


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   ,__snake_case   ,__snake_case   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,__snake_case = "longest"   ,__snake_case = True   ,__snake_case = 5_1_2   ,__snake_case = None   ,__snake_case = True   ,__snake_case = True   ,__snake_case = True   ,**__snake_case   ,):



       """simple docstring"""





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

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


  @classmethod
  def 	__UpperCAmelCase    (       cls   ,__snake_case   ,**__snake_case   ):  # noqa: F821



       """simple docstring"""





       A_					      =					kwargs.pop('''do_lower_case'''   ,UpperCAmelCase_   )
       A_					      =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       A_					      =					kwargs.pop('''cls_token_id'''   ,UpperCAmelCase_   )
       A_					      =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       A_					      =					kwargs.pop('''sep_token_id'''   ,UpperCAmelCase_   )
       A_					      =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       A_					      =					kwargs.pop('''pad_token_id'''   ,UpperCAmelCase_   )
       A_					      =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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


  @classmethod
  def 	__UpperCAmelCase    (       cls   ,__snake_case   ,*__snake_case   ,**__snake_case   ):



       """simple docstring"""





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

            A_					      =					BertTokenizerFast.from_pretrained(UpperCAmelCase_   ,*UpperCAmelCase_   ,**UpperCAmelCase_   )
       return cls.from_tokenizer(UpperCAmelCase_   ,**UpperCAmelCase_   )


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,):



       """simple docstring"""





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

       A_					      =					pad_model_inputs(UpperCAmelCase_   ,max_seq_length=UpperCAmelCase_   ,pad_value=self.pad_token_id   )
       A_					      =					{'input_ids': input_ids}
       if return_attention_mask:
            A_					      =					attention_mask
       if return_token_type_ids:
            A_					      =					pad_model_inputs(
                UpperCAmelCase_   ,max_seq_length=UpperCAmelCase_   ,pad_value=self.pad_token_id   )
            A_					      =					token_type_ids
       return output




  def 	__UpperCAmelCase    (       self   ):



       """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,
       }










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



