
'''simple docstring'''






import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     MPNetForMaskedLM,
     MPNetForMultipleChoice,
     MPNetForQuestionAnswering,
     MPNetForSequenceClassification,
     MPNetForTokenClassification,
     MPNetModel,
 )

class 			UpperCAmelCase__    :



    def __init__(						self   :							Union[str, Any],__A   :							Optional[int],__A   :							Union[str, Any]=1_3,__A   :							str=7,__A   :							str=True,__A   :							Dict=True,__A   :							int=False,__A   :							str=True,__A   :							Optional[Any]=9_9,__A   :							int=6_4,__A   :							Tuple=5,__A   :							Union[str, Any]=4,__A   :							Tuple=6_4,__A   :							Union[str, Any]="gelu",__A   :							Dict=0.1,__A   :							int=0.1,__A   :							List[str]=5_1_2,__A   :							Tuple=1_6,__A   :							List[str]=2,__A   :							int=0.02,__A   :							int=3,__A   :							int=4,__A   :							Any=None,):
          _lowerCamelCase					: Dict		    = parent
          _lowerCamelCase					: Tuple		    = batch_size
          _lowerCamelCase					: str		    = seq_length
          _lowerCamelCase					: Any		    = is_training
          _lowerCamelCase					: Dict		    = use_input_mask
          _lowerCamelCase					: int		    = use_token_type_ids
          _lowerCamelCase					: Optional[Any]		    = use_labels
          _lowerCamelCase					: int		    = vocab_size
          _lowerCamelCase					: Any		    = hidden_size
          _lowerCamelCase					: Tuple		    = num_hidden_layers
          _lowerCamelCase					: Optional[int]		    = num_attention_heads
          _lowerCamelCase					: List[Any]		    = intermediate_size
          _lowerCamelCase					: int		    = hidden_act
          _lowerCamelCase					: List[str]		    = hidden_dropout_prob
          _lowerCamelCase					: List[str]		    = attention_probs_dropout_prob
          _lowerCamelCase					: Optional[int]		    = max_position_embeddings
          _lowerCamelCase					: int		    = type_vocab_size
          _lowerCamelCase					: List[Any]		    = type_sequence_label_size
          _lowerCamelCase					: int		    = initializer_range
          _lowerCamelCase					: Any		    = num_labels
          _lowerCamelCase					: str		    = num_choices
          _lowerCamelCase					: Optional[int]		    = scope



    def 	lowerCamelCase_	(						self   :							str  ):
          return MPNetConfig.from_pretrained("microsoft/mpnet-base"  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Optional[Any]		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

          _lowerCamelCase					: Optional[Any]		    = None
          if self.use_input_mask:
                _lowerCamelCase					: Union[str, Any]		    = random_attention_mask([self.batch_size, self.seq_length]  )

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

          _lowerCamelCase					: List[Any]		    = self.get_config()
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



    def 	lowerCamelCase_	(						self   :							str  ):
          return MPNetConfig(
              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,initializer_range=self.initializer_range,)



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							Union[str, Any],__A   :							Any,__A   :							Dict,__A   :							List[Any],__A   :							Optional[int],__A   :							str  ):
          _lowerCamelCase					: Any		    = MPNetModel(config=__A  )
          model.to(__A  )
          model.eval()
          _lowerCamelCase					: List[str]		    = model(__A,__A  )
          _lowerCamelCase					: List[str]		    = model(__A  )
          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)  )
          self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.hidden_size)  )



    def 	lowerCamelCase_	(						self   :							str,__A   :							Tuple,__A   :							List[str],__A   :							List[Any],__A   :							str,__A   :							Optional[Any],__A   :							Union[str, Any]  ):
          _lowerCamelCase					: Tuple		    = MPNetForQuestionAnswering(config=__A  )
          model.to(__A  )
          model.eval()
          _lowerCamelCase					: Optional[Any]		    = model(
              __A,attention_mask=__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 	lowerCamelCase_	(						self   :							Dict,__A   :							str,__A   :							Any,__A   :							Any,__A   :							int,__A   :							List[Any],__A   :							Optional[Any]  ):
          _lowerCamelCase					: Union[str, Any]		    = self.num_labels
          _lowerCamelCase					: Dict		    = MPNetForSequenceClassification(__A  )
          model.to(__A  )
          model.eval()
          _lowerCamelCase					: Tuple		    = model(__A,attention_mask=__A,labels=__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)  )



