

'''simple docstring'''


import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCamelCase_				: Tuple    	=							logging.get_logger(__name__)
@dataclass
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			__a							:							str					     =					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()	)}	)
			__a							:							str					     =					field(
			    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}	)
			__a							:							int					     =					field(
			    default=128       ,metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }       ,)
			__a							:							bool					     =					field(
			    default=UpperCAmelCase__       ,metadata={"help": "Overwrite the cached training and evaluation sets"}	)






			def 				A      (   self							:						List[str]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.task_name.lower()
class    _SCREAMING_SNAKE_CASE							(		UpperCAmelCase__	):


			'''simple docstring'''





			__a							:							Union[str, Any]					     =					"train"
			__a							:							Any					     =					"dev"
			__a							:							Union[str, Any]					     =					"test"
class    _SCREAMING_SNAKE_CASE							(		UpperCAmelCase__	):


			'''simple docstring'''





			__a							:							GlueDataTrainingArguments
			__a							:							str
			__a							:							List[InputFeatures]






			def __init__(   self							:						List[str] ,      lowercase							:						GlueDataTrainingArguments ,      lowercase							:						PreTrainedTokenizerBase ,      lowercase							:						Optional[int] = None ,      lowercase							:						Union[str, Split] = Split.train ,      lowercase							:						Optional[str] = None ,      )							->    List[Any]:

									'''simple docstring'''







									warnings.warn(
									    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
									    """library. You can have a look at this example script for pointers: """
									    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" ,      lowerCamelCase__ ,      )
									UpperCamelCase__         = args
									UpperCamelCase__         = glue_processors[args.task_name]()
									UpperCamelCase__         = glue_output_modes[args.task_name]
									if isinstance(lowerCamelCase__ ,      lowerCamelCase__			):
															try:
																					UpperCamelCase__         = Split[mode]
															except KeyError:
																					raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
									UpperCamelCase__         = os.path.join(
									    cache_dir if cache_dir is not None else args.data_dir ,      f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}" ,      )
									UpperCamelCase__         = self.processor.get_labels()
									if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
									    "RobertaTokenizer",
									    "RobertaTokenizerFast",
									    "XLMRobertaTokenizer",
									    "BartTokenizer",
									    "BartTokenizerFast",
									):
															# HACK(label indices are swapped in RoBERTa pretrained model)
															UpperCamelCase__         = label_list[2], label_list[1]
									UpperCamelCase__         = label_list

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									UpperCamelCase__         = cached_features_file + ".lock"
									with FileLock(lowerCamelCase__			):
															if os.path.exists(lowerCamelCase__			) and not args.overwrite_cache:
																					UpperCamelCase__         = time.time()
																					UpperCamelCase__         = torch.load(lowerCamelCase__			)
																					logger.info(
																					    f"Loading features from cached file {cached_features_file} [took %.3f s]" ,      time.time() - start			)
															else:
																					logger.info(f"Creating features from dataset file at {args.data_dir}"			)

																					if mode == Split.dev:
																											UpperCamelCase__         = self.processor.get_dev_examples(args.data_dir			)
																					elif mode == Split.test:
																											UpperCamelCase__         = self.processor.get_test_examples(args.data_dir			)
																					else:
																											UpperCamelCase__         = self.processor.get_train_examples(args.data_dir			)
																					if limit_length is not None:
																											UpperCamelCase__         = examples[:limit_length]
																					UpperCamelCase__         = glue_convert_examples_to_features(
																					    lowerCamelCase__ ,      lowerCamelCase__ ,      max_length=args.max_seq_length ,      label_list=lowerCamelCase__ ,      output_mode=self.output_mode ,      )
																					UpperCamelCase__         = time.time()
																					torch.save(self.features ,      lowerCamelCase__			)
																					# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																					logger.info(
																					    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"			)






			def __len__(   self							:						Optional[Any]			)							->    Optional[int]:

									'''simple docstring'''







									return len(self.features			)






			def __getitem__(   self							:						List[str] ,      lowercase							:						Optional[int]			)							->    InputFeatures:

									'''simple docstring'''







									return self.features[i]







			def 				A      (   self							:						List[str]			)							->    Tuple:

									'''simple docstring'''







									return self.label_list








'''simple docstring'''


import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase_				: Any    	=							{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCamelCase_				: Optional[int]    	=							logging.WARNING







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = os.getenv("""DATASETS_VERBOSITY"""						,      _A					)
						if env_level_str:
												if env_level_str in log_levels:
																		return log_levels[env_level_str]
												else:
																		logging.getLogger().warning(
																		    f"Unknown option DATASETS_VERBOSITY={env_level_str}, "
																		    f"has to be one of: { ', '.join(log_levels.keys()					) }"					)
						return _default_log_level







def 	__magic_name__( ):
						'''simple docstring'''





						return __name__.split("""."""					)[0]







def 	__magic_name__( ):
						'''simple docstring'''





						return logging.getLogger(_get_library_name()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(_get_default_logging_level()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(logging.NOTSET					)







def 	__magic_name__( _A = None					):
						'''simple docstring'''





						if name is None:
												UpperCamelCase__         = _get_library_name()
						return logging.getLogger(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return _get_library_root_logger().getEffectiveLevel()







def 	__magic_name__( _A					):
						'''simple docstring'''





						_get_library_root_logger().setLevel(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = False







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						int ,      *lowercase							:						Any ,      **lowercase							:						int			)							->    List[Any]:  # pylint: disable=unused-argument

									'''simple docstring'''







									UpperCamelCase__         = args[0] if args else None






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

									'''simple docstring'''







									return iter(self._iterator			)






			def __getattr__(   self							:						Optional[int] ,      lowercase							:						Tuple			)							->    int:

									'''simple docstring'''







									def empty_fn(*lowercase							:						List[Any] ,      **lowercase							:						Dict			):  # pylint: disable=unused-argument
															return

									return empty_fn






			def __enter__(   self							:						Dict			)							->    Tuple:

									'''simple docstring'''







									return self







			def __exit__(   self							:						Tuple ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						int			)							->    Any:

									'''simple docstring'''







									return
lowerCamelCase_				: str    	=							True
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __call__(   self							:						List[Any] ,      *lowercase							:						str ,      lowercase							:						Optional[int]=False ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									if _tqdm_active and not disable:
															return tqdm_lib.tqdm(*lowercase ,      **lowercase			)
									else:
															return EmptyTqdm(*lowercase ,      **lowercase			)






			def 				A      (   self							:						Optional[Any] ,      *lowercase							:						Tuple ,      **lowercase							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = None
									if _tqdm_active:
															return tqdm_lib.tqdm.set_lock(*lowercase ,      **lowercase			)







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

									'''simple docstring'''







									if _tqdm_active:
															return tqdm_lib.tqdm.get_lock()
lowerCamelCase_				: int    	=							_tqdm_cls()







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						return bool(_tqdm_active					)







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = True







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = False
