


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


lowerCAmelCase__    :Union[str, Any]       =				logging.get_logger(__name__)

@dataclass
class 						__a :
		_a      :    str           =      field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()     )}     )
		_a      :    str           =      field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}     )
		_a      :    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.'
		        )
		    }       , )
		_a      :    bool           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Overwrite the cached training and evaluation sets'}     )
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.task_name.lower()



class 						__a (							UpperCAmelCase     ):
		_a      :    Dict           =      'train'
		_a      :    Optional[Any]           =      'dev'
		_a      :    Any           =      'test'



class 						__a (							UpperCAmelCase     ):
		_a      :    GlueDataTrainingArguments
		_a      :    str
		_a      :    List[InputFeatures]
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = Split.train     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Dict:




									"""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'     ,       _SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     args
									_UpperCAmelCase											=     glue_processors[args.task_name]()
									_UpperCAmelCase											=     glue_output_modes[args.task_name]
									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																try:
																							_UpperCAmelCase											=     Split[mode]
																except KeyError:
																							raise KeyError('mode is not a valid split name'		)
        # Load data features from cache or dataset file
									_UpperCAmelCase											=     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											=     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											=     label_list[2], label_list[1]
									_UpperCAmelCase											=     label_list

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									_UpperCAmelCase											=     cached_features_file + '.lock'
									with FileLock(_SCREAMING_SNAKE_CASE		):
																if os.path.exists(_SCREAMING_SNAKE_CASE		) and not args.overwrite_cache:
																							_UpperCAmelCase											=     time.time()
																							_UpperCAmelCase											=     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											=     self.processor.get_dev_examples(args.data_dir		)
																							elif mode == Split.test:
																														_UpperCAmelCase											=     self.processor.get_test_examples(args.data_dir		)
																							else:
																														_UpperCAmelCase											=     self.processor.get_train_examples(args.data_dir		)
																							if limit_length is not None:
																														_UpperCAmelCase											=     examples[:limit_length]
																							_UpperCAmelCase											=     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											=     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		)					->			int:




									"""simple docstring"""





									return len(self.features		)
		def __getitem__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			InputFeatures:




									"""simple docstring"""





									return self.features[i]






		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									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


lowerCAmelCase__    :Any       =				logging.get_logger(__name__)

lowerCAmelCase__    :Union[str, Any]       =				{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      'xmod'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=30522     ,       _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-1_2     ,       _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=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=("en_XX",)     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     type_vocab_size
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     position_embedding_type
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     classifier_dropout
									_UpperCAmelCase											=     pre_norm
									_UpperCAmelCase											=     adapter_reduction_factor
									_UpperCAmelCase											=     adapter_layer_norm
									_UpperCAmelCase											=     adapter_reuse_layer_norm
									_UpperCAmelCase											=     ln_before_adapter
									_UpperCAmelCase											=     list(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     default_language



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