    def 	lowerCamelCase_	(						self   :							Union[str, Any],__A   :							Optional[int],__A   :							List[Any],__A   :							Tuple,__A   :							int,__A   :							Tuple,__A   :							Any  ):
          _lowerCamelCase					: Union[str, Any]		    = self.num_choices
          _lowerCamelCase					: Optional[Any]		    = MPNetForMultipleChoice(config=__A  )
          model.to(__A  )
          model.eval()
          _lowerCamelCase					: List[Any]		    = input_ids.unsqueeze(1  ).expand(-1,self.num_choices,-1  ).contiguous()
          _lowerCamelCase					: int		    = input_mask.unsqueeze(1  ).expand(-1,self.num_choices,-1  ).contiguous()
          _lowerCamelCase					: Union[str, Any]		    = model(
              __A,attention_mask=__A,labels=__A,)
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)  )



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							str,__A   :							List[str],__A   :							int,__A   :							Optional[int],__A   :							Any,__A   :							Any  ):
          _lowerCamelCase					: Any		    = self.num_labels
          _lowerCamelCase					: List[Any]		    = MPNetForTokenClassification(config=__A  )
          model.to(__A  )
          model.eval()
          _lowerCamelCase					: List[str]		    = model(__A,attention_mask=__A,labels=__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: int		    = self.prepare_config_and_inputs()
          ((_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase))					: List[Any]		    = config_and_inputs
          _lowerCamelCase					: Optional[int]		    = {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict


@require_torch
class 			UpperCAmelCase__    (							A    ,						A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(
        (
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetModel,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase_					     =		(
        {
            'feature-extraction': MPNetModel,
            'fill-mask': MPNetForMaskedLM,
            'question-answering': MPNetForQuestionAnswering,
            'text-classification': MPNetForSequenceClassification,
            'token-classification': MPNetForTokenClassification,
            'zero-shot': MPNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		True



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase					: Any		    = MPNetModelTester(self  )
          _lowerCamelCase					: Tuple		    = ConfigTester(self,config_class=__A,hidden_size=3_7  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          self.config_tester.run_common_tests()



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: List[str]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*__A  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*__A  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Dict		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*__A  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*__A  )



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Union[str, Any]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*__A  )


@require_torch
class 			UpperCAmelCase__    (							unittest.TestCase			):



    @slow
    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: List[Any]		    = MPNetModel.from_pretrained("microsoft/mpnet-base"  )
          _lowerCamelCase					: Optional[Any]		    = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]  )
          _lowerCamelCase					: Dict		    = model(__A  )[0]
          _lowerCamelCase					: int		    = torch.Size((1, 1_1, 7_6_8)  )
          self.assertEqual(output.shape,__A  )
          _lowerCamelCase					: int		    = torch.tensor(
              [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]  )
          # compare the actual values for a slice.
          self.assertTrue(torch.allclose(output[:, :3, :3],__A,atol=1e-4  )  )




"""simple docstring"""






from ...configuration_utils import PretrainedConfig



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''bert-generation'''



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	Optional[int]=5_0_3_5_8							,       __lowerCamelCase    :	List[str]=1_0_2_4							,       __lowerCamelCase    :	Optional[Any]=2_4							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Union[str, Any]=4_0_9_6							,       __lowerCamelCase    :	Optional[Any]="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	List[str]=0.1							,       __lowerCamelCase    :	int=5_1_2							,       __lowerCamelCase    :	Dict=0.0_2							,       __lowerCamelCase    :	Tuple=1E-12							,       __lowerCamelCase    :	Any=0							,       __lowerCamelCase    :	Union[str, Any]=2							,       __lowerCamelCase    :	List[Any]=1							,       __lowerCamelCase    :	Optional[Any]="absolute"							,       __lowerCamelCase    :	str=True							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     super().__init__(pad_token_id=__lowerCamelCase							,       bos_token_id=__lowerCamelCase							,       eos_token_id=__lowerCamelCase							,       **__lowerCamelCase				)

     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      hidden_act
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      initializer_range
     _snake_case   	=      layer_norm_eps
     _snake_case   	=      position_embedding_type
     _snake_case   	=      use_cache





