import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCAmelCase_			    =							'''
import os
'''

UpperCAmelCase_			    =							'''
def foo():
    import os
    return False
'''

UpperCAmelCase_			    =							'''
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
'''

UpperCAmelCase_			    =							'''
import os

try:
    import bar
except ImportError:
    raise ValueError()
'''

UpperCAmelCase_			    =							'''
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
'''

UpperCAmelCase_			    =							'''
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
'''

UpperCAmelCase_			    =							'''
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
'''

UpperCAmelCase_			    =							'''
import os

try:
    import bar
except:
    raise ValueError()
'''

UpperCAmelCase_			    =							'''
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
'''

UpperCAmelCase_			    =							'''
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
'''

UpperCAmelCase_			    =							[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]




@pytest.mark.parametrize('''case'''  ,					lowercase		)
def       lowerCAmelCase_     (		lowercase:							Any  ,					lowercase:							List[str]		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= os.path.join(lowercase  ,					'''test_file.py'''		)
 with open(lowercase  ,					'''w'''		) as _tmp_file:
  _tmp_file.write(lowercase		)

 _UpperCamelCase:     Optional[Any]							= get_imports(lowercase		)
 assert parsed_imports == ["os"]
from typing import TYPE_CHECKING

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


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

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