
'''simple docstring'''

import os

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

from .logging import get_logger


_UpperCAmelCase   :      List[Any] 						=  2 << 20  # 2mb
_UpperCAmelCase   :      Tuple 						=  get_logger(__name__)

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





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







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase=False	) ->      Optional[Any]:
		_UpperCamelCase       : Any    =      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:
						_UpperCamelCase       : Any    =      local_file.read(UpperCamelCase	)
						while chunk:
								remote_file.write(UpperCamelCase	)
								_UpperCamelCase       : Dict    =      local_file.read(UpperCamelCase	)







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase=False	) ->      Union[str, Any]:
		_UpperCamelCase       : str    =      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:
						_UpperCamelCase       : Any    =      remote_file.read(UpperCamelCase	)
						while chunk:
								local_file.write(UpperCamelCase	)
								_UpperCamelCase       : Optional[Any]    =      remote_file.read(UpperCamelCase	)







'''simple docstring'''

from typing import TYPE_CHECKING

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


_UpperCAmelCase   :      int 						=  {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase   :      int 						=  [
	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTSanJapaneseForConditionalGeneration""",
	    """GPTSanJapaneseModel""",
	    """GPTSanJapanesePreTrainedModel""",
	]
	_UpperCAmelCase   :      str 						=  [
	    """GPTSanJapaneseTokenizer""",
	]


if TYPE_CHECKING:
	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptsan_japanese import (
		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTSanJapaneseForConditionalGeneration,
		    GPTSanJapaneseModel,
		    GPTSanJapanesePreTrainedModel,
		)
		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
	import sys

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





