list=_A      ,    do_lower_case=_A      ,    cls_token_id=_A      ,    sep_token_id=_A      ,    pad_token_id=_A      ,    **_A      ,    )



				@classmethod
				def    _lowercase	(cls							: Dict      ,    _A							: Union[str, os.PathLike]      ,    *_A							: Union[str, Any]      ,    **_A							: Union[str, Any])       ->      int:
					try:
						__snake_case     :				Tuple 						=					BertTokenizer.from_pretrained(_A      ,    *_A      ,    **_A)
					except:  # noqa: E722
						from .tokenization_bert_fast import BertTokenizerFast

						__snake_case     :				Optional[int] 						=					BertTokenizerFast.from_pretrained(_A      ,    *_A      ,    **_A)
					return cls.from_tokenizer(_A      ,    **_A)



				def    _lowercase	(self							: Dict      ,    _A							: List[str])       ->      List[str]:
					if self.do_lower_case:
						__snake_case     :				Tuple 						=					case_fold_utfa(_A)
					__snake_case     :				Tuple 						=					self.tf_tokenizer.tokenize(_A)
					return tokens.merge_dims(1      ,    -1)



				def    _lowercase	(self							: Any      ,    _A							: str      ,    _A							: List[Any]=None      ,    _A							: List[str]=None      ,    _A							: Dict=None      ,    _A							: List[Any]=None      ,    _A							: str=None      ,    _A							: List[Any]=None      ,    _A							: List[str]=None      ,    )       ->      Union[str, Any]:
					if padding is None:
						__snake_case     :				Any 						=					self.padding
					if padding not in ("longest", "max_length"):
						raise ValueError('Padding must be either \'longest\' or \'max_length\'!')
					if max_length is not None and text_pair is not None:
						# Because we have to instantiate a Trimmer to do it properly
						raise ValueError('max_length cannot be overridden at call time when truncating paired texts!')
					if max_length is None:
						__snake_case     :				Dict 						=					self.max_length
					if truncation is None:
						__snake_case     :				int 						=					self.truncation
					if pad_to_multiple_of is None:
						__snake_case     :				Optional[int] 						=					self.pad_to_multiple_of
					if return_token_type_ids is None:
						__snake_case     :				str 						=					self.return_token_type_ids
					if return_attention_mask is None:
						__snake_case     :				List[Any] 						=					self.return_attention_mask
					if not isinstance(_A      ,    tf.Tensor):
						__snake_case     :				Tuple 						=					tf.convert_to_tensor(_A)
					if text_pair is not None and not isinstance(_A      ,    tf.Tensor):
						__snake_case     :				str 						=					tf.convert_to_tensor(_A)
					if text_pair is not None:
						if text.shape.rank > 1:
							raise ValueError('text argument should not be multidimensional when a text pair is supplied!')
						if text_pair.shape.rank > 1:
							raise ValueError('text_pair should not be multidimensional!')
					if text.shape.rank == 2:
						__snake_case						,							__snake_case     :				List[Any] 						=					text[:, 0], text[:, 1]
					__snake_case     :				Optional[int] 						=					self.unpaired_tokenize(_A)
					if text_pair is None:  # Unpaired text
						if truncation:
							__snake_case     :				Optional[Any] 						=					text[:, : max_length - 2]  # Allow room for special tokens
						__snake_case						,							__snake_case     :				str 						=					combine_segments(
						    (text,)      ,    start_of_sequence_id=self.cls_token_id      ,    end_of_segment_id=self.sep_token_id)
					else:  # Paired text
						__snake_case     :				int 						=					self.unpaired_tokenize(_A)
						if truncation:
							__snake_case						,							__snake_case     :				Optional[int] 						=					self.paired_trimmer.trim([text, text_pair])
						__snake_case						,							__snake_case     :				int 						=					combine_segments(
						    (text, text_pair)      ,    start_of_sequence_id=self.cls_token_id      ,    end_of_segment_id=self.sep_token_id)
					if padding == "longest":
						__snake_case     :				Union[str, Any] 						=					input_ids.bounding_shape(axis=1)
						if pad_to_multiple_of is not None:
							# No ceiling division in tensorflow, so we negate floordiv instead
							__snake_case     :				int 						=					pad_to_multiple_of * (-tf.math.floordiv(-pad_length      ,    _A))
					else:
						__snake_case     :				List[Any] 						=					max_length

					__snake_case						,							__snake_case     :				Optional[int] 						=					pad_model_inputs(_A      ,    max_seq_length=_A      ,    pad_value=self.pad_token_id)
					__snake_case     :				List[Any] 						=					{'input_ids': input_ids}
					if return_attention_mask:
						__snake_case     :				Dict 						=					attention_mask
					if return_token_type_ids:
						__snake_case						,							__snake_case     :				List[str] 						=					pad_model_inputs(
						    _A      ,    max_seq_length=_A      ,    pad_value=self.pad_token_id)
						__snake_case     :				List[Any] 						=					token_type_ids
					return output






				def    _lowercase	(self							: List[str])       ->      Optional[Any]:
					return {
					    "vocab_list": self.vocab_list,
					    "do_lower_case": self.do_lower_case,
					    "cls_token_id": self.cls_token_id,
					    "sep_token_id": self.sep_token_id,
					    "pad_token_id": self.pad_token_id,
					}






