





'''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					(a__   ):

							def __init__(							self							:   List[Any]				,   __magic_name__							:   Optional[int]				,   __magic_name__							:   Any = None				,   __magic_name__							:   Dict = None				,   __magic_name__							:   Union[str, Any] = None				,   __magic_name__							:   Union[str, Any] = False				,   __magic_name__							:   Union[str, Any] = False				,   __magic_name__							:   Tuple = None				,   __magic_name__							:   List[Any] = None				,   **__magic_name__							:   Union[str, Any]				,   )    ->				List[str]:







													"""simple docstring"""
													super().__init__(
													    __lowerCAmelCase				,   split=__lowerCAmelCase				,   features=__lowerCAmelCase				,   cache_dir=__lowerCAmelCase				,   keep_in_memory=__lowerCAmelCase				,   streaming=__lowerCAmelCase				,   num_proc=__lowerCAmelCase				,   **__lowerCAmelCase				,   )
													UpperCAmelCase_	:					List[Any]     =     field
													UpperCAmelCase_	:					Tuple     =     path_or_paths if isinstance(__lowerCAmelCase				,   __lowerCAmelCase      ) else {self.split: path_or_paths}
													UpperCAmelCase_	:					List[Any]     =     Json(
													    cache_dir=__lowerCAmelCase				,   data_files=__lowerCAmelCase				,   features=__lowerCAmelCase				,   field=__lowerCAmelCase				,   **__lowerCAmelCase				,   )



							def 			UpperCAmelCase__		(							self							:   List[Any]      )    ->				List[str]:







													"""simple docstring"""
													# Build iterable dataset
													if self.streaming:
																			UpperCAmelCase_	:					Union[str, Any]     =     self.builder.as_streaming_dataset(split=self.split      )
													# Build regular (map-style) dataset
													else:
																			UpperCAmelCase_	:					str     =     None
																			UpperCAmelCase_	:					Optional[int]     =     None
																			UpperCAmelCase_	:					List[str]     =     None
																			UpperCAmelCase_	:					List[str]     =     None

																			self.builder.download_and_prepare(
																			    download_config=__lowerCAmelCase				,   download_mode=__lowerCAmelCase				,   verification_mode=__lowerCAmelCase				,   base_path=__lowerCAmelCase				,   num_proc=self.num_proc				,   )
																			UpperCAmelCase_	:					Optional[int]     =     self.builder.as_dataset(
																			    split=self.split				,   verification_mode=__lowerCAmelCase				,   in_memory=self.keep_in_memory      )
													return dataset





class     __a					:

							def __init__(							self							:   Tuple				,   __magic_name__							:   int				,   __magic_name__							:   Tuple				,   __magic_name__							:   Optional[int] = None				,   __magic_name__							:   Any = None				,   **__magic_name__							:   Optional[int]				,   )    ->				Dict:







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

													UpperCAmelCase_	:					List[str]     =     dataset
													UpperCAmelCase_	:					Optional[Any]     =     path_or_buf
													UpperCAmelCase_	:					int     =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													UpperCAmelCase_	:					Optional[Any]     =     num_proc
													UpperCAmelCase_	:					Optional[int]     =     '''utf-8'''
													UpperCAmelCase_	:					List[str]     =     to_json_kwargs

							def 			UpperCAmelCase__		(							self							:   Any      )    ->				int:







													"""simple docstring"""
													UpperCAmelCase_	:					Optional[Any]     =     self.to_json_kwargs.pop('''path_or_buf'''				,   __lowerCAmelCase      )
													UpperCAmelCase_	:					Optional[Any]     =     self.to_json_kwargs.pop('''orient'''				,   '''records'''      )
													UpperCAmelCase_	:					Union[str, Any]     =     self.to_json_kwargs.pop('''lines'''				,   True if orient == '''records''' else False      )
													UpperCAmelCase_	:					Any     =     self.to_json_kwargs.pop('''index'''				,   False if orient in ['''split''', '''table'''] else True      )
													UpperCAmelCase_	:					str     =     self.to_json_kwargs.pop('''compression'''				,   __lowerCAmelCase      )

													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=__lowerCAmelCase      ) as buffer:
																									UpperCAmelCase_	:					Optional[Any]     =     self._write(file_obj=__lowerCAmelCase				,   orient=__lowerCAmelCase				,   lines=__lowerCAmelCase				,   index=__lowerCAmelCase				,   **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.'''      )
																			UpperCAmelCase_	:					Union[str, Any]     =     self._write(
																			    file_obj=self.path_or_buf				,   orient=__lowerCAmelCase				,   lines=__lowerCAmelCase				,   index=__lowerCAmelCase				,   **self.to_json_kwargs      )
													return written

							def 			UpperCAmelCase__		(							self							:   List[str]				,   __magic_name__							:   Optional[int]      )    ->				Any:







													"""simple docstring"""
													UpperCAmelCase_	:					List[Any]     =     args

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



							def 			UpperCAmelCase__		(							self							:   int				,   __magic_name__							:   str				,   __magic_name__							:   int				,   __magic_name__							:   Any				,   __magic_name__							:   Dict				,   **__magic_name__							:   Optional[Any]				,   )    ->				int:







													"""simple docstring"""
													UpperCAmelCase_	:					Optional[Any]     =     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'''				,   ):
																									UpperCAmelCase_	:					List[Any]     =     self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
																									written += file_obj.write(__lowerCAmelCase      )
													else:
																			UpperCAmelCase_	:					Tuple     =     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				,   __lowerCAmelCase				,   __lowerCAmelCase      )]				,   )				,   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(__lowerCAmelCase      )

													return written






'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

