


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			)








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

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




