

import torch
from torch import autograd





class      snake_case_      (					autograd.Function				):


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A       ,    _A       ,    _A				):
												__lowerCAmelCase       =				inputs.numel()
												__lowerCAmelCase       =				int(0.005 * nb_elems				) + 1
												if sigmoid:
																	__lowerCAmelCase       =				(torch.sigmoid(_A				) > threshold).type(inputs.type()				)
												else:
																	__lowerCAmelCase       =				(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
																	__lowerCAmelCase       =				inputs.flatten().kthvalue(max(nb_elems - nb_min       ,    1				)				).values
																	__lowerCAmelCase       =				(inputs > k_threshold).type(inputs.type()				)
												return mask




							@staticmethod
							def 							A__	(			_A       ,    _A				):
												return gradOutput, None, None





class      snake_case_      (					autograd.Function				):


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A       ,    _A				):
												__lowerCAmelCase       =				inputs.clone()
												__lowerCAmelCase,						__lowerCAmelCase       =				inputs.flatten().sort(descending=_A				)
												__lowerCAmelCase       =				int(threshold * inputs.numel()				)

												# flat_out and mask access the same memory.
												__lowerCAmelCase       =				mask.flatten()
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				1
												return mask




							@staticmethod
							def 							A__	(			_A       ,    _A				):
												return gradOutput, None





class      snake_case_      (					_a				):


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A				):
												__lowerCAmelCase       =				inputs.clone()
												__lowerCAmelCase,						__lowerCAmelCase       =				inputs.abs().flatten().sort(descending=_A				)
												__lowerCAmelCase       =				int(threshold * inputs.numel()				)

												# flat_out and mask access the same memory.
												__lowerCAmelCase       =				mask.flatten()
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				1
												return mask









import os

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

from .logging import get_logger


lowerCamelCase   					=  2 << 2_0  # 2mb
lowerCamelCase   					=  get_logger(__name__)





class      snake_case_      (					_a				):


							"""simple docstring"""





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


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				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:
															__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )
															while chunk:
																				remote_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				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:
															__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )
															while chunk:
																				local_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )






