




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	:		List[str]							       =   logging.get_logger(__name__)


@dataclass
class 				UpperCamelCase_    :

    '''simple docstring'''






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


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->str:





           '''simple docstring'''

           A__       =    self.task_name.lower()


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''train'''
    UpperCAmelCase__							  =      '''dev'''
    UpperCAmelCase__							  =      '''test'''


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      42
    UpperCAmelCase__							  =      42
    UpperCAmelCase__							  =      42


    def __init__(			self      :	int					, UpperCAmelCase__      :	GlueDataTrainingArguments					, UpperCAmelCase__      :	PreTrainedTokenizerBase					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Union[str, Split] = Split.train					, UpperCAmelCase__      :	Optional[str] = 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'''					, UpperCAmelCase__					, )
           A__       =    args
           A__       =    glue_processors[args.task_name]()
           A__       =    glue_output_modes[args.task_name]
           if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                  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(UpperCAmelCase__):
                  if os.path.exists(UpperCAmelCase__) and not args.overwrite_cache:
                         A__       =    time.time()
                         A__       =    torch.load(UpperCAmelCase__)
                         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(
                             UpperCAmelCase__					, UpperCAmelCase__					, max_length=args.max_seq_length					, label_list=UpperCAmelCase__					, output_mode=self.output_mode					, )
                         A__       =    time.time()
                         torch.save(self.features					, UpperCAmelCase__)
                         # ^ 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      :	Optional[Any])						->Dict:





           '''simple docstring'''

           return len(self.features)


    def __getitem__(			self      :	int					, UpperCAmelCase__      :	int)						->InputFeatures:





           '''simple docstring'''

           return self.features[i]





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





           '''simple docstring'''

           return self.label_list






'''simple docstring'''
import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
  )
  from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Optional[Any]						,						_A				:    Optional[Any]						,						_A				:    List[str]=13						,						_A				:    Any=7						,						_A				:    str=True						,						_A				:    Any=True						,						_A				:    Any=True						,						_A				:    Optional[int]=True						,						_A				:    int=99						,						_A				:    Optional[int]=32						,						_A				:    List[Any]=5						,						_A				:    Optional[Any]=4						,						_A				:    Dict=37						,						_A				:    Any="gelu"						,						_A				:    str=0.1						,						_A				:    int=0.1						,						_A				:    Optional[Any]=512						,						_A				:    Optional[Any]=16						,						_A				:    List[Any]=2						,						_A				:    str=0.0_2						,						_A				:    Optional[Any]=False						,						_A				:    Any=True						,						_A				:    Dict="None"						,						_A				:    List[str]=3						,						_A				:    List[str]=4						,						_A				:    Tuple=None						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				parent
          UpperCAmelCase__			:		Union[str, Any]													=				batch_size
          UpperCAmelCase__			:		Dict													=				seq_length
          UpperCAmelCase__			:		Dict													=				is_training
          UpperCAmelCase__			:		Optional[Any]													=				use_input_mask
          UpperCAmelCase__			:		Optional[Any]													=				use_token_type_ids
          UpperCAmelCase__			:		Union[str, Any]													=				use_labels
          UpperCAmelCase__			:		Tuple													=				vocab_size
          UpperCAmelCase__			:		Tuple													=				hidden_size
          UpperCAmelCase__			:		Any													=				num_hidden_layers
          UpperCAmelCase__			:		Tuple													=				num_attention_heads
          UpperCAmelCase__			:		List[Any]													=				intermediate_size
          UpperCAmelCase__			:		Union[str, Any]													=				hidden_act
          UpperCAmelCase__			:		Any													=				hidden_dropout_prob
          UpperCAmelCase__			:		Any													=				attention_probs_dropout_prob
          UpperCAmelCase__			:		int													=				max_position_embeddings
          UpperCAmelCase__			:		Optional[int]													=				type_vocab_size
          UpperCAmelCase__			:		Union[str, Any]													=				type_sequence_label_size
          UpperCAmelCase__			:		int													=				initializer_range
          UpperCAmelCase__			:		Any													=				num_labels
          UpperCAmelCase__			:		Optional[Any]													=				num_choices
          UpperCAmelCase__			:		List[Any]													=				relative_attention
          UpperCAmelCase__			:		int													=				position_biased_input
          UpperCAmelCase__			:		str													=				pos_att_type
          UpperCAmelCase__			:		Union[str, Any]													=				scope






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size )

          UpperCAmelCase__			:		Any													=				None
          if self.use_input_mask:
              UpperCAmelCase__			:		Tuple													=				ids_tensor([self.batch_size, self.seq_length]						,						vocab_size=2 )

          UpperCAmelCase__			:		Optional[Any]													=				None
          if self.use_token_type_ids:
              UpperCAmelCase__			:		List[str]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size )

