"""simple docstring"""






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCAmelCase					: str        =abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					Optional[Any]:
      '''simple docstring'''


      config.addinivalue_line(
          """markers"""      ,	"""is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"""	)
      config.addinivalue_line(
          """markers"""      ,	"""is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"""	)
      config.addinivalue_line("""markers"""      ,	"""is_pipeline_test: mark test to run only when pipelines are tested"""	)
      config.addinivalue_line("""markers"""      ,	"""is_staging_test: mark test to run only in the staging environment"""	)
      config.addinivalue_line("""markers"""      ,	"""accelerate_tests: mark test that require accelerate"""	)
      config.addinivalue_line("""markers"""      ,	"""tool_tests: mark the tool tests that are run on their specific schedule"""	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Optional[Any]:
      '''simple docstring'''


      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Optional[int]:
      '''simple docstring'''


      from transformers.testing_utils import pytest_terminal_summary_main

      lowercase														=			terminalreporter.config.getoption("""--make-reports"""	)
      if make_reports:
            pytest_terminal_summary_main(lowerCAmelCase__      ,	id=lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[Any]	)				->					str:
      '''simple docstring'''


      if exitstatus == 5:
            lowercase														=			0


# Doctest custom flag to ignore output.
__lowerCAmelCase					: Union[str, Any]        =doctest.register_optionflag("""IGNORE_RESULT""")

__lowerCAmelCase					: str        =doctest.OutputChecker





class 				_A		( lowerCAmelCase			):

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if IGNORE_RESULT & optionflags:
                  return True
            return OutputChecker.check_output(self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)






__lowerCAmelCase					: List[Any]        =CustomOutputChecker
__lowerCAmelCase					: Tuple        =HfDoctestModule






__lowerCAmelCase					: Optional[Any]        =HfDocTestParser




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase					: Dict        ={
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: int        =[
           """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XLMForMultipleChoice""",
           """XLMForQuestionAnswering""",
           """XLMForQuestionAnsweringSimple""",
           """XLMForSequenceClassification""",
           """XLMForTokenClassification""",
           """XLMModel""",
           """XLMPreTrainedModel""",
           """XLMWithLMHeadModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: Union[str, Any]        =[
           """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFXLMForMultipleChoice""",
           """TFXLMForQuestionAnsweringSimple""",
           """TFXLMForSequenceClassification""",
           """TFXLMForTokenClassification""",
           """TFXLMMainLayer""",
           """TFXLMModel""",
           """TFXLMPreTrainedModel""",
           """TFXLMWithLMHeadModel""",
       ]


if TYPE_CHECKING:
       from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
       from .tokenization_xlm import XLMTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm import (
                  XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMForMultipleChoice,
                  XLMForQuestionAnswering,
                  XLMForQuestionAnsweringSimple,
                  XLMForSequenceClassification,
                  XLMForTokenClassification,
                  XLMModel,
                  XLMPreTrainedModel,
                  XLMWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm import (
                  TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMForMultipleChoice,
                  TFXLMForQuestionAnsweringSimple,
                  TFXLMForSequenceClassification,
                  TFXLMForTokenClassification,
                  TFXLMMainLayer,
                  TFXLMModel,
                  TFXLMPreTrainedModel,
                  TFXLMWithLMHeadModel,
              )

else:
       import sys

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



