



"""simple docstring"""







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase:  Tuple			     =				2 << 20  # 2mb
UpperCAmelCase:  Tuple			     =				get_logger(__name__)





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   self._options.get_all_options().get("""runner"""   )
			return runner in [None, "DirectRunner", "PortableRunner"]







def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=False      ):
		_lowercase				: Dict     =   FileSystems
		if fs.exists(__UpperCAmelCase      ):
				if force_upload:
						logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""      )
				else:
						logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""      )
						return
		with fs.create(__UpperCAmelCase      ) as remote_file:
				with open(__UpperCAmelCase   ,				"""rb"""      ) as local_file:
						_lowercase				: Optional[Any]     =   local_file.read(__UpperCAmelCase      )
						while chunk:
								remote_file.write(__UpperCAmelCase      )
								_lowercase				: Tuple     =   local_file.read(__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=False      ):
		_lowercase				: Optional[int]     =   FileSystems
		if os.path.exists(__UpperCAmelCase      ):
				if force_download:
						logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""      )
				else:
						logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""      )
						return
		with fs.open(__UpperCAmelCase      ) as remote_file:
				with open(__UpperCAmelCase   ,				"""wb"""      ) as local_file:
						_lowercase				: List[str]     =   remote_file.read(__UpperCAmelCase      )
						while chunk:
								local_file.write(__UpperCAmelCase      )
								_lowercase				: List[Any]     =   remote_file.read(__UpperCAmelCase      )








"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase				:     List[Any]								=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
