





"""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       snake_case		(       tf.keras.layers.Layer       ):

      def __init__(      self       :Optional[int]						,							_lowerCamelCase       :List						,							_lowerCamelCase       :bool						,							_lowerCamelCase       :int = None						,							_lowerCamelCase       :int = None						,							_lowerCamelCase       :int = None						,							_lowerCamelCase       :str = "longest"						,							_lowerCamelCase       :bool = True						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = None						,							_lowerCamelCase       :bool = True						,							_lowerCamelCase       :bool = True						,							_lowerCamelCase       :bool = True						,							**_lowerCamelCase       :Tuple						,							):
       super().__init__()
       if use_fast_bert_tokenizer:
        __SCREAMING_SNAKE_CASE			:      int 							=	FastBertTokenizer(
            _lowerCamelCase						,							token_out_type=tf.intaa						,							lower_case_nfd_strip_accents=_lowerCamelCase						,							**_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=_lowerCamelCase						,							key_dtype=tf.string						,							values=tf.range(tf.size(_lowerCamelCase						,							out_type=tf.intaa						)						,							dtype=tf.intaa						)						,							value_dtype=tf.intaa						,							)						,							num_oov_buckets=1						,							)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	BertTokenizerLayer(
            _lowerCamelCase						,							token_out_type=tf.intaa						,							lower_case=_lowerCamelCase						,							**_lowerCamelCase						)

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

      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Any						,							_lowerCamelCase       :"PreTrainedTokenizerBase"						,							**_lowerCamelCase       :Any						):  # noqa: F821
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	kwargs.pop('''do_lower_case'''						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      str 							=	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       __SCREAMING_SNAKE_CASE			:      Any 							=	kwargs.pop('''cls_token_id'''						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      int 							=	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	kwargs.pop('''sep_token_id'''						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       __SCREAMING_SNAKE_CASE			:      Any 							=	kwargs.pop('''pad_token_id'''						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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

      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Tuple						,							_lowerCamelCase       :Union[str, os.PathLike]						,							*_lowerCamelCase       :List[str]						,							**_lowerCamelCase       :Optional[int]						):
       try:
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	BertTokenizer.from_pretrained(_lowerCamelCase						,							*_lowerCamelCase						,							**_lowerCamelCase						)
       except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	BertTokenizerFast.from_pretrained(_lowerCamelCase						,							*_lowerCamelCase						,							**_lowerCamelCase						)
       return cls.from_tokenizer(_lowerCamelCase						,							**_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :int						):
       if self.do_lower_case:
        __SCREAMING_SNAKE_CASE			:      Any 							=	case_fold_utfa(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.tf_tokenizer.tokenize(_lowerCamelCase						)
       return tokens.merge_dims(1						,							-1						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :List[Any]=None						,							_lowerCamelCase       :List[str]=None						,							_lowerCamelCase       :str=None						,							_lowerCamelCase       :List[Any]=None						,							_lowerCamelCase       :Tuple=None						,							_lowerCamelCase       :int=None						,							_lowerCamelCase       :str=None						,							):
       if padding is None:
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	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:
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.max_length
       if truncation is None:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.truncation
       if pad_to_multiple_of is None:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	self.pad_to_multiple_of
       if return_token_type_ids is None:
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.return_token_type_ids
       if return_attention_mask is None:
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.return_attention_mask
       if not isinstance(_lowerCamelCase						,							tf.Tensor						):
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tf.convert_to_tensor(_lowerCamelCase						)
       if text_pair is not None and not isinstance(_lowerCamelCase						,							tf.Tensor						):
        __SCREAMING_SNAKE_CASE			:      Any 							=	tf.convert_to_tensor(_lowerCamelCase						)
       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:
        __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	text[:, 0], text[:, 1]
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.unpaired_tokenize(_lowerCamelCase						)
       if text_pair is None:  # Unpaired text
        if truncation:
         __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	text[:, : max_length - 2]  # Allow room for special tokens
        __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	combine_segments(
            (text,)						,							start_of_sequence_id=self.cls_token_id						,							end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.unpaired_tokenize(_lowerCamelCase						)
        if truncation:
         __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Any 							=	self.paired_trimmer.trim([text, text_pair]						)
        __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	combine_segments(
            (text, text_pair)						,							start_of_sequence_id=self.cls_token_id						,							end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
        __SCREAMING_SNAKE_CASE			:      str 							=	input_ids.bounding_shape(axis=1						)
        if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         __SCREAMING_SNAKE_CASE			:      Dict 							=	pad_to_multiple_of * (-tf.math.floordiv(-pad_length						,							_lowerCamelCase						))
       else:
        __SCREAMING_SNAKE_CASE			:      int 							=	max_length

