


'''simple docstring'''







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  lowercase_					:


    def __init__(					self  , a  , a=13  , a=7  , a=True  , a=True  , a=True  , a=True  , a=99  , a=32  , a=5  , a=4  , a=37  , a="gelu"  , a=0.1  , a=0.1  , a=5_12  , a=16  , a=2  , a=0.02  , a=3  , a=4  , a=None  , ):
          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        __a							(					self   ):
          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        __a							(					self   ):
          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        __a							(					self   ):
          (
              (
              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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , ):
          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        __a							(					self   ):
          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  lowercase_					(   a__  ,						a__  ,						unittest.TestCase       ):
    __UpperCAmelCase             =     (
        (
            ElectraModel,
            ElectraForPreTraining,
            ElectraForMaskedLM,
            ElectraForCausalLM,
            ElectraForMultipleChoice,
            ElectraForTokenClassification,
            ElectraForSequenceClassification,
            ElectraForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    __UpperCAmelCase             =     (
        {
            '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 {}
    )
    __UpperCAmelCase             =     True


    def        __a							(					self  , a  , a  , a=False   ):
          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        __a							(					self   ):
          UpperCamelCase__  =    ElectraModelTester(self   )
          UpperCamelCase__  =    ConfigTester(self  , config_class=a  , hidden_size=37   )


    def        __a							(					self   ):
          self.config_tester.run_common_tests()


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_model(*a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_electra_model_as_decoder(*a   )


    def        __a							(					self   ):
          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        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_masked_lm(*a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_token_classification(*a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_pretraining(*a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_sequence_classification(*a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_question_answering(*a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_multiple_choice(*a   )


    @slow
    def        __a							(					self   ):
          for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCamelCase__  =    ElectraModel.from_pretrained(a   )
                self.assertIsNotNone(a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_electra_for_causal_lm(*a   )

@require_torch
class  lowercase_					(   unittest.TestCase       ):


    @slow
    def        __a							(					self   ):
          UpperCamelCase__  =    ElectraModel.from_pretrained("google/electra-small-discriminator"   )
          UpperCamelCase__  =    torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 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, 2_56)   )
          self.assertEqual(output.shape  , a   )
          UpperCamelCase__  =    torch.tensor(
              [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]   )

          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  , a  , atol=1e-4   )   )









'''simple docstring'''







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      if number != int(__A   ):
            raise ValueError("factorial() only accepts integral values"   )
      if number < 0:
            raise ValueError("factorial() not defined for negative values"   )
      UpperCamelCase__  =    1
      for i in range(1				,      number + 1   ):
            value *= i
      return value







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      if not isinstance(__A				,      __A   ):
            raise ValueError("factorial() only accepts integral values"   )
      if n < 0:
            raise ValueError("factorial() not defined for negative values"   )
      return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a__  : str				=	int(input('Enter a positive integer: ').strip() or 0)
  print(F"""factorial{n} is {factorial(n)}""")





