


'''simple docstring'''






import unittest

from transformers import DebertaConfig, 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 (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=16 ,			__magic_name__=2 ,			__magic_name__=0.0_2 ,			__magic_name__=False ,			__magic_name__=True ,			__magic_name__="None" ,			__magic_name__=3 ,			__magic_name__=4 ,			__magic_name__=None ,			)   ->							Any:
      _a      =							parent
      _a      =							batch_size
      _a      =							seq_length
      _a      =							is_training
      _a      =							use_input_mask
      _a      =							use_token_type_ids
      _a      =							use_labels
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							type_vocab_size
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							num_labels
      _a      =							num_choices
      _a      =							relative_attention
      _a      =							position_biased_input
      _a      =							pos_att_type
      _a      =							scope
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

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

      _a      =							None
      if self.use_token_type_ids:
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size  )

      _a      =							None
      _a      =							None
      _a      =							None
      if self.use_labels:
           _a      =							ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels  )
           _a      =							ids_tensor([self.batch_size] ,			self.num_choices  )

      _a      =							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return DebertaConfig(
          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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.get_config()
      _a      =							3_00
      return config
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      self.parent.assertListEqual(list(result.loss.size()  ) ,			[]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__  )[0]

      self.parent.assertListEqual(list(sequence_output.size()  ) ,			[self.batch_size, self.seq_length, self.hidden_size]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaForMaskedLM(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )

      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							self.num_labels
      _a      =							DebertaForSequenceClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertListEqual(list(result.logits.size()  ) ,			[self.batch_size, self.num_labels]  )
      self.check_loss_output(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							self.num_labels
      _a      =							DebertaForTokenClassification(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							DebertaForQuestionAnswering(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(
          __magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			start_positions=__magic_name__ ,			end_positions=__magic_name__ ,			)
      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 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.prepare_config_and_inputs()
      (
          (
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				
      )      =							config_and_inputs
      _a      =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(
     {
         """feature-extraction""": DebertaModel,
         """fill-mask""": DebertaForMaskedLM,
         """question-answering""": DebertaForQuestionAnswering,
         """text-classification""": DebertaForSequenceClassification,
         """token-classification""": DebertaForTokenClassification,
         """zero-shot""": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							DebertaModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							DebertaModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )





@require_torch
@require_sentencepiece
@require_tokenizers
class 	a       (       unittest.TestCase    ):
 @unittest.skip(reason='Model not available yet'  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      pass




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							DebertaModel.from_pretrained('microsoft/deberta-base'  )

      _a      =							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
      _a      =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      with torch.no_grad():
           _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )[0]
      # compare the actual values for a slice.
      _a      =							torch.tensor(
          [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]  )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,			__magic_name__ ,			atol=1e-4  ) ,			f'{output[:, 1:4, 1:4]}'  )





'''simple docstring'''






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


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

a_   :      List[Any]				    =      list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a_   :      List[str]				    =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 	a       :
 _lowerCAmelCase	   =		field(
     default=_SCREAMING_SNAKE_CASE					,			metadata={"""help""": """Model type selected in the list: """ + """, """.join(_SCREAMING_SNAKE_CASE    )}    )
 _lowerCAmelCase	   =		field(
     default=_SCREAMING_SNAKE_CASE					,			metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""}    )
 _lowerCAmelCase	   =		field(
     default=1_2_8					,			metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=1_2_8					,			metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""}					,			)
 _lowerCAmelCase	   =		field(
     default=6_4					,			metadata={
         """help""": (
             """The maximum number of tokens for the question. Questions longer than this will """
             """be truncated to this length."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=3_0					,			metadata={
         """help""": (
             """The maximum length of an answer that can be generated. This is needed because the start """
             """and end predictions are not conditioned on one another."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=_SCREAMING_SNAKE_CASE					,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )
 _lowerCAmelCase	   =		field(
     default=_SCREAMING_SNAKE_CASE					,			metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""}    )
 _lowerCAmelCase	   =		field(
     default=0.0					,			metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}    )
 _lowerCAmelCase	   =		field(
     default=2_0					,			metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}    )
 _lowerCAmelCase	   =		field(
     default=0					,			metadata={
         """help""": (
             """language id of input for language-specific xlm models (see"""
             """ tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
         )
     }					,			)
 _lowerCAmelCase	   =		field(default=1					,			metadata={"""help""": """multiple threads for converting example to features"""}    )





