




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      ):







      '''simple docstring'''





      def __init__(						self							:					Union[str, Any]	,     __lowercase							:					List	,     __lowercase							:					bool	,     __lowercase							:					int = None	,     __lowercase							:					int = None	,     __lowercase							:					int = None	,     __lowercase							:					str = "longest"	,     __lowercase							:					bool = True	,     __lowercase							:					int = 5_12	,     __lowercase							:					int = None	,     __lowercase							:					bool = True	,     __lowercase							:					bool = True	,     __lowercase							:					bool = True	,     **__lowercase							:					Tuple	,     ):






       """simple docstring"""






       super().__init__()
       if use_fast_bert_tokenizer:
        snake_case_		   =					FastBertTokenizer(
            __lowercase	,     token_out_type=tf.intaa	,     lower_case_nfd_strip_accents=__lowercase	,     **__lowercase						)
       else:
        snake_case_		   =					tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=__lowercase	,     key_dtype=tf.string	,     values=tf.range(tf.size(__lowercase	,     out_type=tf.intaa						)	,     dtype=tf.intaa						)	,     value_dtype=tf.intaa	,     )	,     num_oov_buckets=1	,     )
        snake_case_		   =					BertTokenizerLayer(
            __lowercase	,     token_out_type=tf.intaa	,     lower_case=__lowercase	,     **__lowercase						)

       snake_case_		   =					vocab_list
       snake_case_		   =					do_lower_case
       snake_case_		   =					cls_token_id or vocab_list.index("[CLS]"						)
       snake_case_		   =					sep_token_id or vocab_list.index("[SEP]"						)
       snake_case_		   =					pad_token_id or vocab_list.index("[PAD]"						)
       snake_case_		   =					ShrinkLongestTrimmer(max_length - 3	,     axis=1						)  # Allow room for special tokens
       snake_case_		   =					max_length
       snake_case_		   =					padding
       snake_case_		   =					truncation
       snake_case_		   =					pad_to_multiple_of
       snake_case_		   =					return_token_type_ids
       snake_case_		   =					return_attention_mask






      @classmethod
      def 	snake_case__						(						cls							:					Any	,     __lowercase							:					"PreTrainedTokenizerBase"	,     **__lowercase							:					Any						):  # noqa: F821






       """simple docstring"""






       snake_case_		   =					kwargs.pop("do_lower_case"	,     __lowercase						)
       snake_case_		   =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       snake_case_		   =					kwargs.pop("cls_token_id"	,     __lowercase						)
       snake_case_		   =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       snake_case_		   =					kwargs.pop("sep_token_id"	,     __lowercase						)
       snake_case_		   =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       snake_case_		   =					kwargs.pop("pad_token_id"	,     __lowercase						)
       snake_case_		   =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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






      @classmethod
      def 	snake_case__						(						cls							:					str	,     __lowercase							:					Union[str, os.PathLike]	,     *__lowercase							:					Optional[int]	,     **__lowercase							:					int						):






       """simple docstring"""






       try:
        snake_case_		   =					BertTokenizer.from_pretrained(__lowercase	,     *__lowercase	,     **__lowercase						)
       except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        snake_case_		   =					BertTokenizerFast.from_pretrained(__lowercase	,     *__lowercase	,     **__lowercase						)
       return cls.from_tokenizer(__lowercase	,     **__lowercase						)






      def 	snake_case__						(						self							:					int	,     __lowercase							:					Tuple						):






       """simple docstring"""






       if self.do_lower_case:
        snake_case_		   =					case_fold_utfa(__lowercase						)
       snake_case_		   =					self.tf_tokenizer.tokenize(__lowercase						)
       return tokens.merge_dims(1	,     -1						)






      def 	snake_case__						(						self							:					Dict	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Optional[int]=None	,     __lowercase							:					Any=None	,     __lowercase							:					str=None	,     __lowercase							:					Optional[int]=None	,     __lowercase							:					List[str]=None	,     __lowercase							:					str=None	,     __lowercase							:					Tuple=None	,     ):






       """simple docstring"""






       if padding is None:
        snake_case_		   =					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_		   =					self.max_length
       if truncation is None:
        snake_case_		   =					self.truncation
       if pad_to_multiple_of is None:
        snake_case_		   =					self.pad_to_multiple_of
       if return_token_type_ids is None:
        snake_case_		   =					self.return_token_type_ids
       if return_attention_mask is None:
        snake_case_		   =					self.return_attention_mask
       if not isinstance(__lowercase	,     tf.Tensor						):
        snake_case_		   =					tf.convert_to_tensor(__lowercase						)
       if text_pair is not None and not isinstance(__lowercase	,     tf.Tensor						):
        snake_case_		   =					tf.convert_to_tensor(__lowercase						)
       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_		   =					text[:, 0], text[:, 1]
       snake_case_		   =					self.unpaired_tokenize(__lowercase						)
       if text_pair is None:  # Unpaired text
        if truncation:
         snake_case_		   =					text[:, : max_length - 2]  # Allow room for special tokens
        snake_case_   ,				snake_case_		   =					combine_segments(
            (text,)	,     start_of_sequence_id=self.cls_token_id	,     end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
        snake_case_		   =					self.unpaired_tokenize(__lowercase						)
        if truncation:
         snake_case_   ,				snake_case_		   =					self.paired_trimmer.trim([text, text_pair]						)
        snake_case_   ,				snake_case_		   =					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_		   =					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_		   =					pad_to_multiple_of * (-tf.math.floordiv(-pad_length	,     __lowercase						))
       else:
        snake_case_		   =					max_length

       snake_case_   ,				snake_case_		   =					pad_model_inputs(__lowercase	,     max_seq_length=__lowercase	,     pad_value=self.pad_token_id						)
       snake_case_		   =					{"input_ids": input_ids}
       if return_attention_mask:
        snake_case_		   =					attention_mask
       if return_token_type_ids:
        snake_case_   ,				snake_case_		   =					pad_model_inputs(
            __lowercase	,     max_seq_length=__lowercase	,     pad_value=self.pad_token_id						)
        snake_case_		   =					token_type_ids
       return output






      def 	snake_case__						(						self							:					List[str]						):






       """simple docstring"""






       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,
       }






def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if right <= left:
		return
	snake_case_		   =					snake_case_		   =					left
	snake_case_		   =					right
	snake_case_		   =					sorting[left]
	while i <= b:
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[a]
			a += 1
			i += 1
		elif sorting[i] > pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[b]
			b -= 1
		else:
			i += 1
	quick_sort_apartition(_A					,     _A					,     a - 1							)
	quick_sort_apartition(_A					,     b + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if left < right:
		snake_case_		   =					lomuto_partition(_A					,     _A					,     _A							)
		quick_sort_lomuto_partition(_A					,     _A					,     pivot_index - 1							)
		quick_sort_lomuto_partition(_A					,     pivot_index + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					sorting[right]
	snake_case_		   =					left
	for i in range(_A					,     _A							):
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[store_index]
			store_index += 1
	snake_case_   ,				snake_case_		   =					sorting[store_index], sorting[right]
	return store_index


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	if len(_A							) <= 1:
		return sorting
	return (
	    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
	    + [i for i in sorting if i == sorting[0]]
	    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)

		lowercase__    :					Tuple					=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__    :					int					=						[int(item) for item in user_input.split(",")]
		quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
		print(unsorted)
