

'''simple docstring'''




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
  from .features import FeatureType



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[Any]     	= 42
  __lowerCAmelCase       :			Any     	= None
  # Automatically constructed
  __lowerCAmelCase       :			Optional[Any]     	= "dict"
  __lowerCAmelCase       :			List[str]     	= None
  __lowerCAmelCase       :			Optional[int]     	= field(default="Translation"			,							init=_lowerCamelCase			,							repr=_lowerCamelCase			)
  def __call__(  self  )				->					List[Any]:


    """simple docstring"""
    return pa.struct({lang: pa.string() for lang in sorted(self.languages  )}  )





  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    from .features import Value

    return {k: Value('string'  ) for k in sorted(self.languages  )}




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			int     	= None
  __lowerCAmelCase       :			Any     	= None
  __lowerCAmelCase       :			Any     	= None
  # Automatically constructed
  __lowerCAmelCase       :			List[str]     	= "dict"
  __lowerCAmelCase       :			Tuple     	= None
  __lowerCAmelCase       :			Any     	= field(default="TranslationVariableLanguages"			,							init=_lowerCamelCase			,							repr=_lowerCamelCase			)
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						sorted(set(self.languages  )  ) if self.languages else None
    UpperCAmelCase    				=						len(self.languages  ) if self.languages else None
  def __call__(  self  )				->					int:


    """simple docstring"""
    return pa.struct({'language': pa.list_(pa.string()  ), 'translation': pa.list_(pa.string()  )}  )
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						set(self.languages  )
    if self.languages and set(lowercase_  ) - lang_set:
      raise ValueError(
          F'''Some languages in example ({', '.join(sorted(set(lowercase_  ) - lang_set  )  )}) are not in valid set ({', '.join(lowercase_  )}).'''  )

    # Convert dictionary into tuples, splitting out cases where there are
    # multiple translations for a single language.
    UpperCAmelCase    				=						[]
    for lang, text in translation_dict.items():
      if isinstance(lowercase_ ,			lowercase_  ):
        translation_tuples.append((lang, text)  )
      else:
        translation_tuples.extend([(lang, el) for el in text]  )

        # Ensure translations are in ascending order by language code.
    UpperCAmelCase				,   UpperCAmelCase    				=						zip(*sorted(lowercase_  )  )

    return {"language": languages, "translation": translations}





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


    """simple docstring"""
    from .features import Sequence, Value

    return {
        "language": Sequence(Value('string'  )  ),
        "translation": Sequence(Value('string'  )  ),
    }








'''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 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

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







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

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


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									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=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**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.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

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


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							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'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							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			,							lowercase_			,							lowercase_   )]			,							)			,							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(lowercase_   )

									return written

