

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


lowercase_									=   logging.get_logger(__name__)





@dataclass
class  __lowerCAmelCase     :
  _a									=  field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()							)}							)
  _a									=  field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}							)
  _a									=  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									=  field(
      default=UpperCamelCase__			,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.task_name.lower()





class  __lowerCAmelCase     (					UpperCamelCase__							):
  _a									=  """train"""
  _a									=  """dev"""
  _a									=  """test"""





class  __lowerCAmelCase     (					UpperCamelCase__							):
  _a									=  42
  _a									=  42
  _a									=  42




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = Split.train							,   lowerCAmelCase = None							,   )  -> Tuple:
        '''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'							,   lowercase_							,   )
        _lowercase	       =args
        _lowercase	       =glue_processors[args.task_name]()
        _lowercase	       =glue_output_modes[args.task_name]
        if isinstance(lowercase_							,   lowercase_					):
              try:
                    _lowercase	       =Split[mode]
              except KeyError:
                    raise KeyError('mode is not a valid split name'					)
        # Load data features from cache or dataset file
        _lowercase	       =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}'''							,   )
        _lowercase	       =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)
              _lowercase       ,       _lowercase	       =label_list[2], label_list[1]
        _lowercase	       =label_list

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        _lowercase	       =cached_features_file + '.lock'
        with FileLock(lowercase_					):
              if os.path.exists(lowercase_					) and not args.overwrite_cache:
                    _lowercase	       =time.time()
                    _lowercase	       =torch.load(lowercase_					)
                    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:
                          _lowercase	       =self.processor.get_dev_examples(args.data_dir					)
                    elif mode == Split.test:
                          _lowercase	       =self.processor.get_test_examples(args.data_dir					)
                    else:
                          _lowercase	       =self.processor.get_train_examples(args.data_dir					)
                    if limit_length is not None:
                          _lowercase	       =examples[:limit_length]
                    _lowercase	       =glue_convert_examples_to_features(
                        lowercase_							,   lowercase_							,   max_length=args.max_seq_length							,   label_list=lowercase_							,   output_mode=self.output_mode							,   )
                    _lowercase	       =time.time()
                    torch.save(self.features							,   lowercase_					)
                    # ^ 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					)  -> int:
        '''simple docstring'''






        return len(self.features					)




  def __getitem__(				self							,   lowerCAmelCase					)  -> InputFeatures:
        '''simple docstring'''






        return self.features[i]




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        return self.label_list









'''simple docstring'''

from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__lowerCAmelCase							:	Optional[Any]			=logging.get_logger(__name__)






class    UpperCAmelCase	:
 __lowercase   		=				42
 __lowercase   		=				None
 @staticmethod
 def      UpperCAmelCase_							(				)->							Dict:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							str:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :int							)->							Any:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[Any]:
     if not self.is_available():
         raise RuntimeError(
             F"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :int							)->							Any:
     return F"`pip install {cls.pip_package or cls.name}`"






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""optuna"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							int:
     return is_optuna_available()
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[Any]							)->							Tuple:
     return run_hp_search_optuna(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Optional[int]							)->							Optional[Any]:
     return default_hp_space_optuna(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""ray"""
 __lowercase   		=				"""'ray[tune]'"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							str:
     return is_ray_available()
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Dict						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[str]							)->							int:
     return run_hp_search_ray(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Dict							)->							int:
     return default_hp_space_ray(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sigopt"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							Union[str, Any]:
     return is_sigopt_available()
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							Dict:
     return run_hp_search_sigopt(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Optional[int]							)->							List[str]:
     return default_hp_space_sigopt(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""wandb"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							List[str]:
     return is_wandb_available()
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							List[str]:
     return run_hp_search_wandb(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :str							)->							Dict:
     return default_hp_space_wandb(lowercase_							)

__lowerCAmelCase							:	int			={
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}





def 						UpperCamelCase				(							):
    A__				=			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(_lowerCamelCase       ) > 0:
        A__				=			available_backends[0].name
        if len(_lowerCamelCase       ) > 1:
            logger.info(
                F"{len(_lowerCamelCase       )} hyperparameter search backends available. Using {name} as the default."       )
        return name
    raise RuntimeError(
        "No hyperparameter search backend available.\n"
        + "\n".join(
            F" - To install {backend.name} run {backend.pip_install()}"
            for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )



