




'''simple docstring'''






import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


lowercase	:      Any				=       '''none'''
lowercase	:      List[str]				=       '''none'''




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       lowercase  :		List[Any]						=      value.split(lowerCamelCase      )
       return len(lowerCamelCase      ) > 1 and any((len(lowerCamelCase      ) == 3 for x in split)      )




def 		a_( lowerCamelCase      )				->Any:






       '''simple docstring'''




       if isinstance(lowerCamelCase   ,				lowerCamelCase      ):
              return value
       if isinstance(lowerCamelCase   ,				lowerCamelCase      ):
              return float(lowerCamelCase      )
       if not isinstance(lowerCamelCase   ,				lowerCamelCase      ):
              raise ValueError('''Argument value is not a string. Can\'t parse it as float'''      )
       lowercase  :		str						=      value

       try:
              # Example: 1,000.7
              if "." in sanitized and "," in sanitized:
                     return float(sanitized.replace(''','''   ,				''''''      )      )
              # 1,000
              if "," in sanitized and _split_thousands(''','''   ,				lowerCamelCase      ):
                     return float(sanitized.replace(''','''   ,				''''''      )      )
              # 5,5556
              if "," in sanitized and sanitized.count(''','''      ) == 1 and not _split_thousands(''','''   ,				lowerCamelCase      ):
                     return float(sanitized.replace(''','''   ,				'''.'''      )      )
              # 0.0.0.1
              if sanitized.count('''.'''      ) > 1:
                     return float(sanitized.replace('''.'''   ,				''''''      )      )
              # 0,0,0,1
              if sanitized.count(''','''      ) > 1:
                     return float(sanitized.replace(''','''   ,				''''''      )      )
              return float(lowerCamelCase      )
       except ValueError:
              # Avoid adding the sanitized value in the error message.
              raise ValueError('''Unable to convert value to float'''      )




def 		a_( lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       if answer is None:
              return None
       try:
              lowercase  :		Any						=      convert_to_float(lowerCamelCase      )
              if isinstance(lowerCamelCase   ,				lowerCamelCase      ) and math.isnan(lowerCamelCase      ):
                     return None
              return value
       except ValueError:
              return answer.lower()


lowercase	:      Optional[int]				=       {
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}

class   SCREAMING_SNAKE_CASE_		(       enum.Enum				):
 __snake_case	:     Dict			   =      0
 __snake_case	:     Tuple			   =      1
 __snake_case	:     List[Any]			   =      2
 __snake_case	:     Tuple			   =      3
 __snake_case	:     Union[str, Any]			   =      4
 __snake_case	:     int			   =      5






class   SCREAMING_SNAKE_CASE_		(       enum.Enum				):
 __snake_case	:     Union[str, Any]			   =      0
 __snake_case	:     Any			   =      1
 __snake_case	:     Optional[Any]			   =      2






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE_		:
 __snake_case	:     Text
 __snake_case	:     _Operator
 __snake_case	:     Any






lowercase	:      Tuple				=       re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)




def 		a_( lowerCamelCase      )				->List[Any]:






       '''simple docstring'''




       return list(_TOKENIZER.findall(x.lower()      )      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       if operator == _Operator.EQUALS:
              return src == tgt
       elif operator == _Operator.GREATER:
              return src > tgt
       elif operator == _Operator.LESSER:
              return src < tgt
       raise ValueError(f"""Unknown operator: {operator}"""      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Any:






       '''simple docstring'''




       lowercase  :		Dict						=      table['''types''']
       return _TYPE_CONVERTER[types[column]](lowerCamelCase      )




def 		a_( lowerCamelCase      )				->List[Any]:






       '''simple docstring'''




       return isinstance(lowerCamelCase   ,				lowerCamelCase      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       for cond in conditions:
              lowercase  :		Optional[int]						=      row[cond.column]

              lowercase  :		Any						=      _parse_value(lowerCamelCase   ,				cond.column   ,				cond.cmp_value      )

              if _is_string(lowerCamelCase      ) and _is_string(lowerCamelCase      ):
                     lowercase  :		List[Any]						=      _normalize_for_match(lowerCamelCase      )
                     lowercase  :		Any						=      _normalize_for_match(lowerCamelCase      )

              if not isinstance(lowerCamelCase   ,				type(lowerCamelCase      )      ):
                     raise ValueError('''Type difference {} != {}'''.format(type(lowerCamelCase      )   ,				type(lowerCamelCase      )      )      )

              if not _compare(cond.operator   ,				lowerCamelCase   ,				lowerCamelCase      ):
                     return False
       return True




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       if not answer_coordinates:
              if aggregation_op == _Aggregation.COUNT:
                     return 0.0
              else:
                     return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
       if aggregation_op == _Aggregation.COUNT:
              return float(len(lowerCamelCase      )      )

