


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 A  (       UpperCamelCase_					):






      def __init__( self						:					Dict      ,       lowercase_						:					NestedDataStructureLike[PathLike]      ,       lowercase_						:					Optional[NamedSplit] = None      ,       lowercase_						:					Optional[Features] = None      ,       lowercase_						:					str = None      ,       lowercase_						:					bool = False      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[int] = None      ,       **lowercase_						:					List[Any]      ,       ) ->						List[Any]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[Any]:
             """simple docstring"""




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

                    self.builder.download_and_prepare(
                        download_config=lowercase_      ,       download_mode=lowercase_      ,       verification_mode=lowercase_      ,       base_path=lowercase_      ,       num_proc=self.num_proc      ,       )
                    _lowerCamelCase	:		Tuple          =self.builder.as_dataset(
                        split=self.split      ,       verification_mode=lowercase_      ,       in_memory=self.keep_in_memory			)
             return dataset




class A  :






      def __init__( self						:					List[Any]      ,       lowercase_						:					Dataset      ,       lowercase_						:					Union[PathLike, BinaryIO]      ,       lowercase_						:					Optional[int] = None      ,       lowercase_						:					Optional[int] = None      ,       **lowercase_						:					List[str]      ,       ) ->						Tuple:
             """simple docstring"""




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

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






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =self.to_csv_kwargs.pop('path_or_buf'      ,       lowercase_			)
             _lowerCamelCase	:		Optional[int]          =self.to_csv_kwargs.pop('header'      ,       lowercase_			)
             _lowerCamelCase	:		List[str]          =self.to_csv_kwargs.pop('index'      ,       lowercase_			)

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






      def 	lowerCamelCase	( self						:					str      ,       lowercase_						:					Dict			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =args

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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					BinaryIO      ,       lowercase_						:					str      ,       lowercase_						:					List[str]      ,       **lowercase_						:					Optional[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		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'      ,       ):
                           _lowerCamelCase	:		Tuple          =self._batch_csv((offset, header, index, to_csv_kwargs)			)
                           written += file_obj.write(lowercase_			)

             else:
                    _lowerCamelCase	:		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      ,       lowercase_      ,       lowercase_			)]      ,       )      ,       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(lowercase_			)

             return written






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase						 =	logging.getLogger()




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Any          =argparse.ArgumentParser()
       parser.add_argument('-f'					)
       _lowerCamelCase	:		Optional[Any]          =parser.parse_args()
       return args.f




class A  (       UpperCamelCase_					):






      def 	lowerCamelCase	( self						:					List[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Any          =logging.StreamHandler(sys.stdout			)
             logger.addHandler(lowercase_			)

             _lowerCamelCase	:		Dict          =self.get_auto_remove_tmp_dir()
             _lowerCamelCase	:		int          =F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

             with patch.object(lowercase_      ,       'argv'      ,       lowercase_			):
                    _lowerCamelCase	:		Dict          =run_glue_with_pabee.main()
                    for value in result.values():
                           self.assertGreaterEqual(lowercase_      ,       0.75			)


