code: 
  
  
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
      )
      from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  class   snake_case__ :
  
  
  
  
  
  
  
         def __init__( self:   int,A_:   Optional[int],A_:   int=13,A_:   Union[str, Any]=7,A_:   Any=True,A_:   List[Any]=True,A_:   Union[str, Any]=True,A_:   str=True,A_:   Union[str, Any]=99,A_:   List[str]=32,A_:   Optional[int]=5,A_:   List[str]=4,A_:   Dict=37,A_:   Dict="gelu",A_:   Optional[Any]=0.1,A_:   str=0.1,A_:   Any=512,A_:   str=16,A_:   Tuple=2,A_:   Any=0.0_2,A_:   Any=3,A_:   Any=4,A_:   Any=None,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = parent
              __UpperCamelCase   = batch_size
              __UpperCamelCase   = seq_length
              __UpperCamelCase   = is_training
              __UpperCamelCase   = use_input_mask
              __UpperCamelCase   = use_token_type_ids
              __UpperCamelCase   = use_labels
              __UpperCamelCase   = vocab_size
              __UpperCamelCase   = hidden_size
              __UpperCamelCase   = num_hidden_layers
              __UpperCamelCase   = num_attention_heads
              __UpperCamelCase   = intermediate_size
              __UpperCamelCase   = hidden_act
              __UpperCamelCase   = hidden_dropout_prob
              __UpperCamelCase   = attention_probs_dropout_prob
              __UpperCamelCase   = max_position_embeddings
              __UpperCamelCase   = type_vocab_size
              __UpperCamelCase   = type_sequence_label_size
              __UpperCamelCase   = initializer_range
              __UpperCamelCase   = num_labels
              __UpperCamelCase   = num_choices
              __UpperCamelCase   = scope
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],self.vocab_size				)
  
              __UpperCamelCase   = None
              if self.use_input_mask:
                   __UpperCamelCase   = random_attention_mask([self.batch_size, self.seq_length]				)
  
              __UpperCamelCase   = None
              if self.use_token_type_ids:
                   __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size				)
  
              __UpperCamelCase   = None
              __UpperCamelCase   = None
              __UpperCamelCase   = None
              if self.use_labels:
                   __UpperCamelCase   = ids_tensor([self.batch_size],self.type_sequence_label_size				)
                   __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],self.num_labels				)
                   __UpperCamelCase   = ids_tensor([self.batch_size],self.num_choices				)
                   __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],1				)
  
              __UpperCamelCase   = self.get_config()
  
              return (
                  config,
                  input_ids,
                  token_type_ids,
                  input_mask,
                  sequence_labels,
                  token_labels,
                  choice_labels,
                  fake_token_labels,
              )
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return ElectraConfig(
                  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,is_decoder=A_,initializer_range=self.initializer_range,)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              (
                  (
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),												
              )   = self.prepare_config_and_inputs()
  
              __UpperCamelCase   = True
              __UpperCamelCase   = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
              __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],vocab_size=2				)
  