"""simple docstring"""







import math
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     str:

	'''simple docstring'''





	if number == 0:
		__snake_case     :				Any 						=					'Zero'
		return words
	else:
		__snake_case     :				Optional[Any] 						=					math.logaa(UpperCAmelCase_    )
		__snake_case     :				Optional[Any] 						=					digits + 1
		__snake_case     :				List[str] 						=					{}
		__snake_case     :				List[str] 						=					''
		__snake_case     :				Any 						=					'One'
		__snake_case     :				List[str] 						=					'Two'
		__snake_case     :				Tuple 						=					'Three'
		__snake_case     :				str 						=					'Four'
		__snake_case     :				int 						=					'Five'
		__snake_case     :				Optional[Any] 						=					'Six'
		__snake_case     :				str 						=					'Seven'
		__snake_case     :				Optional[Any] 						=					'Eight'
		__snake_case     :				Tuple 						=					'Nine'

		__snake_case     :				Tuple 						=					{}
		__snake_case     :				Optional[int] 						=					''
		__snake_case     :				str 						=					'Twenty'
		__snake_case     :				List[str] 						=					'Thirty'
		__snake_case     :				str 						=					'Forty'
		__snake_case     :				Union[str, Any] 						=					'Fifty'
		__snake_case     :				str 						=					'Sixty'
		__snake_case     :				Tuple 						=					'Seventy'
		__snake_case     :				Dict 						=					'Eighty'
		__snake_case     :				Optional[Any] 						=					'Ninety'

		__snake_case     :				List[Any] 						=					{}
		__snake_case     :				Optional[Any] 						=					'Ten'
		__snake_case     :				List[str] 						=					'Eleven'
		__snake_case     :				Any 						=					'Twelve'
		__snake_case     :				Tuple 						=					'Thirteen'
		__snake_case     :				Tuple 						=					'Fourteen'
		__snake_case     :				Optional[int] 						=					'Fifteen'
		__snake_case     :				Any 						=					'Sixteen'
		__snake_case     :				int 						=					'Seventeen'
		__snake_case     :				Dict 						=					'Eighteen'
		__snake_case     :				List[Any] 						=					'Nineteen'

		__snake_case     :				List[Any] 						=					{}
		__snake_case     :				List[Any] 						=					'Hundred,'
		__snake_case     :				Union[str, Any] 						=					'Thousand,'
		__snake_case     :				List[Any] 						=					'Lakh,'
		__snake_case     :				List[Any] 						=					'Crore,'

		__snake_case     :				List[Any] 						=					number
		__snake_case     :				List[Any] 						=					''
		__snake_case     :				List[Any] 						=					0
		__snake_case     :				Union[str, Any] 						=					int(UpperCAmelCase_    )
		while counter < digits:
			__snake_case     :				List[str] 						=					temp_num % 10
			if counter % 2 == 0:
				__snake_case     :				Tuple 						=					''
				if counter in placevalue and current != 0:
					__snake_case     :				List[str] 						=					placevalue[counter]
				if counter == 2:
					__snake_case     :				Dict 						=					singles[current] + addition + words
				elif counter == 0:
					if ((temp_num % 1_00) // 10) == 1:
						__snake_case     :				Dict 						=					teens[current] + addition + words
						__snake_case     :				Dict 						=					temp_num // 10
						counter += 1
					else:
						__snake_case     :				Optional[Any] 						=					singles[current] + addition + words

				else:
					__snake_case     :				Any 						=					doubles[current] + addition + words

			else:
				if counter == 1:
					if current == 1:
						__snake_case     :				Dict 						=					teens[number % 10] + words
					else:
						__snake_case     :				Optional[int] 						=					''
						if counter in placevalue:
							__snake_case     :				Dict 						=					placevalue[counter]
						__snake_case     :				Union[str, Any] 						=					doubles[current] + addition + words
				else:
					__snake_case     :				List[Any] 						=					''
					if counter in placevalue:
						if current == 0 and ((temp_num % 1_00) // 10) == 0:
							__snake_case     :				Optional[Any] 						=					''
						else:
							__snake_case     :				Any 						=					placevalue[counter]
					if ((temp_num % 1_00) // 10) == 1:
						__snake_case     :				int 						=					teens[current] + addition + words
						__snake_case     :				Any 						=					temp_num // 10
						counter += 1
					else:
						__snake_case     :				List[str] 						=					singles[current] + addition + words
			counter += 1
			__snake_case     :				str 						=					temp_num // 10
	return words


if __name__ == "__main__":
	import doctest

	doctest.testmod()





