



"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					datasets.utils.logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_(      datasets.BuilderConfig			):





					'''simple docstring'''



					lowercase__      :				Optional[datasets.Features]        =       None


class 							lowerCamelCase_(      datasets.ArrowBasedBuilder			):





					'''simple docstring'''



					lowercase__      :				int        =       ArrowConfig

					def 	snake_case__					(			self      ):
									return datasets.DatasetInfo(features=self.config.features      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):

									if not self.config.data_files:
													raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
									_lowerCamelCase					     = dl_manager.download_and_extract(self.config.data_files      )
									if isinstance(__SCREAMING_SNAKE_CASE ,				(str, list, tuple)      ):
													_lowerCamelCase					     = data_files
													if isinstance(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      ):
																	_lowerCamelCase					     = [files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													_lowerCamelCase					     = [dl_manager.iter_files(__SCREAMING_SNAKE_CASE      ) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={'''files''': files}      )]
									_lowerCamelCase					     = []
									for split_name, files in data_files.items():
													if isinstance(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      ):
																	_lowerCamelCase					     = [files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													_lowerCamelCase					     = [dl_manager.iter_files(__SCREAMING_SNAKE_CASE      ) for file in files]
													# Infer features is they are stoed in the arrow schema
													if self.info.features is None:
																	for file in itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE      ):
																					with open(__SCREAMING_SNAKE_CASE ,				'''rb'''      ) as f:
																									_lowerCamelCase					     = datasets.Features.from_arrow_schema(pa.ipc.open_stream(__SCREAMING_SNAKE_CASE      ).schema      )
																					break
													splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE ,				gen_kwargs={'''files''': files}      )      )
									return splits

					def 	snake_case__					(			self ,				lowerCamelCase__      ):

									if self.info.features is not None:
													# more expensive cast to support nested features with keys in a different order
													# allows str <-> int/float or str to Audio for example
													_lowerCamelCase					     = table_cast(__SCREAMING_SNAKE_CASE ,				self.info.features.arrow_schema      )
									return pa_table





					def 	snake_case__					(			self ,				lowerCamelCase__      ):

									for file_idx, file in enumerate(itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE      )      ):
													with open(__SCREAMING_SNAKE_CASE ,				'''rb'''      ) as f:
																	try:
																					for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__SCREAMING_SNAKE_CASE      )      ):
																									_lowerCamelCase					     = pa.Table.from_batches([record_batch]      )
																									# Uncomment for debugging (will print the Arrow table size and elements)
																									# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																									# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																									yield F"""{file_idx}_{batch_idx}""", self._cast_table(__SCREAMING_SNAKE_CASE      )
																	except ValueError as e:
																					logger.error(F"""Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE      )}: {e}"""      )
																					raise







'''simple docstring'''







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > 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   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

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







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

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