       # If we have just one answer, if float returns it or try a conversion.
       lowercase  :		Tuple						=      [table['''rows'''][i][j] for (i, j) in answer_coordinates]
       if len(lowerCamelCase      ) == 1:
              try:
                     return convert_to_float(values[0]      )
              except ValueError as e:
                     if aggregation_op != _Aggregation.NONE:
                            raise e

       if aggregation_op == _Aggregation.NONE:
              return None

       # Other aggregation only support numeric values. Bail out if we have strings.
       if not all((isinstance(lowerCamelCase   ,				(int, float)      ) for v in values)      ):
              return None

       if aggregation_op == _Aggregation.SUM:
              return float(sum(lowerCamelCase      )      )
       elif aggregation_op == _Aggregation.AVERAGE:
              return sum(lowerCamelCase      ) / len(lowerCamelCase      )
       else:
              raise ValueError(f"""Unknown aggregation: {aggregation_op}"""      )




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Tuple:






       '''simple docstring'''




       lowercase  :		Tuple						=      sql_query['''agg''']
       if aggregation_op_index >= 3:
              lowercase  :		Tuple						=      _Aggregation(lowerCamelCase      )
       else:
              lowercase  :		str						=      _Aggregation.NONE

       lowercase  :		Optional[Any]						=      sql_query['''sel''']
       lowercase  :		int						=      [
           _Condition(lowerCamelCase   ,				_Operator(lowerCamelCase      )   ,				lowerCamelCase      )
           for column, operator, cmp_value in zip(
               sql_query['''conds''']['''column_index''']   ,				sql_query['''conds''']['''operator_index''']   ,				sql_query['''conds''']['''condition''']      )
       ]

       lowercase  :		Optional[int]						=      []
       for row in range(len(table['''rows''']      )      ):
              if _respect_conditions(lowerCamelCase   ,				table['''rows'''][row]   ,				lowerCamelCase      ):
                     indices.append((row, target_column)      )

       if not indices:
              return [], aggregation_op

       if len(lowerCamelCase      ) == 1:
              return indices, aggregation_op

       # Parsing of MIN/MAX.
       if aggregation_op_index in (1, 2):
              lowercase  :		List[str]						=      {2: min, 1: max}
              lowercase  :		Dict						=      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowerCamelCase      )]
              lowercase  :		Tuple						=      functools.reduce(operators[sql_query['''agg''']]   ,				lowerCamelCase      )

              lowercase  :		Optional[Any]						=      [indices[reduced[1]]]
              return ret, _Aggregation.NONE

       return indices, aggregation_op




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[Any]:






       '''simple docstring'''




       if float_answer is not None:
              return [str(lowerCamelCase      )]
       return [str(table['''real_rows'''][r][c]      ) for r, c in answer_coordinates]





def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->List:






       '''simple docstring'''




       lowercase  :		Union[str, Any]						=      _get_answer_coordinates(lowerCamelCase   ,				lowerCamelCase      )
       lowercase  :		List[Any]						=      _get_float_answer(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       lowercase  :		Union[str, Any]						=      _get_answer_text(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       # keep the original data the same with TaPas
       if len(lowerCamelCase      ) == 0:
              lowercase  :		Any						=      [EMPTY_ANSWER]
       return answer_text










'''simple docstring'''






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
      from tokenizers import Tokenizer
      from tokenizers.models import WordPiece

class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      list(tokenizer_class.max_model_input_sizes.keys() )
        for model_name in sa_models[:1]:
               lowercase  :		Dict						=      tokenizer_class.from_pretrained(A )
               self.assertIsNotNone(A )
               self.assertIsInstance(A   ,		A )
               self.assertIsInstance(A   ,		A )

               for special_tok in tokenizer.all_special_tokens:
                      self.assertIsInstance(A   ,		A )
                      lowercase  :		Tuple						=      tokenizer.convert_tokens_to_ids(A )
                      self.assertIsInstance(A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = None ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      pickle.dumps(A )
        self.assertIsNotNone(A )

        lowercase  :		str						=      pickle.loads(A )

        # Ensure is_fast is correctly restored
        self.assertEqual(be_restored.is_fast   ,		be_original.is_fast )

        # Ensure encodings are potentially correctly restored
        if be_original.is_fast:
               self.assertIsNotNone(be_restored.encodings )
        else:
               self.assertIsNone(be_restored.encodings )

        # Ensure the keys are the same
        for original_v, restored_v in zip(be_original.values()   ,		be_restored.values() ):
               if equal_op:
                      self.assertTrue(equal_op(A   ,		A ) )
               else:
                      self.assertEqual(A   ,		A )



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.check_tokenizer_from_pretrained(A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.assertEqual(TensorType('''tf''' )   ,		TensorType.TENSORFLOW )
        self.assertEqual(TensorType('''pt''' )   ,		TensorType.PYTORCH )
        self.assertEqual(TensorType('''np''' )   ,		TensorType.NUMPY )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import numpy as np

        lowercase  :		Union[str, Any]						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		str						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        # Python no tensor
        with self.subTest('''BatchEncoding (Python, return_tensors=None)''' ):
               self.assert_dump_and_restore(tokenizer_p('''Small example to encode''' ) )

        with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)''' ):
               self.assert_dump_and_restore(
                   tokenizer_p('''Small example to encode'''   ,		return_tensors=TensorType.NUMPY )   ,		np.array_equal )

        with self.subTest('''BatchEncoding (Rust, return_tensors=None)''' ):
               self.assert_dump_and_restore(tokenizer_r('''Small example to encode''' ) )

        with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)''' ):
               self.assert_dump_and_restore(
                   tokenizer_r('''Small example to encode'''   ,		return_tensors=TensorType.NUMPY )   ,		np.array_equal )



