





import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


_lowerCAmelCase							:      Dict						=	datasets.utils.logging.get_logger(__name__)


@dataclass
class 					__magic_name__  (							datasets.BuilderConfig							):





  """simple docstring"""



  __UpperCamelCase					    =     None
  __UpperCamelCase					    =     '''utf-8'''
  __UpperCamelCase					    =     None
  __UpperCamelCase					    =     10 << 20  # 10MB
  __UpperCamelCase					    =     False
  __UpperCamelCase					    =     '''line'''




class 					__magic_name__  (							datasets.ArrowBasedBuilder							):





  """simple docstring"""



  __UpperCamelCase					    =     TextConfig



  def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





     '''simple docstring'''




     return datasets.DatasetInfo(features=self.config.features  )



  def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :int  ):





     '''simple docstring'''




     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}"  )
     A_					:				Union[str, Any]          =			dl_manager.download_and_extract(self.config.data_files  )
     if isinstance(__a			,      (str, list, tuple)  ):
        A_					:				Optional[int]          =			data_files
        if isinstance(__a			,      __a  ):
           A_					:				int          =			[files]
        A_					:				Any          =			[dl_manager.iter_files(__a  ) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={"files": files}  )]
     A_					:				List[Any]          =			[]
     for split_name, files in data_files.items():
        if isinstance(__a			,      __a  ):
           A_					:				Dict          =			[files]
        A_					:				List[str]          =			[dl_manager.iter_files(__a  ) for file in files]
        splits.append(datasets.SplitGenerator(name=__a			,      gen_kwargs={"files": files}  )  )
     return splits



  def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :List[Any]  ):





     '''simple docstring'''




     if self.config.features is not None:
        A_					:				Any          =			self.config.features.arrow_schema
        if all(not require_storage_cast(__a  ) for feature in self.config.features.values()  ):
           # cheaper cast
           A_					:				Tuple          =			pa_table.cast(__a  )
        else:
           # more expensive cast; allows str <-> int/float or str to Audio for example
           A_					:				Any          =			table_cast(__a			,      __a  )
        return pa_table
     else:
        return pa_table.cast(pa.schema({"text": pa.string()}  )  )





  def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :Dict  ):





     '''simple docstring'''




     A_					:				str          =			list(self.config.features  ) if self.config.features is not None else ['text']
     for file_idx, file in enumerate(itertools.chain.from_iterable(__a  )  ):
        # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
        with open(__a			,      encoding=self.config.encoding			,      errors=self.config.errors  ) as f:
           if self.config.sample_by == "line":
              A_					:				List[Any]          =			0
              while True:
                 A_					:				Union[str, Any]          =			f.read(self.config.chunksize  )
                 if not batch:
                    break
                 batch += f.readline()  # finish current line
                 # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                 A_					:				int          =			StringIO(__a  ).readlines()
                 if not self.config.keep_linebreaks:
                    A_					:				int          =			[line.rstrip("\n"  ) for line in batch]
                 A_					:				str          =			pa.Table.from_arrays([pa.array(__a  )]			,      names=__a  )
                 # 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 (file_idx, batch_idx), self._cast_table(__a  )
                 batch_idx += 1
           elif self.config.sample_by == "paragraph":
              A_					:				List[Any]          =			0
              A_					:				int          =			''
              while True:
                 A_					:				Tuple          =			f.read(self.config.chunksize  )
                 if not new_batch:
                    break
                 batch += new_batch
                 batch += f.readline()  # finish current line
                 A_					:				str          =			batch.split("\n\n"  )
                 A_					:				Optional[Any]          =			pa.Table.from_arrays(
                     [pa.array([example for example in batch[:-1] if example]  )]			,      names=__a  )
                 # 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 (file_idx, batch_idx), self._cast_table(__a  )
                 batch_idx += 1
                 A_					:				Union[str, Any]          =			batch[-1]
              if batch:
                 A_					:				Tuple          =			pa.Table.from_arrays([pa.array([batch]  )]			,      names=__a  )
                 yield (file_idx, batch_idx), self._cast_table(__a  )
           elif self.config.sample_by == "document":
              A_					:				Dict          =			f.read()
              A_					:				Any          =			pa.Table.from_arrays([pa.array([text]  )]			,      names=__a  )
              yield file_idx, self._cast_table(__a  )







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[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
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

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