              return (
                  config,
                  input_ids,
                  token_type_ids,
                  input_mask,
                  sequence_labels,
                  token_labels,
                  choice_labels,
                  encoder_hidden_states,
                  encoder_attention_mask,
              )
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   List[Any],A_:   int,A_:   int,A_:   Optional[int],A_:   Union[str, Any],A_:   Tuple,A_:   str,A_:   Optional[int],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraModel(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_				)
              __UpperCamelCase   = model(A_,token_type_ids=A_				)
              __UpperCamelCase   = model(A_				)
              self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple,A_:   Optional[int],A_:   Optional[Any],A_:   int,A_:   Any,A_:   int,A_:   Tuple,A_:   Tuple,A_:   List[Any],A_:   int,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = True
              __UpperCamelCase   = ElectraModel(A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,encoder_hidden_states=A_,encoder_attention_mask=A_,)
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,encoder_hidden_states=A_,)
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_				)
              self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any],A_:   List[Any],A_:   List[Any],A_:   Union[str, Any],A_:   Optional[int],A_:   Union[str, Any],A_:   List[str],A_:   List[str],A_:   str,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraForMaskedLM(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = 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 		snake_case_  ( self:   Tuple,A_:   List[Any],A_:   Optional[int],A_:   Any,A_:   Optional[int],A_:   Dict,A_:   Optional[int],A_:   Any,A_:   Tuple,A_:   Dict,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraForCausalLM(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = 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 		snake_case_  ( self:   int,A_:   List[str],A_:   Dict,A_:   Optional[int],A_:   Tuple,A_:   Optional[int],A_:   Optional[Any],A_:   Optional[int],A_:   Union[str, Any],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_labels
              __UpperCamelCase   = ElectraForTokenClassification(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = 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 		snake_case_  ( self:   Dict,A_:   Optional[Any],A_:   str,A_:   int,A_:   str,A_:   Tuple,A_:   str,A_:   Dict,A_:   List[Any],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_labels
              __UpperCamelCase   = ElectraForPreTraining(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   List[Any],A_:   Union[str, Any],A_:   Dict,A_:   List[Any],A_:   Optional[int],A_:   List[Any],A_:   Dict,A_:   Dict,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_labels
              __UpperCamelCase   = ElectraForSequenceClassification(A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   str,A_:   Any,A_:   Optional[Any],A_:   Union[str, Any],A_:   List[str],A_:   List[Any],A_:   Any,A_:   Optional[int],A_:   Any,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraForQuestionAnswering(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = 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 		snake_case_  ( self:   str,A_:   List[Any],A_:   Union[str, Any],A_:   Optional[int],A_:   Dict,A_:   List[str],A_:   str,A_:   Tuple,A_:   Union[str, Any],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_choices
              __UpperCamelCase   = ElectraForMultipleChoice(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = input_ids.unsqueeze(1				).expand(-1,self.num_choices,-1				).contiguous()
              __UpperCamelCase   = token_type_ids.unsqueeze(1				).expand(-1,self.num_choices,-1				).contiguous()
              __UpperCamelCase   = input_mask.unsqueeze(1				).expand(-1,self.num_choices,-1				).contiguous()
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,labels=A_,)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.prepare_config_and_inputs()
              (
                  (
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),						(
                  __UpperCamelCase						
              ),												
              )   = config_and_inputs
              __UpperCamelCase   = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
              return config, inputs_dict
  
  
  
  @require_torch
  class   snake_case__ (_a							,			_a							,			unittest.TestCase	):
         _lowercase       							=    (
             (
                 ElectraModel,
                 ElectraForPreTraining,
                 ElectraForMaskedLM,
                 ElectraForCausalLM,
                 ElectraForMultipleChoice,
                 ElectraForTokenClassification,
                 ElectraForSequenceClassification,
                 ElectraForQuestionAnswering,
             )
             if is_torch_available()
             else ()
         )
         _lowercase       							=    (
             {
                 """feature-extraction""": ElectraModel,
                 """fill-mask""": ElectraForMaskedLM,
                 """question-answering""": ElectraForQuestionAnswering,
                 """text-classification""": ElectraForSequenceClassification,
                 """text-generation""": ElectraForCausalLM,
                 """token-classification""": ElectraForTokenClassification,
                 """zero-shot""": ElectraForSequenceClassification,
             }
             if is_torch_available()
             else {}
         )
         _lowercase       							=    True
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   Any,A_:   List[str],A_:   Optional[Any]=False				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = super()._prepare_for_class(A_,A_,return_labels=A_				)
  
              if return_labels:
                   if model_class in get_values(A_				):
                        __UpperCamelCase   = torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length),dtype=torch.long,device=A_				)
              return inputs_dict
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraModelTester(self				)
              __UpperCamelCase   = ConfigTester(self,config_class=A_,hidden_size=37				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              self.config_tester.run_common_tests()
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_model(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_electra_model_as_decoder(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                   __UpperCamelCase   = type
                   self.model_tester.create_and_check_electra_model(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_masked_lm(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_token_classification(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_pretraining(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_sequence_classification(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_question_answering(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_multiple_choice(*A_				)
  
