




'''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


UpperCamelCase_						:      Optional[int]       =						logging.get_logger(__name__)

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   str       =						field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()							)}							)
     SCREAMING_SNAKE_CASE_							:   str       =						field(
         metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}							)
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=1_28 , metadata={
             """help""": (
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         } , )
     SCREAMING_SNAKE_CASE_							:   bool       =						field(
         default=__lowerCAmelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.task_name.lower()







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						"""train"""
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""dev"""
     SCREAMING_SNAKE_CASE_							:   str       =						"""test"""







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   GlueDataTrainingArguments
     SCREAMING_SNAKE_CASE_							:   str
     SCREAMING_SNAKE_CASE_							:   List[InputFeatures]






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

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






     def __getitem__(			self ,_SCREAMING_SNAKE_CASE   )							->      InputFeatures:
          return self.features[i]






     def     _lowercase     (			self   )							->      Tuple:
          return self.label_list








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


