
import os

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

from .logging import get_logger


snake_case_      :					List[Any]				      =     2 << 2_0  # 2mb
snake_case_      :					Tuple				      =     get_logger(__name__)





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     self._options.get_all_options().get("runner"							)
								return runner in [None, "DirectRunner", "PortableRunner"]



def      __UpperCAmelCase    (  snake_case_							:		Dict				,					snake_case_							:		Tuple				,					snake_case_							:		int=False				):



	'''simple docstring'''



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



def      __UpperCAmelCase    (  snake_case_							:		Any				,					snake_case_							:		Dict				,					snake_case_							:		List[Any]=False				):



	'''simple docstring'''



	UpperCAmelCase:					int 						=     FileSystems
	if os.path.exists(snake_case_				):
		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(snake_case_				) as remote_file:
		with open(snake_case_				,					"wb"				) as local_file:
			UpperCAmelCase:					Dict 						=     remote_file.read(snake_case_				)
			while chunk:
				local_file.write(snake_case_				)
				UpperCAmelCase:					str 						=     remote_file.read(snake_case_				)


import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
