


"""simple docstring"""






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							str:
          super().__init__(
              _SCREAMING_SNAKE_CASE    ,				split=_SCREAMING_SNAKE_CASE    ,				features=_SCREAMING_SNAKE_CASE    ,				cache_dir=_SCREAMING_SNAKE_CASE    ,				keep_in_memory=_SCREAMING_SNAKE_CASE    ,				streaming=_SCREAMING_SNAKE_CASE    ,				num_proc=_SCREAMING_SNAKE_CASE    ,				**_SCREAMING_SNAKE_CASE    ,				)
          _a    	=				field
          _a    	=				path_or_paths if isinstance(_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE							) else {self.split: path_or_paths}
          _a    	=				Json(
              cache_dir=_SCREAMING_SNAKE_CASE    ,				data_files=_SCREAMING_SNAKE_CASE    ,				features=_SCREAMING_SNAKE_CASE    ,				field=_SCREAMING_SNAKE_CASE    ,				**_SCREAMING_SNAKE_CASE    ,				)





     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:






          if self.streaming:
               _a    	=				self.builder.as_streaming_dataset(split=self.split							)
          # Build regular (map-style) dataset
          else:
               _a    	=				None
               _a    	=				None
               _a    	=				None
               _a    	=				None

               self.builder.download_and_prepare(
                   download_config=_SCREAMING_SNAKE_CASE    ,				download_mode=_SCREAMING_SNAKE_CASE    ,				verification_mode=_SCREAMING_SNAKE_CASE    ,				base_path=_SCREAMING_SNAKE_CASE    ,				num_proc=self.num_proc    ,				)
               _a    	=				self.builder.as_dataset(
                   split=self.split    ,				verification_mode=_SCREAMING_SNAKE_CASE    ,				in_memory=self.keep_in_memory							)
          return dataset







class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							str:






          if num_proc is not None and num_proc <= 0:
               raise ValueError(F'num_proc {num_proc} must be an integer > 0.'							)

          _a    	=				dataset
          _a    	=				path_or_buf
          _a    	=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          _a    	=				num_proc
          _a    	=				'''utf-8'''
          _a    	=				to_json_kwargs


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.to_json_kwargs.pop('''path_or_buf'''    ,				_SCREAMING_SNAKE_CASE							)
          _a    	=				self.to_json_kwargs.pop('''orient'''    ,				'''records'''							)
          _a    	=				self.to_json_kwargs.pop('''lines'''    ,				True if orient == '''records''' else False							)
          _a    	=				self.to_json_kwargs.pop('''index'''    ,				False if orient in ['''split''', '''table'''] else True							)
          _a    	=				self.to_json_kwargs.pop('''compression'''    ,				_SCREAMING_SNAKE_CASE							)

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
               raise NotImplementedError(F'`datasets` currently does not support {compression} compression'							)

          if isinstance(self.path_or_buf    ,				(str, bytes, os.PathLike)							):
               with fsspec.open(self.path_or_buf    ,				'''wb'''    ,				compression=_SCREAMING_SNAKE_CASE							) as buffer:
                    _a    	=				self._write(file_obj=_SCREAMING_SNAKE_CASE    ,				orient=_SCREAMING_SNAKE_CASE    ,				lines=_SCREAMING_SNAKE_CASE    ,				index=_SCREAMING_SNAKE_CASE    ,				**self.to_json_kwargs							)
          else:
               if compression:
                    raise NotImplementedError(
                        F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                        ''' was passed. Please provide a local path instead.'''							)
               _a    	=				self._write(
                   file_obj=self.path_or_buf    ,				orient=_SCREAMING_SNAKE_CASE    ,				lines=_SCREAMING_SNAKE_CASE    ,				index=_SCREAMING_SNAKE_CASE    ,				**self.to_json_kwargs							)
          return written


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Dict:
          _a	,      _a	,      _a	,      _a	,      _a    	=				args

          _a    	=				query_table(
              table=self.dataset.data    ,				key=slice(_SCREAMING_SNAKE_CASE    ,				offset + self.batch_size							)    ,				indices=self.dataset._indices    ,				)
          _a    	=				batch.to_pandas().to_json(
              path_or_buf=_SCREAMING_SNAKE_CASE    ,				orient=_SCREAMING_SNAKE_CASE    ,				lines=_SCREAMING_SNAKE_CASE    ,				index=_SCREAMING_SNAKE_CASE    ,				**_SCREAMING_SNAKE_CASE							)
          if not json_str.endswith('''\n'''							):
               json_str += "\n"
          return json_str.encode(self.encoding							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)       ->							int:
          _a    	=				0

          if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0    ,				len(self.dataset							)    ,				self.batch_size							)    ,				unit='''ba'''    ,				disable=not logging.is_progress_bar_enabled()    ,				desc='''Creating json from Arrow format'''    ,				):
                    _a    	=				self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
                    written += file_obj.write(_SCREAMING_SNAKE_CASE							)
          else:
               _a	,      _a    	=				len(self.dataset							), self.batch_size
               with multiprocessing.Pool(self.num_proc							) as pool:
                    for json_str in logging.tqdm(
                        pool.imap(
                            self._batch_json    ,				[(offset, orient, lines, index, to_json_kwargs) for offset in range(0    ,				_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE							)]    ,				)    ,				total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size    ,				unit='''ba'''    ,				disable=not logging.is_progress_bar_enabled()    ,				desc='''Creating json from Arrow format'''    ,				):
                         written += file_obj.write(_SCREAMING_SNAKE_CASE							)

          return written



"""simple docstring"""






from pathlib import Path

import fire
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				Path(_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       )
     dest_dir.mkdir(exist_ok=_lowerCAmelCase       )
     for path in src_dir.iterdir():
          _a    	=				[x.rstrip() for x in list(path.open().readlines()       )][:n]
          _a    	=				dest_dir.joinpath(path.name       )
          print(_lowerCAmelCase       )
          dest_path.open('''w'''       ).write('''\n'''.join(_lowerCAmelCase       )       )


if __name__ == "__main__":
   fire.Fire(minify)