


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


snake_case__      			=    logging.get_logger(__name__)


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




						def 			_snake_case       (						self    : Optional[Any]    )   ->Tuple:






									"""simple docstring"""






									a__			:Optional[int]    	=     self.task_name.lower()






class        lowerCAmelCase_			(							SCREAMING_SNAKE_CASE_):
						lowerCamelCase_    =  "train"
						lowerCamelCase_    =  "dev"
						lowerCamelCase_    =  "test"






class        lowerCAmelCase_			(							SCREAMING_SNAKE_CASE_):
						lowerCamelCase_    =  42
						lowerCamelCase_    =  42
						lowerCamelCase_    =  42




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






									"""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_ ,  )
									a__			:Tuple    	=     args
									a__			:Optional[int]    	=     glue_processors[args.task_name]()
									a__			:Any    	=     glue_output_modes[args.task_name]
									if isinstance(lowercase_ ,  lowercase_    ):
												try:
															a__			:Dict    	=     Split[mode]
												except KeyError:
															raise KeyError("mode is not a valid split name"    )
        # Load data features from cache or dataset file
									a__			:int    	=     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}''' ,  )
									a__			:Any    	=     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)
												a__	,	a__			:Any    	=     label_list[2], label_list[1]
									a__			:Dict    	=     label_list

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									a__			:Union[str, Any]    	=     cached_features_file + ".lock"
									with FileLock(lowercase_    ):
												if os.path.exists(lowercase_    ) and not args.overwrite_cache:
															a__			:Optional[Any]    	=     time.time()
															a__			:Optional[Any]    	=     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:
																		a__			:str    	=     self.processor.get_dev_examples(args.data_dir    )
															elif mode == Split.test:
																		a__			:Optional[int]    	=     self.processor.get_test_examples(args.data_dir    )
															else:
																		a__			:Union[str, Any]    	=     self.processor.get_train_examples(args.data_dir    )
															if limit_length is not None:
																		a__			:Union[str, Any]    	=     examples[:limit_length]
															a__			:List[Any]    	=     glue_convert_examples_to_features(
															    lowercase_ ,  lowercase_ ,  max_length=args.max_seq_length ,  label_list=lowercase_ ,  output_mode=self.output_mode ,  )
															a__			:Optional[int]    	=     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    : Any    )   ->str:






									"""simple docstring"""






									return len(self.features    )




						def __getitem__(						self    : str ,  __A    : List[str]    )   ->InputFeatures:






									"""simple docstring"""






									return self.features[i]






						def 			_snake_case       (						self    : Union[str, Any]    )   ->Optional[int]:






									"""simple docstring"""






									return self.label_list




"""simple docstring"""

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_            =    '''scheduler_config.json'''
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						1
      __SCREAMING_SNAKE_CASE :				Dict				   =						2
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						3
      __SCREAMING_SNAKE_CASE :				Any				   =						4
      __SCREAMING_SNAKE_CASE :				Any				   =						5
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						6
      __SCREAMING_SNAKE_CASE :				str				   =						7
      __SCREAMING_SNAKE_CASE :				Any				   =						8
      __SCREAMING_SNAKE_CASE :				Tuple				   =						9
      __SCREAMING_SNAKE_CASE :				int				   =						1_0
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_1
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_2
      __SCREAMING_SNAKE_CASE :				Dict				   =						1_3
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						1_4

@dataclass
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				torch.FloatTensor

class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				str				   =						SCHEDULER_CONFIG_NAME
      __SCREAMING_SNAKE_CASE :				Dict				   =						[]
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						True




      @classmethod
      def      a_   (				cls ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_=False ,							**lowercase_ ,							)    ->       List[Any]:
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       cls.load_config(
              pretrained_model_name_or_path=lowercase_ ,							subfolder=lowercase_ ,							return_unused_kwargs=lowercase_ ,							return_commit_hash=lowercase_ ,							**lowercase_ ,							)
          return cls.from_config(lowercase_ ,							return_unused_kwargs=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ = False ,							**lowercase_				)    ->       str:
          self.save_config(save_directory=lowercase_ ,							push_to_hub=lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Union[str, Any]:
          return self._get_compatibles()




      @classmethod
      def      a_   (				cls				)    ->       Any:
          UpperCAmelCase          =       list(set([cls.__name__] + cls._compatibles				)				)
          UpperCAmelCase          =       importlib.import_module(__name__.split('.'				)[0]				)
          UpperCAmelCase          =       [
              getattr(lowercase_ ,							lowercase_				) for c in compatible_classes_str if hasattr(lowercase_ ,							lowercase_				)
          ]
          return compatible_classes



