

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 				__lowerCAmelCase  (      unittest.TestCase		):




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =0




  @slow
  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
              _lowercase			 =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():
              _lowercase			 =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 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )
        self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     (BertTokenizer, BertTokenizerFast)      )
        self.assertEqual(tokenizer.vocab_size							,     12      )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )
        self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     (RobertaTokenizer, RobertaTokenizerFast)      )
        self.assertEqual(tokenizer.vocab_size							,     20      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






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




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






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

              _lowercase			 =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'      )      )

              _lowercase			 =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 						A__  (       self      )						->				str:
        '''simple docstring'''






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

              _lowercase			 =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'      )      )

              _lowercase			 =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							,     tokenizer_type='gpt2'      )
              self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_      )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






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




  @require_tokenizers
  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
              _lowercase			 =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 						A__  (       self      )						->				Dict:
        '''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'							,     ):
                    _lowercase			 =tokenizer_class.from_pretrained('julien-c/herlolip-not-exists'      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =TOKENIZER_MAPPING.values()
        _lowercase			 =[]

        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 						A__  (       self      )						->				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 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






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

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




  @require_tokenizers
  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =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							,     30_000      )
        self.assertEqual(tokenizer.unk_token							,     '[UNK]'      )
        self.assertEqual(tokenizer.padding_side							,     'right'      )
        self.assertEqual(tokenizer.truncation_side							,     'right'      )




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =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_      )
              _lowercase			 =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )

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




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =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 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =get_tokenizer_config('bert-base-cased'      )
        _lowercase			 =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.
        _lowercase			 =get_tokenizer_config(SCREAMING_SNAKE_CASE_      )
        self.assertDictEqual(SCREAMING_SNAKE_CASE_							,     {}      )

        # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
        _lowercase			 =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )
        with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )
              _lowercase			 =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 						A__  (       self      )						->				Optional[Any]:
        '''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_      )

              _lowercase			 =CustomTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )
              with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )

                    _lowercase			 =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 						A__  (       self      )						->				str:
        '''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:
                    _lowercase			 =BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_      )
                    bert_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )
                    _lowercase			 =CustomTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_      )

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

                    _lowercase			 =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_      )

                    _lowercase			 =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 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
              _lowercase			 =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_      ):
              _lowercase			 =AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'							,     trust_remote_code=SCREAMING_SNAKE_CASE_      )

        _lowercase			 =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_      )
              _lowercase			 =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
              _lowercase			 =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_      )
                    _lowercase			 =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 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        class 				__lowerCAmelCase  (      __lowerCamelCase		):
          _a		    =						False





        class 				__lowerCAmelCase  (      __lowerCamelCase		):
          _a		    =						NewTokenizer
          _a		    =						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
              _lowercase			 =AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'      )
              self.assertEqual(tokenizer.__class__.__name__							,     'NewTokenizerFast'      )
              self.assertFalse(tokenizer.special_attribute_present      )
              _lowercase			 =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.
              _lowercase			 =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      )
              _lowercase			 =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
              _lowercase			 =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      )
              _lowercase			 =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 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =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
              _lowercase			 =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 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'      )
        with RequestCounter() as counter:
              _lowercase			 =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      )





from math import pow, sqrt
def   a   (    *A__			:     float  )					->   bool:





      """simple docstring"""



      _lowercase			 =len(A__  ) > 0 and all(value > 0.0 for value in values  )
      return result
def   a   (    A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__  )
          else ValueError('Input Error: Molar mass values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,     2  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )







def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,     2  ) / molar_mass   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )


