

import numpy as np


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:






    '''simple docstring'''
    __UpperCAmelCase					,     __UpperCAmelCase									=			a.shape
    __UpperCAmelCase									=			min(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			np.eye(SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			a.copy()

    for k in range(t - 1      ):
        # select a column of modified matrix A':
        __UpperCAmelCase									=			r[k:, [k]]
        # construct first basis vector
        __UpperCAmelCase									=			np.zeros_like(SCREAMING_SNAKE_CASE      )
        __UpperCAmelCase									=			1.0
        # determine scaling factor
        __UpperCAmelCase									=			np.linalg.norm(SCREAMING_SNAKE_CASE      )
        # construct vector v for Householder reflection
        __UpperCAmelCase									=			x + np.sign(x[0]      ) * alpha * ea
        v /= np.linalg.norm(SCREAMING_SNAKE_CASE      )

        # construct the Householder matrix
        __UpperCAmelCase									=			np.eye(m - k      ) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        __UpperCAmelCase									=			np.block([[np.eye(SCREAMING_SNAKE_CASE      ), np.zeros((k, m - k)      )], [np.zeros((m - k, k)      ), q_k]]      )

        __UpperCAmelCase									=			q @ q_k.T
        __UpperCAmelCase									=			q_k @ r

    return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()







from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.mpnet.modeling_tf_mpnet import (
           TFMPNetForMaskedLM,
           TFMPNetForMultipleChoice,
           TFMPNetForQuestionAnswering,
           TFMPNetForSequenceClassification,
           TFMPNetForTokenClassification,
           TFMPNetModel,
       )
class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__=13     ,      lowercase__=7     ,      lowercase__=True     ,      lowercase__=True     ,      lowercase__=False     ,      lowercase__=True     ,      lowercase__=99     ,      lowercase__=64     ,      lowercase__=2     ,      lowercase__=4     ,      lowercase__=64     ,      lowercase__="gelu"     ,      lowercase__=0.1     ,      lowercase__=0.1     ,      lowercase__=512     ,      lowercase__=16     ,      lowercase__=2     ,      lowercase__=0.02     ,      lowercase__=3     ,      lowercase__=4     ,      lowercase__=None     ,      )     ->      Tuple:
        __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      lowerCAmelCase_						(self  )     ->      str:
        __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
        __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									=			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     ,      )
        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[Any]:
        __UpperCAmelCase									=			TFMPNetModel(config=lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        __UpperCAmelCase									=			[input_ids, input_mask]
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Dict:
        __UpperCAmelCase									=			TFMPNetForMaskedLM(config=lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[Any]:
        __UpperCAmelCase									=			TFMPNetForQuestionAnswering(config=lowercase__  )
        __UpperCAmelCase									=			{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
        }
        __UpperCAmelCase									=			model(lowercase__  )
        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     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
        __UpperCAmelCase									=			self.num_labels
        __UpperCAmelCase									=			TFMPNetForSequenceClassification(lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
        __UpperCAmelCase									=			self.num_choices
        __UpperCAmelCase									=			TFMPNetForMultipleChoice(lowercase__  )
        __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
        __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
        __UpperCAmelCase									=			{
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
        }
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
        __UpperCAmelCase									=			self.num_labels
        __UpperCAmelCase									=			TFMPNetForTokenClassification(lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )






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






@require_tf
class 					A_   (      _a ,     _a ,     unittest.TestCase						):


    '''simple docstring'''






    a__     							=       (
        (
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetModel,
        )
        if is_tf_available()
        else ()
    )
    a__     							=       (
        {
            "feature-extraction": TFMPNetModel,
            "fill-mask": TFMPNetForMaskedLM,
            "question-answering": TFMPNetForQuestionAnswering,
            "text-classification": TFMPNetForSequenceClassification,
            "token-classification": TFMPNetForTokenClassification,
            "zero-shot": TFMPNetForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    a__     							=       False
    a__     							=       False


    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase									=			TFMPNetModelTester(self  )
        __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      hidden_size=37  )


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


    def      lowerCAmelCase_						(self  )     ->      Dict:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_model(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_masked_lm(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase__  )






    @slow
    def      lowerCAmelCase_						(self  )     ->      List[str]:
        for model_name in ["microsoft/mpnet-base"]:
            __UpperCAmelCase									=			TFMPNetModel.from_pretrained(lowercase__  )
            self.assertIsNotNone(lowercase__  )






@require_tf
class 					A_   (      unittest.TestCase						):


    '''simple docstring'''






    @slow
    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
        __UpperCAmelCase									=			tf.constant([[0, 1, 2, 3, 4, 5]]  )
        __UpperCAmelCase									=			model(lowercase__  )[0]

        __UpperCAmelCase									=			[1, 6, 768]
        self.assertEqual(output.shape     ,      lowercase__  )

        __UpperCAmelCase									=			tf.constant(
            [
                [
                    [-0.1067172, 0.08216473, 0.0024543],
                    [-0.03465879, 0.8354118, -0.03252288],
                    [-0.06569476, -0.12424111, -0.0494436],
                ]
            ]  )
        tf.debugging.assert_near(output[:, :3, :3]     ,      lowercase__     ,      atol=1E-4  )




