




"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							=			{
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			["DebertaV2TokenizerFast"]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDebertaV2ForMaskedLM",
          "TFDebertaV2ForQuestionAnswering",
          "TFDebertaV2ForSequenceClassification",
          "TFDebertaV2ForTokenClassification",
          "TFDebertaV2Model",
          "TFDebertaV2PreTrainedModel",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DebertaV2ForMaskedLM",
          "DebertaV2ForMultipleChoice",
          "DebertaV2ForQuestionAnswering",
          "DebertaV2ForSequenceClassification",
          "DebertaV2ForTokenClassification",
          "DebertaV2Model",
          "DebertaV2PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_deberta_va import (
          DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DebertaVaConfig,
          DebertaVaOnnxConfig,
      )
      from .tokenization_deberta_va import DebertaVaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deberta_va import (
                TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDebertaVaForMaskedLM,
                TFDebertaVaForQuestionAnswering,
                TFDebertaVaForSequenceClassification,
                TFDebertaVaForTokenClassification,
                TFDebertaVaModel,
                TFDebertaVaPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deberta_va import (
                DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                DebertaVaForMaskedLM,
                DebertaVaForMultipleChoice,
                DebertaVaForQuestionAnswering,
                DebertaVaForSequenceClassification,
                DebertaVaForTokenClassification,
                DebertaVaModel,
                DebertaVaPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











"""simple docstring"""



import os

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

from .logging import get_logger


SCREAMING_SNAKE_CASE							=			2 << 20  # 2mb
SCREAMING_SNAKE_CASE							=			get_logger(__name__)




class       UpperCAmelCase_    (		A_				):




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self._options.get_all_options().get("runner" )
     return runner in [None, "DirectRunner", "PortableRunner"]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						Dict:
 A__			=							FileSystems
 if fs.exists(lowercase_		):
  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(lowercase_		) as remote_file:
  with open(lowercase_     ,					"rb"		) as local_file:
   A__			=							local_file.read(lowercase_		)
   while chunk:
    remote_file.write(lowercase_		)
    A__			=							local_file.read(lowercase_		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						int:
 A__			=							FileSystems
 if os.path.exists(lowercase_		):
  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(lowercase_		) as remote_file:
  with open(lowercase_     ,					"wb"		) as local_file:
   A__			=							remote_file.read(lowercase_		)
   while chunk:
    local_file.write(lowercase_		)
    A__			=							remote_file.read(lowercase_		)





