


'''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 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							Any):
           _A					:     Dict     = 0


    @slow
    def        A   (  self      :							List[Any]):
           for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
                  _A					:     Tuple     = 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():
                  _A					:     Dict     = 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      :							Optional[Any]):
           _A					:     List[Any]     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
           self.assertIsInstance(SCREAMING_SNAKE_CASE				,							(BertTokenizer, BertTokenizerFast))
           self.assertEqual(tokenizer.vocab_size				,							12)


    def        A   (  self      :							Union[str, Any]):
           _A					:     int     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
           self.assertIsInstance(SCREAMING_SNAKE_CASE				,							(RobertaTokenizer, RobertaTokenizerFast))
           self.assertEqual(tokenizer.vocab_size				,							20)


    def        A   (  self      :							Optional[Any]):
           _A					:     Dict     = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
           self.assertIsInstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           # Check that tokenizer_type ≠ model_type
           _A					:     Optional[Any]     = 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      :							str):
           with tempfile.TemporaryDirectory() as tmp_dir:
                  shutil.copy('./tests/fixtures/vocab.txt'				,							os.path.join(SCREAMING_SNAKE_CASE				,							'vocab.txt'))

                  _A					:     List[str]     = 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'))

                  _A					:     Union[str, Any]     = 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      :							List[str]):
           with tempfile.TemporaryDirectory() as tmp_dir:
                  shutil.copy('./tests/fixtures/vocab.txt'				,							os.path.join(SCREAMING_SNAKE_CASE				,							'vocab.txt'))

                  _A					:     Union[str, Any]     = 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'))

                  _A					:     str     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				,							tokenizer_type='gpt2')
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           with pytest.raises(SCREAMING_SNAKE_CASE):
                  AutoTokenizer.from_pretrained('./'				,							tokenizer_type='xxx')


    @require_tokenizers
    def        A   (  self      :							Union[str, Any]):
           for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
                  _A					:     str     = 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      :							Optional[Any]):
           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'				,							):
                         _A					:     List[str]     = tokenizer_class.from_pretrained('julien-c/herlolip-not-exists')


    def        A   (  self      :							int):
           # tests: https://github.com/huggingface/transformers/pull/13251
           # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
           # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
           _A					:     Optional[Any]     = TOKENIZER_MAPPING.values()
           _A					:     str     = []

           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      :							Tuple):
           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      :							Union[str, Any]):
           _A					:     List[str]     = AutoTokenizer.from_pretrained('distilbert-base-uncased'				,							do_lower_case=SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = 'Hello, world. How are you?'
           _A					:     Dict     = tokenizer.tokenize(SCREAMING_SNAKE_CASE)
           self.assertEqual('[UNK]'				,							tokens[0])

           _A					:     str     = AutoTokenizer.from_pretrained('microsoft/mpnet-base'				,							do_lower_case=SCREAMING_SNAKE_CASE)
           _A					:     Any     = tokenizer.tokenize(SCREAMING_SNAKE_CASE)
           self.assertEqual('[UNK]'				,							tokens[0])


    @require_tokenizers
    def        A   (  self      :							Union[str, Any]):
           _A					:     Tuple     = 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				,							30000)
           self.assertEqual(tokenizer.unk_token				,							'[UNK]')
           self.assertEqual(tokenizer.padding_side				,							'right')
           self.assertEqual(tokenizer.truncation_side				,							'right')


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[str]     = 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)
                  _A					:     List[Any]     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)

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


    def        A   (  self      :							Optional[Any]):
           _A					:     int     = 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      :							int):
           # Check we can load the tokenizer config of an online model.
           _A					:     Optional[Any]     = get_tokenizer_config('bert-base-cased')
           _A					:     Optional[int]     = 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.
           _A					:     Union[str, Any]     = get_tokenizer_config(SCREAMING_SNAKE_CASE)
           self.assertDictEqual(SCREAMING_SNAKE_CASE				,							{})

           # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
           _A					:     str     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
           with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)
                  _A					:     List[str]     = 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      :							List[str]):
           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)

                  _A					:     str     = CustomTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)

                         _A					:     Tuple     = 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      :							List[Any]):
           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:
                         _A					:     str     = BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE)
                         bert_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)
                         _A					:     str     = CustomTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE)

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

                         _A					:     Union[str, Any]     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
                         self.assertIsInstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                         _A					:     Dict     = 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      :							List[str]):
           # If remote code is not set, we will time out when asking whether to load the model.
           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  _A					:     Optional[Any]     = 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):
                  _A					:     Any     = AutoTokenizer.from_pretrained(
                      'hf-internal-testing/test_dynamic_tokenizer'				,							trust_remote_code=SCREAMING_SNAKE_CASE)

           _A					:     Optional[Any]     = 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)
                  _A					:     Optional[Any]     = 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
                  _A					:     Union[str, Any]     = 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)
                         _A					:     Union[str, Any]     = 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      :							List[str]):


           class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE__	):
               """simple docstring"""
               a													=   False




           class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE__	):
               """simple docstring"""
               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
                  _A					:     str     = 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=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.
                  _A					:     List[Any]     = 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)
                  _A					:     Dict     = 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
                  _A					:     Union[str, Any]     = 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)
                  _A					:     Optional[int]     = 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      :							Optional[int]):
           _A					:     int     = 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
                  _A					:     int     = 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      :							Optional[int]):
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE				,							'bert-base is not a local folder and is not a valid model identifier'):
                  _A					:     Tuple     = AutoTokenizer.from_pretrained('bert-base')


    def        A   (  self      :							Tuple):
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE				,							R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
                  _A					:     str     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				,							revision='aaaaaa')


    def        A   (  self      :							str):
           # Make sure we have cached the tokenizer.
           _A					:     Tuple     = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
           with RequestCounter() as counter:
                  _A					:     str     = 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)



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

   A				:					Optional[int]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