  
  
  
  
  
  
         @slow
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __UpperCamelCase   = ElectraModel.from_pretrained(A_				)
                   self.assertIsNotNone(A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_electra_for_causal_lm(*A_				)
  
  
  
  @require_torch
  class   snake_case__ (unittest.TestCase	):
  
  
  
  
  
  
  
         @slow
         def 		snake_case_  ( self:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraModel.from_pretrained('google/electra-small-discriminator'				)
              __UpperCamelCase   = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
              __UpperCamelCase   = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
              __UpperCamelCase   = model(A_,attention_mask=A_				)[0]
              __UpperCamelCase   = torch.Size((1, 11, 256)				)
              self.assertEqual(output.shape,A_				)
              __UpperCamelCase   = torch.tensor(
                  [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]				)
  
              self.assertTrue(torch.allclose(output[:, 1:4, 1:4],A_,atol=1E-4				)				)
  
  
  
style_context: 
  
  
  import jax
  import jax.numpy as jnp
  from bigbird_flax import FlaxBigBirdForNaturalQuestions
  from datasets import load_from_disk
  
  from transformers import BigBirdTokenizerFast
  
  
  __snake_case             =	{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
  __snake_case             =	set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=0				,     _lowercase=None   )				->   Union[str, Any]:
  
  
       """simple docstring"""
  
  
       return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=False   )				->   Tuple:
  
  
       """simple docstring"""
  
  
       if make_sub_answers:
            # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
            # *e.g.* if the correct answer contains a prefix such as "the", or "a"
            __UpperCamelCase   = (
                given_answers + get_sub_answers(_lowercase				,     begin=1   ) + get_sub_answers(_lowercase				,     end=-1   )
            )
       __UpperCamelCase   = []
       for answer in given_answers:
            __UpperCamelCase   = answer.replace('_'				,     ' '   ).lower()
            __UpperCamelCase   = ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
            answers.append(' '.join(alias.split()   ).strip()   )
       return set(_lowercase   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=1				,     _lowercase=1_00   )				->   Any:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
       __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
  
       __UpperCamelCase   = best_end_idx[:, None] - best_start_idx[None, :]
       __UpperCamelCase   = jnp.logical_or(widths < 0				,     widths > max_size   )
       __UpperCamelCase   = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
       __UpperCamelCase   = jnp.argmax(_lowercase   ).item()
  
       return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = sample['question']['text']
       __UpperCamelCase   = sample['document']['tokens']['token']
       __UpperCamelCase   = sample['document']['tokens']['is_html']
       __UpperCamelCase   = sample['annotations']['long_answer']
       __UpperCamelCase   = sample['annotations']['short_answers']
  
       __UpperCamelCase   = ' '.join([context[i] for i in range(len(_lowercase   )   ) if not is_html[i]]   )
  
       # 0 - No ; 1 - Yes
       for answer in sample["annotations"]["yes_no_answer"]:
            if answer == 0 or answer == 1:
                 return {
                     "question": question,
                     "context": context_string,
                     "short": [],
                     "long": [],
                     "category": "no" if answer == 0 else "yes",
                 }
  
       __UpperCamelCase   = []
       for s in short_answers:
            short_targets.extend(s['text']   )
       __UpperCamelCase   = list(set(_lowercase   )   )
  
       __UpperCamelCase   = []
       for s in long_answers:
            if s["start_token"] == -1:
                 continue
            __UpperCamelCase   = context[s['start_token'] : s['end_token']]
            __UpperCamelCase   = is_html[s['start_token'] : s['end_token']]
            __UpperCamelCase   = ' '.join([answer[i] for i in range(len(_lowercase   )   ) if not html[i]]   )
            if new_answer not in long_targets:
                 long_targets.append(_lowercase   )
  
