


"""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 			__snake_case   (lowerCamelCase  ):




							def __init__(     self:    List[str]     ,							A_:    NestedDataStructureLike[PathLike]     ,							A_:    Optional[NamedSplit] = None     ,							A_:    Optional[Features] = None     ,							A_:    str = None     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    Optional[str] = None     ,							A_:    Optional[int] = None     ,							**A_:    int     ,							):
										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_     ,							)
										__lowerCamelCase          =      field
										__lowerCamelCase          =      path_or_paths if isinstance(SCREAMING_SNAKE_CASE_     ,							SCREAMING_SNAKE_CASE_   ) else {self.split: path_or_paths}
										__lowerCamelCase          =      Json(
										    cache_dir=SCREAMING_SNAKE_CASE_     ,							data_files=SCREAMING_SNAKE_CASE_     ,							features=SCREAMING_SNAKE_CASE_     ,							field=SCREAMING_SNAKE_CASE_     ,							**SCREAMING_SNAKE_CASE_     ,							)




							def   __a   (     self:    Dict   ):
										# 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=SCREAMING_SNAKE_CASE_     ,							download_mode=SCREAMING_SNAKE_CASE_     ,							verification_mode=SCREAMING_SNAKE_CASE_     ,							base_path=SCREAMING_SNAKE_CASE_     ,							num_proc=self.num_proc     ,							)
													__lowerCamelCase          =      self.builder.as_dataset(
													    split=self.split     ,							verification_mode=SCREAMING_SNAKE_CASE_     ,							in_memory=self.keep_in_memory   )
										return dataset




class 			__snake_case   :




							def __init__(     self:    Union[str, Any]     ,							A_:    Dataset     ,							A_:    Union[PathLike, BinaryIO]     ,							A_:    Optional[int] = None     ,							A_:    Optional[int] = None     ,							**A_:    List[str]     ,							):
										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   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.to_json_kwargs.pop("""path_or_buf"""     ,							SCREAMING_SNAKE_CASE_   )
										__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"""     ,							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:
																__lowerCamelCase          =      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."""   )
													__lowerCamelCase          =      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   __a   (     self:    Dict     ,							A_:    Optional[Any]   ):
										__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      args

										__lowerCamelCase          =      query_table(
										    table=self.dataset.data     ,							key=slice(SCREAMING_SNAKE_CASE_     ,							offset + self.batch_size   )     ,							indices=self.dataset._indices     ,							)
										__lowerCamelCase          =      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   __a   (     self:    List[Any]     ,							A_:    BinaryIO     ,							A_:    List[str]     ,							A_:    List[Any]     ,							A_:    str     ,							**A_:    str     ,							):
										__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(SCREAMING_SNAKE_CASE_   )
										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     ,							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






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a         =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 							a		(				snake_case__:   Dict		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return (abs(source - target   ) / target) < 0.0_1




@pytest.mark.integration
def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   _TestCommandArgs(dataset=snake_case__		,  all_configs=snake_case__		,  save_infos=snake_case__   )
				lowercase_	    =   TestCommand(*snake_case__   )
				test_command.run()
				lowercase_	    =   os.path.join(snake_case__		,  '''README.md'''   )
				assert os.path.exists(snake_case__   )
				lowercase_	    =   DatasetInfosDict.from_directory(snake_case__   )
				lowercase_	    =   DatasetInfosDict(
				    {
				        '''default''': DatasetInfo(
				            features=Features(
				                {
				                    '''tokens''': Sequence(Value('''string'''   )   ),
				                    '''ner_tags''': Sequence(
				                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']   )   ),
				                    '''langs''': Sequence(Value('''string'''   )   ),
				                    '''spans''': Sequence(Value('''string'''   )   ),
				                }   )		,  splits=[
				                {
				                    '''name''': '''train''',
				                    '''num_bytes''': 2_351_563,
				                    '''num_examples''': 10_000,
				                },
				                {
				                    '''name''': '''validation''',
				                    '''num_bytes''': 238_418,
				                    '''num_examples''': 1_000,
				                },
				            ]		,  download_size=3_940_680		,  dataset_size=2_589_981		,  )
				    }   )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								lowercase_    ,      lowercase_	    =   getattr(dataset_infos['''default''']		,  snake_case__   ), getattr(expected_dataset_infos['''default''']		,  snake_case__   )
								if key == "num_bytes":
												assert is_apercent_close(snake_case__		,  snake_case__   )
								elif key == "splits":
												assert list(snake_case__   ) == list(snake_case__   )
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes		,  expected[split].num_bytes   )
								else:
												result == expected




