





from scipy.stats import pearsonr

import datasets


lowerCAmelCase_        =							'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


lowerCAmelCase_        =							'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


lowerCAmelCase_        =							'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,	_KWARGS_DESCRIPTION	)
class    __lowerCAmelCase			(     datasets.Metric	):






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''float'''       ),
							            '''references''': datasets.Value('''float'''       ),
							        }       )   ,				reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']   ,				)






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__=False       )	->  Union[str, Any]:


							'''simple docstring'''



							if return_pvalue:
											snake_case_		:       Dict           =    pearsonr(__magic_name__   ,				__magic_name__       )
											return {"pearsonr": results[0], "p-value": results[1]}
							else:
											return {"pearsonr": float(pearsonr(__magic_name__   ,				__magic_name__       )[0]       )}








import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
       from test_module.custom_tokenization_fast import CustomTokenizerFast





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  0







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
                 self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(BertTokenizer, BertTokenizerFast) )
                 self.assertGreater(len(_SCREAMING_SNAKE_CASE )   ,						0 )

           for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
                 self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(GPTaTokenizer, GPTaTokenizerFast) )
                 self.assertGreater(len(_SCREAMING_SNAKE_CASE )   ,						0 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(BertTokenizer, BertTokenizerFast) )
           self.assertEqual(tokenizer.vocab_size   ,						12 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(RobertaTokenizer, RobertaTokenizerFast) )
           self.assertEqual(tokenizer.vocab_size   ,						20 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           # Check that tokenizer_type ≠ model_type
           UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						config=_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(BertTokenizer, BertTokenizerFast) )
           self.assertEqual(tokenizer.vocab_size   ,						12 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy('./tests/fixtures/vocab.txt'   ,						os.path.join(_SCREAMING_SNAKE_CASE   ,						'vocab.txt' ) )

                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						tokenizer_type='bert'   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy('./tests/fixtures/vocab.json'   ,						os.path.join(_SCREAMING_SNAKE_CASE   ,						'vocab.json' ) )
                 shutil.copy('./tests/fixtures/merges.txt'   ,						os.path.join(_SCREAMING_SNAKE_CASE   ,						'merges.txt' ) )

                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						tokenizer_type='gpt2'   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy('./tests/fixtures/vocab.txt'   ,						os.path.join(_SCREAMING_SNAKE_CASE   ,						'vocab.txt' ) )

                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						tokenizer_type='bert' )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy('./tests/fixtures/vocab.json'   ,						os.path.join(_SCREAMING_SNAKE_CASE   ,						'vocab.json' ) )
                 shutil.copy('./tests/fixtures/merges.txt'   ,						os.path.join(_SCREAMING_SNAKE_CASE   ,						'merges.txt' ) )

                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						tokenizer_type='gpt2' )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           with pytest.raises(_SCREAMING_SNAKE_CASE ):
                 AutoTokenizer.from_pretrained('./'   ,						tokenizer_type='xxx' )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
                 UpperCamelCase					       =  tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased' )
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(BertTokenizer, BertTokenizerFast) )

                 if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                       self.assertEqual(tokenizer.basic_tokenizer.do_lower_case   ,						_SCREAMING_SNAKE_CASE )
                 else:
                       self.assertEqual(tokenizer.do_lower_case   ,						_SCREAMING_SNAKE_CASE )

                 self.assertEqual(tokenizer.model_max_length   ,						512 )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
                 with self.assertRaisesRegex(
                     _SCREAMING_SNAKE_CASE   ,						'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'   ,						):
                       UpperCamelCase					       =  tokenizer_class.from_pretrained('julien-c/herlolip-not-exists' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  TOKENIZER_MAPPING.values()
           UpperCamelCase					       =  []

           for slow_tok, fast_tok in tokenizers:
                 if slow_tok is not None:
                       tokenizer_names.append(slow_tok.__name__ )

                 if fast_tok is not None:
                       tokenizer_names.append(fast_tok.__name__ )

           for tokenizer_name in tokenizer_names:
                 # must find the right class
                 tokenizer_class_from_name(_SCREAMING_SNAKE_CASE )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'   ,						use_fast=_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased' )   ,						_SCREAMING_SNAKE_CASE )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained('distilbert-base-uncased'   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  'Hello, world. How are you?'
           UpperCamelCase					       =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertEqual('[UNK]'   ,						tokens[0] )

           UpperCamelCase					       =  AutoTokenizer.from_pretrained('microsoft/mpnet-base'   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertEqual('[UNK]'   ,						tokens[0] )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config' )
           self.assertEqual(type(_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE )
           self.assertEqual(tokenizer.model_max_length   ,						512 )
           self.assertEqual(tokenizer.vocab_size   ,						3_0000 )
           self.assertEqual(tokenizer.unk_token   ,						'[UNK]' )
           self.assertEqual(tokenizer.padding_side   ,						'right' )
           self.assertEqual(tokenizer.truncation_side   ,						'right' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						(BertTokenizer, BertTokenizerFast) )
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )

           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						tokenizer.__class__ )
           self.assertEqual(tokenizera.vocab_size   ,						12 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained('ctrl' )
           # There is no fast CTRL so this always gives us a slow tokenizer.
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  get_tokenizer_config('bert-base-cased' )
           UpperCamelCase					       =  config.pop('_commit_hash'   ,						_SCREAMING_SNAKE_CASE )
           # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
           self.assertEqual(_SCREAMING_SNAKE_CASE   ,						{'do_lower_case': False} )