       __UpperCamelCase   = 'long_short' if len(short_targets + long_targets   ) > 0 else 'null'
  
       return {
           "question": question,
           "context": context_string,
           "short": short_targets,
           "long": long_targets,
           "category": category,
       }
  
  
  
  
  
  
  def 		_A		(		)				->   Optional[int]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = load_from_disk('natural-questions-validation'   )
       __UpperCamelCase   = dataset.map(_lowercase   ).remove_columns(['annotations', 'document', 'id']   )
       print(_lowercase   )
  
       __UpperCamelCase   = dataset.filter(lambda _lowercase   : (len(x['question']   ) + len(x['context']   )) < 4 * 40_96   )
       __UpperCamelCase   = short_validation_dataset.filter(lambda _lowercase   : x["category"] != "null"   )
       short_validation_dataset
  
       __UpperCamelCase   = 'vasudevgupta/flax-bigbird-natural-questions'
       __UpperCamelCase   = FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase   )
       __UpperCamelCase   = BigBirdTokenizerFast.from_pretrained(_lowercase   )
  
       @jax.jit
       def forward(*_lowercase				,     **_lowercase   ):
            __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = model(*_lowercase				,     **_lowercase   )
            return start_logits, end_logits, jnp.argmax(_lowercase				,     axis=-1   )
  
       def evaluate(_lowercase   ):
            # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
            __UpperCamelCase   = tokenizer(
                example['question']				,     example['context']				,     return_tensors='np'				,     max_length=40_96				,     padding='max_length'				,     truncation=_lowercase				,     )
  
            __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = forward(**_lowercase   )
  
            __UpperCamelCase   = CATEGORY_MAPPING[category.item()]
  
            __UpperCamelCase   = example['long'] + example['short']
            if example["category"] in ["yes", "no", "null"]:
                 __UpperCamelCase   = [example['category']]
            __UpperCamelCase   = example['category'] != 'null'
            # Now target can be: "yes", "no", "null", "list of long & short answers"
  
            if predicted_category in ["yes", "no", "null"]:
                 __UpperCamelCase   = [predicted_category]
                 __UpperCamelCase   = example['output'] == example['targets']
                 __UpperCamelCase   = predicted_category != 'null'
                 return example
  
            __UpperCamelCase   = 38 if predicted_category == 'short' else 10_24
            __UpperCamelCase,						__UpperCamelCase   = get_best_valid_start_end_idx(
                start_scores[0]				,     end_scores[0]				,     top_k=8				,     max_size=_lowercase   )
  
            __UpperCamelCase   = inputs['input_ids'][0].tolist()
            __UpperCamelCase   = [tokenizer.decode(input_ids[start_score : end_score + 1]   )]
  
            __UpperCamelCase   = expand_to_aliases(example['targets']				,     make_sub_answers=_lowercase   )
            __UpperCamelCase   = expand_to_aliases(example['output']   )
  
            # some preprocessing to both prediction and answer
            __UpperCamelCase   = {''.join(a.split()   ) for a in answers}
            __UpperCamelCase   = {''.join(p.split()   ) for p in predictions}
            __UpperCamelCase   = {s for s in predictions if s not in ['``', '\'\'', '`', '\'']}
  
            # if there is a common element, it's a exact match
            __UpperCamelCase   = len(list(answers & predictions   )   ) > 0
            __UpperCamelCase   = predicted_category != 'null' and len(_lowercase   ) > 0
  
            return example
  
       __UpperCamelCase   = short_validation_dataset.map(_lowercase   )
  
       __UpperCamelCase   = len(_lowercase   )
       __UpperCamelCase   = len(short_validation_dataset.filter(lambda _lowercase   : x["match"] == 1   )   )
       print('EM score:'				,     (matched / total) * 1_00				,     '%'   )
  
  
  if __name__ == "__main__":
      main()
  
  
  