


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					str			=	{
    """configuration_mpnet""": ["""MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MPNetConfig"""],
    """tokenization_mpnet""": ["""MPNetTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	["""MPNetTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MPNetForMaskedLM""",
       """MPNetForMultipleChoice""",
       """MPNetForQuestionAnswering""",
       """MPNetForSequenceClassification""",
       """MPNetForTokenClassification""",
       """MPNetLayer""",
       """MPNetModel""",
       """MPNetPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Tuple			=	[
       """TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFMPNetEmbeddings""",
       """TFMPNetForMaskedLM""",
       """TFMPNetForMultipleChoice""",
       """TFMPNetForQuestionAnswering""",
       """TFMPNetForSequenceClassification""",
       """TFMPNetForTokenClassification""",
       """TFMPNetMainLayer""",
       """TFMPNetModel""",
       """TFMPNetPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
   from .tokenization_mpnet import MPNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mpnet_fast import MPNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mpnet import (
          MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetLayer,
          MPNetModel,
          MPNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mpnet import (
          TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMPNetEmbeddings,
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetMainLayer,
          TFMPNetModel,
          TFMPNetPreTrainedModel,
      )

else:
   import sys

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





import os

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

from .logging import get_logger


_SCREAMING_SNAKE_CASE :					Optional[int]			=	2 << 20  # 2mb
_SCREAMING_SNAKE_CASE :					Optional[Any]			=	get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
           UpperCamelCase__							:  int								=     self._options.get_all_options().get('''runner'''   )
           return runner in [None, "DirectRunner", "PortableRunner"]







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[str]=False			)  ->   Optional[int]:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     FileSystems
     if fs.exists(__lowerCamelCase			):
          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(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''rb'''			) as local_file:
               UpperCamelCase__							:  List[Any]								=     local_file.read(__lowerCamelCase			)
               while chunk:
                    remote_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Union[str, Any]								=     local_file.read(__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					Any		,__lowerCamelCase			:					int		,__lowerCamelCase			:					str=False			)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     FileSystems
     if os.path.exists(__lowerCamelCase			):
          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(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''wb'''			) as local_file:
               UpperCamelCase__							:  Any								=     remote_file.read(__lowerCamelCase			)
               while chunk:
                    local_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Dict								=     remote_file.read(__lowerCamelCase			)




