



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,
)


__UpperCAmelCase			  =			{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

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

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase			  =			[
        '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''XLMRobertaForCausalLM''',
        '''XLMRobertaForMaskedLM''',
        '''XLMRobertaForMultipleChoice''',
        '''XLMRobertaForQuestionAnswering''',
        '''XLMRobertaForSequenceClassification''',
        '''XLMRobertaForTokenClassification''',
        '''XLMRobertaModel''',
        '''XLMRobertaPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase			  =			[
        '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFXLMRobertaForCausalLM''',
        '''TFXLMRobertaForMaskedLM''',
        '''TFXLMRobertaForMultipleChoice''',
        '''TFXLMRobertaForQuestionAnswering''',
        '''TFXLMRobertaForSequenceClassification''',
        '''TFXLMRobertaForTokenClassification''',
        '''TFXLMRobertaModel''',
        '''TFXLMRobertaPreTrainedModel''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase			  =			[
        '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''FlaxXLMRobertaForMaskedLM''',
        '''FlaxXLMRobertaForCausalLM''',
        '''FlaxXLMRobertaForMultipleChoice''',
        '''FlaxXLMRobertaForQuestionAnswering''',
        '''FlaxXLMRobertaForSequenceClassification''',
        '''FlaxXLMRobertaForTokenClassification''',
        '''FlaxXLMRobertaModel''',
        '''FlaxXLMRobertaPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_xlm_roberta import (
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLMRobertaConfig,
        XLMRobertaOnnxConfig,
    )

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlm_roberta import XLMRobertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm_roberta import (
            XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMRobertaForCausalLM,
            XLMRobertaForMaskedLM,
            XLMRobertaForMultipleChoice,
            XLMRobertaForQuestionAnswering,
            XLMRobertaForSequenceClassification,
            XLMRobertaForTokenClassification,
            XLMRobertaModel,
            XLMRobertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm_roberta import (
            TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMRobertaForCausalLM,
            TFXLMRobertaForMaskedLM,
            TFXLMRobertaForMultipleChoice,
            TFXLMRobertaForQuestionAnswering,
            TFXLMRobertaForSequenceClassification,
            TFXLMRobertaForTokenClassification,
            TFXLMRobertaModel,
            TFXLMRobertaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_xlm_roberta import (
            FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            FlaxXLMRobertaForCausalLM,
            FlaxXLMRobertaForMaskedLM,
            FlaxXLMRobertaForMultipleChoice,
            FlaxXLMRobertaForQuestionAnswering,
            FlaxXLMRobertaForSequenceClassification,
            FlaxXLMRobertaForTokenClassification,
            FlaxXLMRobertaModel,
            FlaxXLMRobertaPreTrainedModel,
        )

else:
    import sys

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









'''simple docstring'''







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowercase      :   List[str]      =       """."""


if __name__ == "__main__":
      lowercase      :   List[str]      =       os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
      lowercase      :   Union[str, Any]      =       []
      lowercase      :   List[str]      =       []
      with open(doctest_file_path) as fp:
            for line in fp:
                  lowercase      :   List[str]      =       line.strip()
                  lowercase      :   Optional[Any]      =       os.path.join(REPO_PATH, line)
                  if not (os.path.isfile(path) or os.path.isdir(path)):
                        non_existent_paths.append(line)
                  all_paths.append(path)
      if len(non_existent_paths) > 0:
            lowercase      :   int      =       """\n""".join(non_existent_paths)
            raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
      if all_paths != sorted(all_paths):
            raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")



