



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	       =	logging.get_logger(__name__)



@dataclass
class     lowerCAmelCase_		:
       UpperCAmelCase__    : str       					=  field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()							)}							)
       UpperCAmelCase__    : str       					=  field(
           metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}							)
       UpperCAmelCase__    : 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."
               )
           }	,   )
       UpperCAmelCase__    : bool       					=  field(
           default=a__	,   metadata={"help": "Overwrite the cached training and evaluation sets"}							)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Union[str, Any]						     =     self.task_name.lower()






class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Tuple       					=  "train"
       UpperCAmelCase__    : Optional[Any]       					=  "dev"
       UpperCAmelCase__    : List[Any]       					=  "test"






class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : GlueDataTrainingArguments
       UpperCAmelCase__    : str
       UpperCAmelCase__    : List[InputFeatures]
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = Split.train,      SCREAMING_SNAKE_CASE_ = None,      )					->		Tuple:
          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_,      )
          UpperCamelCase   :  Tuple						     =     args
          UpperCamelCase   :  Tuple						     =     glue_processors[args.task_name]()
          UpperCamelCase   :  Any						     =     glue_output_modes[args.task_name]
          if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             try:
                UpperCamelCase   :  List[Any]						     =     Split[mode]
             except KeyError:
                raise KeyError('mode is not a valid split name'	)
        # Load data features from cache or dataset file
          UpperCamelCase   :  Union[str, Any]						     =     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   :  Dict						     =     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     ,					UpperCamelCase   :  Optional[Any]						     =     label_list[2], label_list[1]
          UpperCamelCase   :  Optional[int]						     =     label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          UpperCamelCase   :  Dict						     =     cached_features_file + '.lock'
          with FileLock(SCREAMING_SNAKE_CASE_	):
             if os.path.exists(SCREAMING_SNAKE_CASE_	) and not args.overwrite_cache:
                UpperCamelCase   :  Tuple						     =     time.time()
                UpperCamelCase   :  Optional[int]						     =     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:
                   UpperCamelCase   :  Optional[Any]						     =     self.processor.get_dev_examples(args.data_dir	)
                elif mode == Split.test:
                   UpperCamelCase   :  str						     =     self.processor.get_test_examples(args.data_dir	)
                else:
                   UpperCamelCase   :  Any						     =     self.processor.get_train_examples(args.data_dir	)
                if limit_length is not None:
                   UpperCamelCase   :  Union[str, Any]						     =     examples[:limit_length]
                UpperCamelCase   :  List[Any]						     =     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,      )
                UpperCamelCase   :  List[Any]						     =     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	)					->		Dict:
          return len(self.features	)
       def __getitem__(      self,      SCREAMING_SNAKE_CASE_	)					->		InputFeatures:
          return self.features[i]






       def  snake_case_	(      self	)					->		Optional[Any]:
          return self.label_list








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  "data2vec-text"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_0522,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_="absolute",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     vocab_size
          UpperCamelCase   :  Tuple						     =     hidden_size
          UpperCamelCase   :  int						     =     num_hidden_layers
          UpperCamelCase   :  Dict						     =     num_attention_heads
          UpperCamelCase   :  str						     =     hidden_act
          UpperCamelCase   :  List[str]						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  List[str]						     =     type_vocab_size
          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  List[str]						     =     layer_norm_eps
          UpperCamelCase   :  List[str]						     =     position_embedding_type
          UpperCamelCase   :  Any						     =     use_cache
          UpperCamelCase   :  Any						     =     classifier_dropout






class     lowerCAmelCase_		(  a__							):
       @property
       def  snake_case_	(      self	)					->		Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             UpperCamelCase   :  List[Any]						     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
             UpperCamelCase   :  int						     =     {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]	)



