




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     __a:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=13  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=99  ,_SCREAMING_SNAKE_CASE=64  ,_SCREAMING_SNAKE_CASE=5  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=64  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=None  ,)					->						Any:
         UpperCAmelCase_     :				str  =     parent
         UpperCAmelCase_     :				str  =     batch_size
         UpperCAmelCase_     :				Dict  =     seq_length
         UpperCAmelCase_     :				Union[str, Any]  =     is_training
         UpperCAmelCase_     :				Optional[int]  =     use_input_mask
         UpperCAmelCase_     :				Tuple  =     use_token_type_ids
         UpperCAmelCase_     :				Tuple  =     use_labels
         UpperCAmelCase_     :				Dict  =     vocab_size
         UpperCAmelCase_     :				Dict  =     hidden_size
         UpperCAmelCase_     :				Dict  =     num_hidden_layers
         UpperCAmelCase_     :				List[Any]  =     num_attention_heads
         UpperCAmelCase_     :				Tuple  =     intermediate_size
         UpperCAmelCase_     :				Union[str, Any]  =     hidden_act
         UpperCAmelCase_     :				Any  =     hidden_dropout_prob
         UpperCAmelCase_     :				Tuple  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				str  =     max_position_embeddings
         UpperCAmelCase_     :				List[str]  =     type_vocab_size
         UpperCAmelCase_     :				Optional[int]  =     type_sequence_label_size
         UpperCAmelCase_     :				Dict  =     initializer_range
         UpperCAmelCase_     :				Any  =     num_labels
         UpperCAmelCase_     :				Optional[int]  =     num_choices
         UpperCAmelCase_     :				Optional[int]  =     scope





    def 				a__ ( self    )					->						Optional[int]:
         return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				str  =     ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

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

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

         UpperCAmelCase_     :				int  =     self.get_config()
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def 				a__ ( self    )					->						Any:
         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  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				int  =     MPNetModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     model(_SCREAMING_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  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						str:
         UpperCAmelCase_     :				List[str]  =     MPNetForQuestionAnswering(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Optional[int]  =     model(
             _SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,start_positions=_SCREAMING_SNAKE_CASE  ,end_positions=_SCREAMING_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  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				str  =     self.num_labels
         UpperCAmelCase_     :				Optional[Any]  =     MPNetForSequenceClassification(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)    )





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				Dict  =     self.num_labels
         UpperCAmelCase_     :				int  =     MPNetForTokenClassification(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Union[str, Any]  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     self.prepare_config_and_inputs()
         ((UpperCAmelCase_),       (UpperCAmelCase_),       (UpperCAmelCase_),       (UpperCAmelCase_),       (UpperCAmelCase_),       (UpperCAmelCase_))     :				Any  =     config_and_inputs
         UpperCAmelCase_     :				Tuple  =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





    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 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     MPNetModelTester(self    )
         UpperCAmelCase_     :				str  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE  ,hidden_size=37    )





    def 				a__ ( self    )					->						Optional[int]:
         self.config_tester.run_common_tests()





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				int  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*_SCREAMING_SNAKE_CASE    )


@require_torch
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     MPNetModel.from_pretrained('''microsoft/mpnet-base'''    )
         UpperCAmelCase_     :				Dict  =     torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]    )
         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE    )[0]
         UpperCAmelCase_     :				List[Any]  =     torch.Size((1, 11, 768)    )
         self.assertEqual(output.shape  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     torch.tensor(
             [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]]    )
         # compare the actual values for a slice.
         self.assertTrue(torch.allclose(output[:, :3, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )


# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							return 1 / (1 + np.exp(-z ))





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							return (-y * np.log(UpperCamelCase__ ) - (1 - y) * np.log(1 - h )).mean()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
							return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase__ ) ) )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=70000 ):







							'''simple docstring'''






							snake_case_     							=       np.zeros(x.shape[1] )

							for iterations in range(UpperCamelCase__ ):
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       np.dot(x.T  ,				h - y ) / y.size
														snake_case_     							=       theta - alpha * gradient  # updating the weights
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       cost_function(UpperCamelCase__  ,				UpperCamelCase__ )
														if iterations % 100 == 0:
																					print(F'''loss: {j} \t''' )  # printing the loss after every 100 iterations
							return theta


# In[68]:

if __name__ == "__main__":
					_UpperCAmelCase						:  str									=			datasets.load_iris()
					_UpperCAmelCase						:  Union[str, Any]									=			iris.data[:, :2]
					_UpperCAmelCase						:  List[str]									=			(iris.target != 0) * 1

					_UpperCAmelCase						:  Optional[int]									=			0.1
					_UpperCAmelCase						:  Dict									=			logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def    __lowerCamelCase      (					UpperCamelCase__ ):







												'''simple docstring'''






												return sigmoid_function(
												    np.dot(UpperCamelCase__  ,				UpperCamelCase__ ) )  # predicting the value of probability from the logistic regression algorithm

					plt.figure(figsize=(10, 6))
					plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
					plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Any									=			(x[:, 0].min(), x[:, 0].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Union[str, Any]									=			(x[:, 1].min(), x[:, 1].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  int									=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					_UpperCAmelCase						:  List[Any]									=			np.c_[xxa.ravel(), xxa.ravel()]
					_UpperCAmelCase						:  Dict									=			predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()




