





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 SCREAMING_SNAKE_CASE_    (       snake_case_       ):






						def UpperCAmelCase_					(   self   :   List[str]						)				->     int:







							"""simple docstring"""


							snake_case_		:      List[str]           =						self._options.get_all_options().get('runner'						)
							return runner in [None, "DirectRunner", "PortableRunner"]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a=False ):
	snake_case_		:      List[str]           =						FileSystems
	if fs.exists(__a ):
		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(__a ) as remote_file:
		with open(__a ,			'rb' ) as local_file:
			snake_case_		:      Optional[Any]           =						local_file.read(__a )
			while chunk:
				remote_file.write(__a )
				snake_case_		:      Tuple           =						local_file.read(__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a=False ):
	snake_case_		:      List[Any]           =						FileSystems
	if os.path.exists(__a ):
		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(__a ) as remote_file:
		with open(__a ,			'wb' ) as local_file:
			snake_case_		:      Union[str, Any]           =						remote_file.read(__a )
			while chunk:
				local_file.write(__a )
				snake_case_		:      List[str]           =						remote_file.read(__a )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