 @require_tf
 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import tensorflow as tf

        def tf_array_equals(A   ,		A ):
               return tf.reduce_all(tf.equal(A   ,		A ) )

        lowercase  :		Optional[Any]						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		Optional[Any]						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)''' ):
               self.assert_dump_and_restore(
                   tokenizer_p('''Small example to encode'''   ,		return_tensors=TensorType.TENSORFLOW )   ,		A )

        with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)''' ):
               self.assert_dump_and_restore(
                   tokenizer_r('''Small example to encode'''   ,		return_tensors=TensorType.TENSORFLOW )   ,		A )



 @require_torch
 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import torch

        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		Any						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)''' ):
               self.assert_dump_and_restore(
                   tokenizer_p('''Small example to encode'''   ,		return_tensors=TensorType.PYTORCH )   ,		torch.equal )

        with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)''' ):
               self.assert_dump_and_restore(
                   tokenizer_r('''Small example to encode'''   ,		return_tensors=TensorType.PYTORCH )   ,		torch.equal )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		List[Any]						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        with self.subTest('''Python Tokenizer''' ):
               self.assertFalse(tokenizer_p('''Small example to_encode''' ).is_fast )

        with self.subTest('''Rust Tokenizer''' ):
               self.assertTrue(tokenizer_r('''Small example to_encode''' ).is_fast )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )
        lowercase  :		str						=      tokenizer_r(['''Test''', '''\xad''', '''test''']   ,		is_split_into_words=A )

        self.assertEqual(encoded.word_to_tokens(0 )   ,		TokenSpan(start=1   ,		end=2 ) )
        self.assertEqual(encoded.word_to_tokens(1 )   ,		A )
        self.assertEqual(encoded.word_to_tokens(2 )   ,		TokenSpan(start=2   ,		end=3 ) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''np''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Any						=      batch.convert_to_tensors(tensor_type='''np''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''np'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		Union[str, Any]						=      batch.convert_to_tensors(tensor_type='''pt''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Tuple						=      batch.convert_to_tensors(tensor_type='''pt''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		Optional[Any]						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''pt'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_tf
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		Dict						=      batch.convert_to_tensors(tensor_type='''tf''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Optional[int]						=      batch.convert_to_tensors(tensor_type='''tf''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		str						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''tf'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_flax
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		List[Any]						=      batch.convert_to_tensors(tensor_type='''jax''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Dict						=      batch.convert_to_tensors(tensor_type='''jax''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		str						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		str						=      batch.convert_to_tensors(tensor_type='''jax'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [{'''input_ids''': np.array([0, 1, 2] )}, {'''input_ids''': np.array([0, 1, 2, 3] )}]
        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )

        lowercase  :		Dict						=      tokenizer.pad(A   ,		padding=A )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		np.ndarray ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
        lowercase  :		Dict						=      tokenizer.pad(A   ,		padding=A   ,		return_tensors='''np''' )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		np.ndarray ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import torch

        lowercase  :		Optional[Any]						=      [{'''input_ids''': torch.tensor([0, 1, 2] )}, {'''input_ids''': torch.tensor([0, 1, 2, 3] )}]
        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )

        lowercase  :		Any						=      tokenizer.pad(A   ,		padding=A )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		torch.Tensor ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
        lowercase  :		Union[str, Any]						=      tokenizer.pad(A   ,		padding=A   ,		return_tensors='''pt''' )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		torch.Tensor ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )



 @require_tf
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import tensorflow as tf

        lowercase  :		Optional[int]						=      [{'''input_ids''': tf.constant([0, 1, 2] )}, {'''input_ids''': tf.constant([0, 1, 2, 3] )}]
        lowercase  :		Dict						=      BertTokenizer.from_pretrained('''bert-base-cased''' )

        lowercase  :		List[Any]						=      tokenizer.pad(A   ,		padding=A )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		tf.Tensor ) )
        self.assertEqual(batch['''input_ids'''].numpy().tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
        lowercase  :		Any						=      tokenizer.pad(A   ,		padding=A   ,		return_tensors='''tf''' )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		tf.Tensor ) )
        self.assertEqual(batch['''input_ids'''].numpy().tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
        PreTrainedTokenizerFast(tokenizer_object=A )





 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
        with tempfile.TemporaryDirectory() as tmpdirname:
               bert_tokenizer.save(os.path.join(A   ,		'''tokenizer.json''' ) )
               PreTrainedTokenizerFast(tokenizer_file=os.path.join(A   ,		'''tokenizer.json''' ) )




