






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE 							=						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						['AlbertTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						['AlbertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						[
      'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AlbertForMaskedLM',
      'AlbertForMultipleChoice',
      'AlbertForPreTraining',
      'AlbertForQuestionAnswering',
      'AlbertForSequenceClassification',
      'AlbertForTokenClassification',
      'AlbertModel',
      'AlbertPreTrainedModel',
      'load_tf_weights_in_albert',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						[
      'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFAlbertForMaskedLM',
      'TFAlbertForMultipleChoice',
      'TFAlbertForPreTraining',
      'TFAlbertForQuestionAnswering',
      'TFAlbertForSequenceClassification',
      'TFAlbertForTokenClassification',
      'TFAlbertMainLayer',
      'TFAlbertModel',
      'TFAlbertPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						[
      'FlaxAlbertForMaskedLM',
      'FlaxAlbertForMultipleChoice',
      'FlaxAlbertForPreTraining',
      'FlaxAlbertForQuestionAnswering',
      'FlaxAlbertForSequenceClassification',
      'FlaxAlbertForTokenClassification',
      'FlaxAlbertModel',
      'FlaxAlbertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_albert import AlbertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_albert_fast import AlbertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_albert import (
        ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
        AlbertPreTrainedModel,
        load_tf_weights_in_albert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_albert import (
        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAlbertForMaskedLM,
        TFAlbertForMultipleChoice,
        TFAlbertForPreTraining,
        TFAlbertForQuestionAnswering,
        TFAlbertForSequenceClassification,
        TFAlbertForTokenClassification,
        TFAlbertMainLayer,
        TFAlbertModel,
        TFAlbertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_albert import (
        FlaxAlbertForMaskedLM,
        FlaxAlbertForMultipleChoice,
        FlaxAlbertForPreTraining,
        FlaxAlbertForQuestionAnswering,
        FlaxAlbertForSequenceClassification,
        FlaxAlbertForTokenClassification,
        FlaxAlbertModel,
        FlaxAlbertPreTrainedModel,
    )
else:
  import sys

  SCREAMING_SNAKE_CASE 							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402


SCREAMING_SNAKE_CASE 							=						get_tests_dir('fixtures/dummy-config.json')
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				0


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     self.assertIsNotNone(transformers.models.auto.__spec__)
     self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto'''))


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				AutoConfig.from_pretrained('''bert-base-uncased''')
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				AutoConfig.from_pretrained(_A)
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				AutoConfig.from_pretrained(_A)
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				AutoConfig.for_model('''roberta''')
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as tmp_dir:
         # This model name contains bert and roberta, but roberta ends up being picked.
         _UpperCAmelCase				:      str			  =				os.path.join(_A							,  '''fake-roberta''')
         os.makedirs(_A							,  exist_ok=_A)
         with open(os.path.join(_A							,  '''config.json''')							,  '''w''') as f:
             f.write(json.dumps({}))
         _UpperCAmelCase				:      Any			  =				AutoConfig.from_pretrained(_A)
         self.assertEqual(type(_A)							,  _A)


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     try:
         AutoConfig.register('''custom'''							,  _A)
         # Wrong model type will raise an error
         with self.assertRaises(_A):
             AutoConfig.register('''model'''							,  _A)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(_A):
             AutoConfig.register('''bert'''							,  _A)

         # Now that the config is registered, it can be used as any other config with the auto-API
         _UpperCAmelCase				:      Tuple			  =				CustomConfig()
         with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(_A)
             _UpperCAmelCase				:      Tuple			  =				AutoConfig.from_pretrained(_A)
             self.assertIsInstance(_A							,  _A)

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


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     with self.assertRaisesRegex(
         _A							,  '''bert-base is not a local folder and is not a valid model identifier'''):
         _UpperCAmelCase				:      Tuple			  =				AutoConfig.from_pretrained('''bert-base''')


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     with self.assertRaisesRegex(
         _A							,  '''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.'''							,  ):
         _UpperCAmelCase				:      List[str]			  =				AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo''')


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     with self.assertRaises(_A):
         _UpperCAmelCase				:      Dict			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''')
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_A):
         _UpperCAmelCase				:      Optional[int]			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)

     _UpperCAmelCase				:      int			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)
     self.assertEqual(config.__class__.__name__							,  '''NewModelConfig''')

     # Test config can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
         config.save_pretrained(_A)
         _UpperCAmelCase				:      Any			  =				AutoConfig.from_pretrained(_A							,  trust_remote_code=_A)
     self.assertEqual(reloaded_config.__class__.__name__							,  '''NewModelConfig''')


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     class 							A_    (						__lowercase			):






      '''simple docstring'''




      _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					"new-model"

     try:
         AutoConfig.register('''new-model'''							,  _A)
         # If remote code is not set, the default is to use local
         _UpperCAmelCase				:      List[str]			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''')
         self.assertEqual(config.__class__.__name__							,  '''NewModelConfigLocal''')

         # If remote code is disabled, we load the local one.
         _UpperCAmelCase				:      List[Any]			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)
         self.assertEqual(config.__class__.__name__							,  '''NewModelConfigLocal''')

         # If remote is enabled, we load from the Hub
         _UpperCAmelCase				:      Dict			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)
         self.assertEqual(config.__class__.__name__							,  '''NewModelConfig''')

     finally:
         if "new-model" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["new-model"]




