






# 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.
__snake_case      :      int             =       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				(      UpperCamelCase_    :   Any						)					->				Tuple:

      """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				(      UpperCamelCase_    :   Any						)					->				Tuple:

      """simple docstring"""



      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Union[str, Any]:

      """simple docstring"""



      from transformers.testing_utils import pytest_terminal_summary_main

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Union[str, Any]						)					->				Any:

      """simple docstring"""



      if exitstatus == 5:
            lowerCAmelCase__        =  0


# Doctest custom flag to ignore output.
__snake_case      :      str             =       doctest.register_optionflag("""IGNORE_RESULT""")

__snake_case      :      Optional[int]             =       doctest.OutputChecker


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            if IGNORE_RESULT & optionflags:
                  return True
            return OutputChecker.check_output(self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)




__snake_case      :      int             =       CustomOutputChecker
__snake_case      :      Optional[Any]             =       HfDoctestModule


__snake_case      :      List[Any]             =       HfDocTestParser













from typing import TYPE_CHECKING

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


__snake_case      :      Union[str, Any]             =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      List[str]             =       ["""ConvBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Optional[Any]             =       [
					    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvBertForMaskedLM""",
					    """ConvBertForMultipleChoice""",
					    """ConvBertForQuestionAnswering""",
					    """ConvBertForSequenceClassification""",
					    """ConvBertForTokenClassification""",
					    """ConvBertLayer""",
					    """ConvBertModel""",
					    """ConvBertPreTrainedModel""",
					    """load_tf_weights_in_convbert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Union[str, Any]             =       [
					    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFConvBertForMaskedLM""",
					    """TFConvBertForMultipleChoice""",
					    """TFConvBertForQuestionAnswering""",
					    """TFConvBertForSequenceClassification""",
					    """TFConvBertForTokenClassification""",
					    """TFConvBertLayer""",
					    """TFConvBertModel""",
					    """TFConvBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

					__snake_case      :      Dict             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