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



      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, 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"""





#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class       snake_case		:
      lowerCamelCase__      =			'''block_{0}.dat'''

      def __init__(      self       :int						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	filename
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[]

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.BLOCK_FILENAME_FORMAT.format(_lowerCamelCase						)
       with open(_lowerCamelCase						,							'''w'''						) as file:
        file.write(_lowerCamelCase						)
       self.block_filenames.append(_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       return self.block_filenames

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Optional[Any]=None						):
       __SCREAMING_SNAKE_CASE			:      int 							=	0
       with open(self.filename						) as file:
        while True:
         __SCREAMING_SNAKE_CASE			:      Any 							=	file.readlines(_lowerCamelCase						)

         if lines == []:
          break

         if sort_key is None:
          lines.sort()
         else:
          lines.sort(key=_lowerCamelCase						)

         self.write_block(''''''.join(_lowerCamelCase						)						,							_lowerCamelCase						)
         i += 1



      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       map(os.remove						,							self.block_filenames						)







class       snake_case		:

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	-1
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	None

       for i in range(len(_lowerCamelCase						)						):
        if min_str is None or choices[i] < min_str:
         __SCREAMING_SNAKE_CASE			:      Tuple 							=	i

       return min_index







class       snake_case		:

      def __init__(      self       :str						,							_lowerCamelCase       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	files
       __SCREAMING_SNAKE_CASE			:      str 							=	set()
       __SCREAMING_SNAKE_CASE			:      Dict 							=	len(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      str 							=	{i: None for i in range(self.num_buffers						)}

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       return {
           i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
       }

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       for i in range(self.num_buffers						):
        if self.buffers[i] is None and i not in self.empty:
         __SCREAMING_SNAKE_CASE			:      int 							=	self.files[i].readline()

         if self.buffers[i] == "":
          self.empty.add(_lowerCamelCase						)
          self.files[i].close()

       if len(self.empty						) == self.num_buffers:
        return False

       return True



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.buffers[index]
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	None

       return value







class       snake_case		:

      def __init__(      self       :List[Any]						,							_lowerCamelCase       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	merge_strategy

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	FilesArray(self.get_file_handles(_lowerCamelCase						,							_lowerCamelCase						)						)
       with open(_lowerCamelCase						,							'''w'''						,							_lowerCamelCase						) as outfile:
        while buffers.refresh():
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.merge_strategy.select(buffers.get_dict()						)
         outfile.write(buffers.unshift(_lowerCamelCase						)						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :int						,							_lowerCamelCase       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{}

       for i in range(len(_lowerCamelCase						)						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	open(filenames[i]						,							'''r'''						,							_lowerCamelCase						)  # noqa: UP015

       return files







class       snake_case		:

      def __init__(      self       :Dict						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      int 							=	block_size

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Dict=None						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.get_number_blocks(_lowerCamelCase						,							self.block_size						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	FileSplitter(_lowerCamelCase						)
       splitter.split(self.block_size						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Any 							=	FileMerger(NWayMerge()						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.block_size / (num_blocks + 1)
       merger.merge(splitter.get_block_filenames()						,							filename + '''.out'''						,							_lowerCamelCase						)

       splitter.cleanup()



      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						,							_lowerCamelCase       :str						,							_lowerCamelCase       :List[Any]						):
       return (os.stat(_lowerCamelCase						).st_size / block_size) + 1







def 		lowerCAmelCase_     (  lowercase_       :							str		):

 '''simple docstring'''






 if string[-1].lower() == "k":
  return int(string[:-1]		) * 1024
 elif string[-1].lower() == "m":
  return int(string[:-1]		) * 1024 * 1024
 elif string[-1].lower() == "g":
  return int(string[:-1]		) * 1024 * 1024 * 1024
 else:
  return int(lowercase_		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	argparse.ArgumentParser()
 parser.add_argument(
     '''-m'''			,				'''--mem'''			,				help='''amount of memory to use for sorting'''			,				default='''100M'''		)
 parser.add_argument(
     '''filename'''			,				metavar='''<filename>'''			,				nargs=1			,				help='''name of file to sort'''		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	parser.parse_args()

 __SCREAMING_SNAKE_CASE			:      List[str] 							=	ExternalSort(parse_memory(args.mem		)		)
 sorter.sort(args.filename[0]		)


if __name__ == "__main__":
    main()

