





'''simple docstring'''


import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


__lowercase							:				List[Any]					=       datasets.utils.logging.get_logger(__name__)

@dataclass
class 		__lowercase			(					datasets.BuilderConfig      ):
			lowerCamelCase     :						Optional[datasets.Features]		 =     None
			lowerCamelCase     :						str		 =     "utf-8"
			lowerCamelCase     :						Optional[str]		 =     None
			lowerCamelCase     :						int		 =     10 << 20  # 10MB
			lowerCamelCase     :						bool		 =     False
			lowerCamelCase     :						str		 =     "line"

class 		__lowercase			(					datasets.ArrowBasedBuilder      ):
			lowerCamelCase     :						List[Any]		 =     TextConfig

			def     UpperCAmelCase__	(self ):
								return datasets.DatasetInfo(features=self.config.features )

			def     UpperCAmelCase__	(self     ,				A ):
								if not self.config.data_files:
													raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
								lowerCamelCase_   : Optional[int]       =						dl_manager.download_and_extract(self.config.data_files )
								if isinstance(A     ,				(str, list, tuple) ):
													lowerCamelCase_   : Optional[Any]       =						data_files
													if isinstance(A     ,				A ):
																		lowerCamelCase_   : Dict       =						[files]
													lowerCamelCase_   : str       =						[dl_manager.iter_files(A ) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,				gen_kwargs={'''files''': files} )]
								lowerCamelCase_   : Optional[int]       =						[]
								for split_name, files in data_files.items():
													if isinstance(A     ,				A ):
																		lowerCamelCase_   : Union[str, Any]       =						[files]
													lowerCamelCase_   : Optional[Any]       =						[dl_manager.iter_files(A ) for file in files]
													splits.append(datasets.SplitGenerator(name=A     ,				gen_kwargs={'''files''': files} ) )
								return splits

			def     UpperCAmelCase__	(self     ,				A ):
								if self.config.features is not None:
													lowerCamelCase_   : Any       =						self.config.features.arrow_schema
													if all(not require_storage_cast(A ) for feature in self.config.features.values() ):
																		# cheaper cast
																		lowerCamelCase_   : List[str]       =						pa_table.cast(A )
													else:
																		# more expensive cast; allows str <-> int/float or str to Audio for example
																		lowerCamelCase_   : Union[str, Any]       =						table_cast(A     ,				A )
													return pa_table
								else:
													return pa_table.cast(pa.schema({'''text''': pa.string()} ) )






			def     UpperCAmelCase__	(self     ,				A ):
								lowerCamelCase_   : Optional[Any]       =						list(self.config.features ) if self.config.features is not None else ['''text''']
								for file_idx, file in enumerate(itertools.chain.from_iterable(A ) ):
													# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
													with open(A     ,				encoding=self.config.encoding     ,				errors=self.config.errors ) as f:
																		if self.config.sample_by == "line":
																							lowerCamelCase_   : Optional[int]       =						0
																							while True:
																												lowerCamelCase_   : Dict       =						f.read(self.config.chunksize )
																												if not batch:
																																	break
																												batch += f.readline()  # finish current line
																												# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																												lowerCamelCase_   : List[Any]       =						StringIO(A ).readlines()
																												if not self.config.keep_linebreaks:
																																	lowerCamelCase_   : Any       =						[line.rstrip('''\n''' ) for line in batch]
																												lowerCamelCase_   : Dict       =						pa.Table.from_arrays([pa.array(A )]     ,				names=A )
																												# Uncomment for debugging (will print the Arrow table size and elements)
																												# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																												# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																												yield (file_idx, batch_idx), self._cast_table(A )
																												batch_idx += 1
																		elif self.config.sample_by == "paragraph":
																							lowerCamelCase_   : Dict       =						0
																							lowerCamelCase_   : Dict       =						''''''
																							while True:
																												lowerCamelCase_   : Union[str, Any]       =						f.read(self.config.chunksize )
																												if not new_batch:
																																	break
																												batch += new_batch
																												batch += f.readline()  # finish current line
																												lowerCamelCase_   : int       =						batch.split('''\n\n''' )
																												lowerCamelCase_   : Dict       =						pa.Table.from_arrays(
																												    [pa.array([example for example in batch[:-1] if example] )]     ,				names=A )
																												# Uncomment for debugging (will print the Arrow table size and elements)
																												# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																												# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																												yield (file_idx, batch_idx), self._cast_table(A )
																												batch_idx += 1
																												lowerCamelCase_   : Optional[Any]       =						batch[-1]
																							if batch:
																												lowerCamelCase_   : Any       =						pa.Table.from_arrays([pa.array([batch] )]     ,				names=A )
																												yield (file_idx, batch_idx), self._cast_table(A )
																		elif self.config.sample_by == "document":
																							lowerCamelCase_   : Dict       =						f.read()
																							lowerCamelCase_   : Optional[Any]       =						pa.Table.from_arrays([pa.array([text] )]     ,				names=A )
																							yield file_idx, self._cast_table(A )




'''simple docstring'''


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)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

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






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						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(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


