'''simple docstring'''


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

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






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = False ,						snake_case_ = False ,						snake_case_ = None ,						**snake_case_ ,						):




       '''simple docstring'''

       super().__init__(
           snake_case_ ,						split=snake_case_ ,						features=snake_case_ ,						cache_dir=snake_case_ ,						keep_in_memory=snake_case_ ,						streaming=snake_case_ ,						num_proc=snake_case_ ,						**snake_case_ ,						)
       UpperCAmelCase_				:  Union[str, Any]  = path_or_paths if isinstance(snake_case_ ,						snake_case_		) else {self.split: path_or_paths}
       UpperCAmelCase_				:  List[Any]  = Csv(
           cache_dir=snake_case_ ,						data_files=snake_case_ ,						features=snake_case_ ,						**snake_case_ ,						)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

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







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = None ,						**snake_case_ ,						):




       '''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_				:  Optional[int]  = dataset
       UpperCAmelCase_				:  Optional[int]  = path_or_buf
       UpperCAmelCase_				:  Any  = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       UpperCAmelCase_				:  int  = num_proc
       UpperCAmelCase_				:  Dict  = 'utf-8'
       UpperCAmelCase_				:  Tuple  = to_csv_kwargs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.to_csv_kwargs.pop('path_or_buf' ,						snake_case_		)
       UpperCAmelCase_				:  str  = self.to_csv_kwargs.pop('header' ,						snake_case_		)
       UpperCAmelCase_				:  Tuple  = self.to_csv_kwargs.pop('index' ,						snake_case_		)

       if isinstance(self.path_or_buf ,						(str, bytes, os.PathLike)		):
            with open(self.path_or_buf ,						'wb+'		) as buffer:
                 UpperCAmelCase_				:  Optional[int]  = self._write(file_obj=snake_case_ ,						header=snake_case_ ,						index=snake_case_ ,						**self.to_csv_kwargs		)
       else:
            UpperCAmelCase_				:  Union[str, Any]  = self._write(file_obj=self.path_or_buf ,						header=snake_case_ ,						index=snake_case_ ,						**self.to_csv_kwargs		)
       return written


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = args

       UpperCAmelCase_				:  List[Any]  = query_table(
           table=self.dataset.data ,						key=slice(snake_case_ ,						offset + self.batch_size		) ,						indices=self.dataset._indices ,						)
       UpperCAmelCase_				:  Dict  = batch.to_pandas().to_csv(
           path_or_buf=snake_case_ ,						header=header if (offset == 0) else False ,						index=snake_case_ ,						**snake_case_		)
       return csv_str.encode(self.encoding		)






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						**snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = 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 CSV from Arrow format' ,						):
                 UpperCAmelCase_				:  Any  = self._batch_csv((offset, header, index, to_csv_kwargs)		)
                 written += file_obj.write(snake_case_		)

       else:
            UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = len(self.dataset		), self.batch_size
            with multiprocessing.Pool(self.num_proc		) as pool:
                 for csv_str in logging.tqdm(
                     pool.imap(
                         self._batch_csv ,						[(offset, header, index, to_csv_kwargs) for offset in range(0 ,						snake_case_ ,						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 CSV from Arrow format' ,						):
                      written += file_obj.write(snake_case_		)

       return written







'''simple docstring'''


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = min(re.search('^ +'      ,	lowerCamelCase_  ).end() if t.startswith(' '  ) else 0 for t in string.splitlines()  )
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCamelCase_  )]  )


snake_case__	:     Dict      =    '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case__	:     Union[str, Any]      =    '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case__	:     int      =    '''\
# Begin of markdown

Some cool dataset card
'''






class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Tuple  = Path(snake_case_		) / 'README.md'
            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Optional[Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  List[str]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertEqual(snake_case_ ,						{}		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '		)
       with self.assertRaises(snake_case_		):
            DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  str  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)






