



'''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


lowercase_									=    logging.get_logger(__name__)






@dataclass
class __A     :



 '''simple docstring'''







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



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


      """simple docstring"""



      _a           =    self.task_name.lower()






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			'train'
 __lowerCamelCase       :			Tuple		=			'dev'
 __lowerCamelCase       :			Tuple		=			'test'






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			GlueDataTrainingArguments
 __lowerCamelCase       :			str
 __lowerCamelCase       :			List[InputFeatures]



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

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


      """simple docstring"""



      return len(self.features			)



 def __getitem__(self					,  A			)       ->	InputFeatures:


      """simple docstring"""



      return self.features[i]



 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.label_list





from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