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""train"""
 _lowerCAmelCase	   =		"""dev"""





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = Split.train ,			__magic_name__ = False ,			__magic_name__ = None ,			__magic_name__ = "pt" ,			)   ->							Any:
      _a      =							args
      _a      =							is_language_sensitive
      _a      =							SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
      if isinstance(__magic_name__ ,			__magic_name__  ):
           try:
                _a      =							Split[mode]
           except KeyError:
                raise KeyError('mode is not a valid split name'  )
      _a      =							mode
      # Load data features from cache or dataset file
      _a      =							'v2' if args.version_2_with_negative else 'v1'
      _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}_{version_tag}' ,			)

      # 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(__magic_name__  ):
           if os.path.exists(__magic_name__  ) and not args.overwrite_cache:
                _a      =							time.time()
                _a      =							torch.load(__magic_name__  )

                # Legacy cache files have only features, while new cache files
                # will have dataset and examples also.
                _a      =							self.old_features['features']
                _a      =							self.old_features.get('dataset' ,			__magic_name__  )
                _a      =							self.old_features.get('examples' ,			__magic_name__  )
                logger.info(
                    f'Loading features from cached file {cached_features_file} [took %.3f s]' ,			time.time() - start  )

                if self.dataset is None or self.examples is None:
                     logger.warning(
                         f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
                         ' future run'  )
           else:
                if mode == Split.dev:
                     _a      =							self.processor.get_dev_examples(args.data_dir  )
                else:
                     _a      =							self.processor.get_train_examples(args.data_dir  )

                _a ,				_a      =							squad_convert_examples_to_features(
                    examples=self.examples ,			tokenizer=__magic_name__ ,			max_seq_length=args.max_seq_length ,			doc_stride=args.doc_stride ,			max_query_length=args.max_query_length ,			is_training=mode == Split.train ,			threads=args.threads ,			return_dataset=__magic_name__ ,			)

                _a      =							time.time()
                torch.save(
                    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples} ,			__magic_name__ ,			)
                # ^ 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[Any]:
      return len(self.features  )




 def __getitem__(	self ,			__magic_name__  )   ->							Dict[str, torch.Tensor]:
      # Convert to Tensors and build dataset
      _a      =							self.features[i]

      _a      =							torch.tensor(feature.input_ids ,			dtype=torch.long  )
      _a      =							torch.tensor(feature.attention_mask ,			dtype=torch.long  )
      _a      =							torch.tensor(feature.token_type_ids ,			dtype=torch.long  )
      _a      =							torch.tensor(feature.cls_index ,			dtype=torch.long  )
      _a      =							torch.tensor(feature.p_mask ,			dtype=torch.float  )
      _a      =							torch.tensor(feature.is_impossible ,			dtype=torch.float  )

      _a      =							{
          'input_ids': input_ids,
          'attention_mask': attention_mask,
          'token_type_ids': token_type_ids,
      }

      if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
           del inputs["token_type_ids"]

      if self.args.model_type in ["xlnet", "xlm"]:
           inputs.update({'cls_index': cls_index, 'p_mask': p_mask}  )
           if self.args.version_2_with_negative:
                inputs.update({'is_impossible': is_impossible}  )
           if self.is_language_sensitive:
                inputs.update({'langs': (torch.ones(input_ids.shape ,			dtype=torch.intaa  ) * self.args.lang_id)}  )

      if self.mode == Split.train:
           _a      =							torch.tensor(feature.start_position ,			dtype=torch.long  )
           _a      =							torch.tensor(feature.end_position ,			dtype=torch.long  )
           inputs.update({'start_positions': start_positions, 'end_positions': end_positions}  )

      return inputs

