
"""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  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = False							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							super().__init__(
							    UpperCAmelCase							,						split=UpperCAmelCase							,						features=UpperCAmelCase							,						cache_dir=UpperCAmelCase							,						keep_in_memory=UpperCAmelCase							,						streaming=UpperCAmelCase							,						num_proc=UpperCAmelCase							,						**UpperCAmelCase							,						)
							lowerCamelCase_					=  field
							lowerCamelCase_					=  path_or_paths if isinstance(UpperCAmelCase							,						UpperCAmelCase   ) else {self.split: path_or_paths}
							lowerCamelCase_					=  Json(
							    cache_dir=UpperCAmelCase							,						data_files=UpperCAmelCase							,						features=UpperCAmelCase							,						field=UpperCAmelCase							,						**UpperCAmelCase							,						)





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

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




class  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							if num_proc is not None and num_proc <= 0:
										raise ValueError(f"num_proc {num_proc} must be an integer > 0."   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.to_json_kwargs.pop('''path_or_buf'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''orient'''							,						'''records'''   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''lines'''							,						True if orient == '''records''' else False   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''index'''							,						False if orient in ['''split''', '''table'''] else True   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''compression'''							,						UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  args

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





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  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'''							,						):
													lowerCamelCase_					=  self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
													written += file_obj.write(UpperCAmelCase   )
							else:
										lowerCamelCase_		, lowerCamelCase_					=  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							,						UpperCAmelCase							,						UpperCAmelCase   )]							,						)							,						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(UpperCAmelCase   )

							return written





"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



