"""simple docstring"""
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase__   			=      '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'




class   __snake_case			(	unittest.TestCase			, _UpperCAmelCase							):



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	List[Any]     				=      load_tool('summarization')
        self.tool.setup()
        a__:	Union[str, Any]     				=      load_tool('summarization'       ,     remote=UpperCAmelCase_)



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	List[str]     				=      self.tool(UpperCAmelCase_)
        self.assertEqual(
            UpperCAmelCase_       ,     'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'       ,     )



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





        '''simple docstring'''





        a__:	Tuple     				=      self.remote_tool(UpperCAmelCase_)
        self.assertEqual(
            UpperCAmelCase_       ,     'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'       ,     )



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	List[Any]     				=      self.tool(text=UpperCAmelCase_)
        self.assertEqual(
            UpperCAmelCase_       ,     'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'       ,     )



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Dict     				=      self.remote_tool(text=UpperCAmelCase_)
        self.assertEqual(
            UpperCAmelCase_       ,     'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'       ,     )










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  __UpperCAmelCase						(_UpperCAmelCase       ):







			def __init__(      self:    Optional[int] ,      UpperCAmelCase_:    NestedDataStructureLike[PathLike] ,      UpperCAmelCase_:    Optional[NamedSplit] = None ,      UpperCAmelCase_:    Optional[Features] = None ,      UpperCAmelCase_:    str = None ,      UpperCAmelCase_:    bool = False ,      UpperCAmelCase_:    bool = False ,      UpperCAmelCase_:    Optional[int] = None ,      **UpperCAmelCase_:    Union[str, Any] ,      ):







									'''simple docstring'''




									super().__init__(
									    UpperCAmelCase_ ,      split=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      cache_dir=UpperCAmelCase_ ,      keep_in_memory=UpperCAmelCase_ ,      streaming=UpperCAmelCase_ ,      num_proc=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )
									_SCREAMING_SNAKE_CASE       =    path_or_paths if isinstance(UpperCAmelCase_ ,      UpperCAmelCase_	) else {self.split: path_or_paths}
									_SCREAMING_SNAKE_CASE       =    Csv(
									    cache_dir=UpperCAmelCase_ ,      data_files=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




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

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



class  __UpperCAmelCase						:







			def __init__(      self:    List[str] ,      UpperCAmelCase_:    Dataset ,      UpperCAmelCase_:    Union[PathLike, BinaryIO] ,      UpperCAmelCase_:    Optional[int] = None ,      UpperCAmelCase_:    Optional[int] = None ,      **UpperCAmelCase_:    Any ,      ):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    dataset
									_SCREAMING_SNAKE_CASE       =    path_or_buf
									_SCREAMING_SNAKE_CASE       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_SCREAMING_SNAKE_CASE       =    num_proc
									_SCREAMING_SNAKE_CASE       =    """utf-8"""
									_SCREAMING_SNAKE_CASE       =    to_csv_kwargs







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""path_or_buf""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""header""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""index""" ,      UpperCAmelCase_	)

									if isinstance(self.path_or_buf ,      (str, bytes, os.PathLike)	):
															with open(self.path_or_buf ,      """wb+"""	) as buffer:
																					_SCREAMING_SNAKE_CASE       =    self._write(file_obj=UpperCAmelCase_ ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									else:
															_SCREAMING_SNAKE_CASE       =    self._write(file_obj=self.path_or_buf ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									return written







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    args

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







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    BinaryIO ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Any ,      **UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_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 CSV from Arrow format""" ,      ):
																					_SCREAMING_SNAKE_CASE       =    self._batch_csv((offset, header, index, to_csv_kwargs)	)
																					written += file_obj.write(UpperCAmelCase_	)

									else:
															_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    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 ,      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 CSV from Arrow format""" ,      ):
																											written += file_obj.write(UpperCAmelCase_	)

									return written