          UpperCAmelCase__			:		Dict													=				None
          UpperCAmelCase__			:		Any													=				None
          UpperCAmelCase__			:		Dict													=				None
          if self.use_labels:
              UpperCAmelCase__			:		int													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )
              UpperCAmelCase__			:		List[str]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels )
              UpperCAmelCase__			:		Dict													=				ids_tensor([self.batch_size]						,						self.num_choices )

          UpperCAmelCase__			:		Optional[Any]													=				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          return DebertaVaConfig(
              vocab_size=self.vocab_size						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						max_position_embeddings=self.max_position_embeddings						,						type_vocab_size=self.type_vocab_size						,						initializer_range=self.initializer_range						,						relative_attention=self.relative_attention						,						position_biased_input=self.position_biased_input						,						pos_att_type=self.pos_att_type						,						)






      def lowercase_							(					self				:    Dict						,						_A				:    Optional[int] ):


          '''simple docstring'''


          self.parent.assertListEqual(list(result.loss.size() )						,						[] )






      def lowercase_							(					self				:    int						,						_A				:    int						,						_A				:    Any						,						_A				:    Tuple						,						_A				:    List[Any]						,						_A				:    str						,						_A				:    Union[str, Any]						,						_A				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaModel(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		str													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A )[0]
          UpperCAmelCase__			:		List[str]													=				model(_A						,						token_type_ids=_A )[0]
          UpperCAmelCase__			:		List[Any]													=				model(_A )[0]

          self.parent.assertListEqual(list(sequence_output.size() )						,						[self.batch_size, self.seq_length, self.hidden_size] )






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    Tuple						,						_A				:    List[Any]						,						_A				:    Optional[Any]						,						_A				:    int						,						_A				:    List[Any]						,						_A				:    Optional[int]						,						_A				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				DebertaVaForMaskedLM(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Any													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )

          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size) )






      def lowercase_							(					self				:    str						,						_A				:    str						,						_A				:    Any						,						_A				:    Any						,						_A				:    List[Any]						,						_A				:    Dict						,						_A				:    Tuple						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.num_labels
          UpperCAmelCase__			:		Union[str, Any]													=				DebertaVaForSequenceClassification(_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		str													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )
          self.parent.assertListEqual(list(result.logits.size() )						,						[self.batch_size, self.num_labels] )
          self.check_loss_output(_A )






      def lowercase_							(					self				:    Any						,						_A				:    List[str]						,						_A				:    List[str]						,						_A				:    Optional[int]						,						_A				:    Tuple						,						_A				:    Dict						,						_A				:    List[str]						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.num_labels
          UpperCAmelCase__			:		int													=				DebertaVaForTokenClassification(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		List[Any]													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels) )






      def lowercase_							(					self				:    str						,						_A				:    List[str]						,						_A				:    str						,						_A				:    Optional[int]						,						_A				:    Optional[int]						,						_A				:    Union[str, Any]						,						_A				:    Dict						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				DebertaVaForQuestionAnswering(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		int													=				model(
              _A						,						attention_mask=_A						,						token_type_ids=_A						,						start_positions=_A						,						end_positions=_A						,						)
          self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length) )






      def lowercase_							(					self				:    Any						,						_A				:    Tuple						,						_A				:    Optional[int]						,						_A				:    Optional[int]						,						_A				:    str						,						_A				:    List[str]						,						_A				:    Any						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaForMultipleChoice(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				input_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		str													=				token_type_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		Dict													=				input_mask.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		List[str]													=				model(
              _A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A						,						)
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices) )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						
          )			:		Any													=				config_and_inputs
          UpperCAmelCase__			:		Optional[int]													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_torch
class 			lowerCamelCase_   (	__a     ,       __a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     (
          (
              DebertaVaModel,
              DebertaVaForMaskedLM,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaForQuestionAnswering,
              DebertaVaForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__		     =     (
          {
              'feature-extraction': DebertaVaModel,
              'fill-mask': DebertaVaForMaskedLM,
              'question-answering': DebertaVaForQuestionAnswering,
              'text-classification': DebertaVaForSequenceClassification,
              'token-classification': DebertaVaForTokenClassification,
              'zero-shot': DebertaVaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__		     =     True
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaModelTester(self )
          UpperCAmelCase__			:		Tuple													=				ConfigTester(self						,						config_class=_A						,						hidden_size=37 )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*_A )






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*_A )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*_A )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*_A )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_multiple_choice(*_A )






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:		List[str]													=				DebertaVaModel.from_pretrained(_A )
              self.assertIsNotNone(_A )




@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_   (	unittest.TestCase      ):






      @unittest.skip(reason='''Model not available yet''' )
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          pass






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge''' )

          UpperCAmelCase__			:		List[Any]													=				torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
          UpperCAmelCase__			:		List[str]													=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          with torch.no_grad():
              UpperCAmelCase__			:		List[str]													=				model(_A						,						attention_mask=_A )[0]
          # compare the actual values for a slice.
          UpperCAmelCase__			:		str													=				torch.tensor(
              [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,						_A						,						atol=1e-4 )						,						f"""{output[:, 1:4, 1:4]}""" )
