





'''simple docstring'''




import re
import string

import numpy as np

import datasets


lowercase__   :				Optional[Any]      =   '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

lowercase__   :				Any      =   '''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

lowercase__   :				Dict      =   '''
'''


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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return datasets.MetricInfo(
              description=_DESCRIPTION						,     citation=_CITATION						,     inputs_description=_KWARGS_DESCRIPTION						,     features=datasets.Features(
                  {
                      'predictions': datasets.Value('string'						,     id='sequence'),
                      'references': datasets.Value('string'						,     id='sequence'),
                  })						,     reference_urls=[]						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase=None						,     _UpperCAmelCase=False						,     _UpperCAmelCase=False						,     _UpperCAmelCase=False						,     ):

          '''simple docstring'''






          if regexes_to_ignore is not None:
               for s in regexes_to_ignore:
                    __A						:			Optional[Any]								=	np.array([re.sub(_UpperCAmelCase						,     ''						,     _UpperCAmelCase) for x in predictions])
                    __A						:			Any								=	np.array([re.sub(_UpperCAmelCase						,     ''						,     _UpperCAmelCase) for x in references])
          else:
               __A						:			Optional[int]								=	np.asarray(_UpperCAmelCase)
               __A						:			Optional[Any]								=	np.asarray(_UpperCAmelCase)

          if ignore_case:
               __A						:			List[Any]								=	np.char.lower(_UpperCAmelCase)
               __A						:			Tuple								=	np.char.lower(_UpperCAmelCase)

          if ignore_punctuation:
               __A						:			Any								=	string.punctuation.maketrans(''						,     ''						,     string.punctuation)
               __A						:			Any								=	np.char.translate(_UpperCAmelCase						,     table=_UpperCAmelCase)
               __A						:			str								=	np.char.translate(_UpperCAmelCase						,     table=_UpperCAmelCase)

          if ignore_numbers:
               __A						:			List[str]								=	string.digits.maketrans(''						,     ''						,     string.digits)
               __A						:			Tuple								=	np.char.translate(_UpperCAmelCase						,     table=_UpperCAmelCase)
               __A						:			List[Any]								=	np.char.translate(_UpperCAmelCase						,     table=_UpperCAmelCase)

          __A						:			List[str]								=	predictions == references

          return {"exact_match": np.mean(_UpperCAmelCase) * 100}






'''simple docstring'''




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 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	0


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
               __A						:			List[str]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
               self.assertGreater(len(_UpperCAmelCase)						,     0)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
          self.assertEqual(tokenizer.vocab_size						,     12)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (RobertaTokenizer, RobertaTokenizerFast))
          self.assertEqual(tokenizer.vocab_size						,     20)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	AutoConfig.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)
          # Check that tokenizer_type ≠ model_type
          __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     config=_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
          self.assertEqual(tokenizer.vocab_size						,     12)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='bert'						,     use_fast=_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

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

               __A						:			str								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='gpt2'						,     use_fast=_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

               __A						:			List[str]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='bert')
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

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

               __A						:			List[str]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='gpt2')
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

               self.assertEqual(tokenizer.model_max_length						,     512)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	TOKENIZER_MAPPING.values()
          __A						:			Union[str, Any]								=	[]

          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(_UpperCAmelCase)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Dict								=	AutoTokenizer.from_pretrained('microsoft/mpnet-base'						,     do_lower_case=_UpperCAmelCase)
          __A						:			List[Any]								=	tokenizer.tokenize(_UpperCAmelCase)
          self.assertEqual('[UNK]'						,     tokens[0])


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config')
          self.assertEqual(type(_UpperCAmelCase)						,     _UpperCAmelCase)
          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):

          '''simple docstring'''






          __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
          with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer.save_pretrained(_UpperCAmelCase)
               __A						:			Any								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	get_tokenizer_config('bert-base-cased')
          __A						:			Optional[int]								=	config.pop('_commit_hash'						,     _UpperCAmelCase)
          # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
          self.assertEqual(_UpperCAmelCase						,     {'do_lower_case': False})

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

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

          # 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):

          '''simple docstring'''






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

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

               __A						:			Optional[Any]								=	CustomTokenizer.from_pretrained(_UpperCAmelCase)
               with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						:			int								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          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):

          '''simple docstring'''






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

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

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

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

               # 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:
                    __A						:			Optional[int]								=	BertTokenizerFast.from_pretrained(_UpperCAmelCase)
                    bert_tokenizer.save_pretrained(_UpperCAmelCase)
                    __A						:			Dict								=	CustomTokenizerFast.from_pretrained(_UpperCAmelCase)

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

                    __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

                    __A						:			Any								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          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):

          '''simple docstring'''






          with self.assertRaises(_UpperCAmelCase):
               __A						:			List[str]								=	AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer')
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(_UpperCAmelCase):
               __A						:			Dict								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)

          __A						:			str								=	AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)
          self.assertTrue(tokenizer.special_attribute_present)
          # Test tokenizer can be reloaded.
          with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer.save_pretrained(_UpperCAmelCase)
               __A						:			Dict								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     trust_remote_code=_UpperCAmelCase)
          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
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               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(_UpperCAmelCase)
                    __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               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):

          '''simple docstring'''






          class 			SCREAMING_SNAKE_CASE   (a__							):
               lowerCAmelCase		      =	False




          class 			SCREAMING_SNAKE_CASE   (a__							):
               lowerCAmelCase		      =	NewTokenizer
               lowerCAmelCase		      =	False

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

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

               # If remote is enabled, we load from the Hub
               __A						:			int								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizerFast')
               self.assertTrue(tokenizer.special_attribute_present)
               __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               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):

          '''simple docstring'''






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

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with self.assertRaisesRegex(
              _UpperCAmelCase						,     'bert-base is not a local folder and is not a valid model identifier'):
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('bert-base')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with self.assertRaisesRegex(
              _UpperCAmelCase						,     R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     revision='aaaaaa')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
          with RequestCounter() as counter:
               __A						:			Union[str, Any]								=	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)