






"""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 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							,			snake_case__      :	int							,			snake_case__      :	str=1_3							,			snake_case__      :	Dict=7							,			snake_case__      :	Tuple=True							,			snake_case__      :	str=True							,			snake_case__      :	Tuple=False							,			snake_case__      :	int=True							,			snake_case__      :	Any=9_9							,			snake_case__      :	Any=6_4							,			snake_case__      :	Optional[int]=5							,			snake_case__      :	int=4							,			snake_case__      :	int=6_4							,			snake_case__      :	Tuple="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Any=0.1							,			snake_case__      :	Optional[Any]=5_1_2							,			snake_case__      :	Union[str, Any]=1_6							,			snake_case__      :	Dict=2							,			snake_case__      :	Any=0.02							,			snake_case__      :	Union[str, Any]=3							,			snake_case__      :	Optional[int]=4							,			snake_case__      :	Tuple=None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							parent
        UpperCAmelCase__						:    Optional[int]							=							batch_size
        UpperCAmelCase__						:    Optional[int]							=							seq_length
        UpperCAmelCase__						:    Union[str, Any]							=							is_training
        UpperCAmelCase__						:    Tuple							=							use_input_mask
        UpperCAmelCase__						:    Optional[int]							=							use_token_type_ids
        UpperCAmelCase__						:    Optional[int]							=							use_labels
        UpperCAmelCase__						:    Any							=							vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_size
        UpperCAmelCase__						:    int							=							num_hidden_layers
        UpperCAmelCase__						:    Optional[Any]							=							num_attention_heads
        UpperCAmelCase__						:    Optional[int]							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    str							=							hidden_dropout_prob
        UpperCAmelCase__						:    Union[str, Any]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Optional[Any]							=							max_position_embeddings
        UpperCAmelCase__						:    Tuple							=							type_vocab_size
        UpperCAmelCase__						:    Optional[Any]							=							type_sequence_label_size
        UpperCAmelCase__						:    List[Any]							=							initializer_range
        UpperCAmelCase__						:    Optional[int]							=							num_labels
        UpperCAmelCase__						:    int							=							num_choices
        UpperCAmelCase__						:    Dict							=							scope
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        return MPNetConfig.from_pretrained("microsoft/mpnet-base"							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size							)

        UpperCAmelCase__						:    List[str]							=							None
        if self.use_input_mask:
             UpperCAmelCase__						:    int							=							random_attention_mask([self.batch_size, self.seq_length]							)

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

        UpperCAmelCase__						:    Dict							=							self.get_config()
        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        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       __a							(		self      :	int							,			snake_case__      :	Any							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[str]							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							MPNetModel(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Dict							=							model(snake_case__							)
        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       __a							(		self      :	List[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Any							,			snake_case__      :	List[str]							,			snake_case__      :	Optional[int]							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							MPNetForQuestionAnswering(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    List[Any]							=							model(
            snake_case__							,			attention_mask=snake_case__							,			start_positions=snake_case__							,			end_positions=snake_case__							,			)
        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      :	Optional[int]							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	Any							,			snake_case__      :	List[Any]							,			snake_case__      :	Tuple							,			snake_case__      :	Any							,			snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.num_labels
        UpperCAmelCase__						:    Union[str, Any]							=							MPNetForSequenceClassification(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Any							=							model(snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)							)
   def       __a							(		self      :	List[str]							,			snake_case__      :	Any							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	int							,			snake_case__      :	Dict							,			snake_case__      :	List[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							self.num_choices
        UpperCAmelCase__						:    Tuple							=							MPNetForMultipleChoice(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    List[Any]							=							input_ids.unsqueeze(1							).expand(-1							,			self.num_choices							,			-1							).contiguous()
        UpperCAmelCase__						:    Optional[Any]							=							input_mask.unsqueeze(1							).expand(-1							,			self.num_choices							,			-1							).contiguous()
        UpperCAmelCase__						:    Tuple							=							model(
            snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							,			)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_choices)							)
   def       __a							(		self      :	str							,			snake_case__      :	List[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[int]							,			snake_case__      :	List[str]							,			snake_case__      :	Tuple							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.num_labels
        UpperCAmelCase__						:    Union[str, Any]							=							MPNetForTokenClassification(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)							)




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.prepare_config_and_inputs()
        ((UpperCAmelCase__)							,				(UpperCAmelCase__)							,				(UpperCAmelCase__)							,				(UpperCAmelCase__)							,				(UpperCAmelCase__)							,				(UpperCAmelCase__))						:    Dict							=							config_and_inputs
        UpperCAmelCase__						:    List[str]							=							{"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_torch
class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(
       (
           MPNetForMaskedLM,
           MPNetForMultipleChoice,
           MPNetForQuestionAnswering,
           MPNetForSequenceClassification,
           MPNetForTokenClassification,
           MPNetModel,
       )
       if is_torch_available()
       else ()
   )
   SCREAMING_SNAKE_CASE_    =(
       {
           '''feature-extraction''': MPNetModel,
           '''fill-mask''': MPNetForMaskedLM,
           '''question-answering''': MPNetForQuestionAnswering,
           '''text-classification''': MPNetForSequenceClassification,
           '''token-classification''': MPNetForTokenClassification,
           '''zero-shot''': MPNetForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =True
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							MPNetModelTester(self							)
        UpperCAmelCase__						:    List[str]							=							ConfigTester(self							,			config_class=snake_case__							,			hidden_size=3_7							)
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_model(*snake_case__							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case__							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case__							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case__							)




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case__							)




@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							MPNetModel.from_pretrained("microsoft/mpnet-base"							)
        UpperCAmelCase__						:    str							=							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]]							)
        UpperCAmelCase__						:    List[str]							=							model(snake_case__							)[0]
        UpperCAmelCase__						:    int							=							torch.Size((1, 1_1, 7_6_8)							)
        self.assertEqual(output.shape							,			snake_case__							)
        UpperCAmelCase__						:    Tuple							=							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]							,			snake_case__							,			atol=1e-4							)							)










"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