           # This model does not have a tokenizer_config so we get back an empty dict.
           UpperCamelCase					       =  get_tokenizer_config(_SCREAMING_SNAKE_CASE )
           self.assertDictEqual(_SCREAMING_SNAKE_CASE   ,						{} )

           # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
           UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  get_tokenizer_config(_SCREAMING_SNAKE_CASE )

           # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
           self.assertEqual(config['tokenizer_class']   ,						'BertTokenizer' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           try:
                 AutoConfig.register('custom'   ,						_SCREAMING_SNAKE_CASE )

                 AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						slow_tokenizer_class=_SCREAMING_SNAKE_CASE )
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(_SCREAMING_SNAKE_CASE ):
                       AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						slow_tokenizer_class=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  CustomTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )

                       UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
                       self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           try:
                 AutoConfig.register('custom'   ,						_SCREAMING_SNAKE_CASE )

                 # Can register in two steps
                 AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						slow_tokenizer_class=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,						(CustomTokenizer, None) )
                 AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						fast_tokenizer_class=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,						(CustomTokenizer, CustomTokenizerFast) )

                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
                 # Can register in one step
                 AutoTokenizer.register(
                     _SCREAMING_SNAKE_CASE   ,						slow_tokenizer_class=_SCREAMING_SNAKE_CASE   ,						fast_tokenizer_class=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,						(CustomTokenizer, CustomTokenizerFast) )

                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(_SCREAMING_SNAKE_CASE ):
                       AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						fast_tokenizer_class=_SCREAMING_SNAKE_CASE )

                 # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
                 # and that model does not have a tokenizer.json
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       UpperCamelCase					       =  BertTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE )
                       bert_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  CustomTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE )

                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )

                       UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
                       self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

                       UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						use_fast=_SCREAMING_SNAKE_CASE )
                       self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' )
           # If remote code is disabled, we can't load this config.
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE )
           self.assertTrue(tokenizer.special_attribute_present )
           # Test tokenizer can be reloaded.
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						trust_remote_code=_SCREAMING_SNAKE_CASE )
           self.assertTrue(reloaded_tokenizer.special_attribute_present )

           if is_tokenizers_available():
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizerFast' )
                 self.assertEqual(reloaded_tokenizer.__class__.__name__   ,						'NewTokenizerFast' )

                 # Test we can also load the slow version
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertTrue(tokenizer.special_attribute_present )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )
                 # Test tokenizer can be reloaded.
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						trust_remote_code=_SCREAMING_SNAKE_CASE   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(reloaded_tokenizer.__class__.__name__   ,						'NewTokenizer' )
                 self.assertTrue(reloaded_tokenizer.special_attribute_present )
           else:
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )
                 self.assertEqual(reloaded_tokenizer.__class__.__name__   ,						'NewTokenizer' )







     @require_tokenizers
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           class 					A__     (     __snake_case ):

                '''simple docstring'''






                snake_case__					=					False





           class 					A__     (     __snake_case ):

                '''simple docstring'''






                snake_case__					=					NewTokenizer
                snake_case__					=					False

           try:
                 AutoConfig.register('custom'   ,						_SCREAMING_SNAKE_CASE )
                 AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						slow_tokenizer_class=_SCREAMING_SNAKE_CASE )
                 AutoTokenizer.register(_SCREAMING_SNAKE_CASE   ,						fast_tokenizer_class=_SCREAMING_SNAKE_CASE )
                 # If remote code is not set, the default is to use local
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizerFast' )
                 self.assertFalse(tokenizer.special_attribute_present )
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )
                 self.assertFalse(tokenizer.special_attribute_present )

                 # If remote code is disabled, we load the local one.
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizerFast' )
                 self.assertFalse(tokenizer.special_attribute_present )
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )
                 self.assertFalse(tokenizer.special_attribute_present )

                 # If remote is enabled, we load from the Hub
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizerFast' )
                 self.assertTrue(tokenizer.special_attribute_present )
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )
                 self.assertTrue(tokenizer.special_attribute_present )

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained(
               'hf-internal-testing/test_dynamic_tokenizer_legacy'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE )
           self.assertTrue(tokenizer.special_attribute_present )
           if is_tokenizers_available():
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizerFast' )

                 # Test we can also load the slow version
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(
                     'hf-internal-testing/test_dynamic_tokenizer_legacy'   ,						trust_remote_code=_SCREAMING_SNAKE_CASE   ,						use_fast=_SCREAMING_SNAKE_CASE )
                 self.assertTrue(tokenizer.special_attribute_present )
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )
           else:
                 self.assertEqual(tokenizer.__class__.__name__   ,						'NewTokenizer' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           with self.assertRaisesRegex(
               _SCREAMING_SNAKE_CASE   ,						'bert-base is not a local folder and is not a valid model identifier' ):
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained('bert-base' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           with self.assertRaisesRegex(
               _SCREAMING_SNAKE_CASE   ,						r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,						revision='aaaaaa' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
           with RequestCounter() as counter:
                 UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
                 self.assertEqual(counter.get_request_count   ,						0 )
                 self.assertEqual(counter.head_request_count   ,						1 )
                 self.assertEqual(counter.other_request_count   ,						0 )



