





import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=7				,     snake_case_=True				,     snake_case_=True				,     snake_case_=False				,     snake_case_=True				,     snake_case_=99				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=64				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=16				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=3				,     snake_case_=4				,     snake_case_=None				,     snake_case_=2				,     snake_case_=2				,     snake_case_=2				,     snake_case_=2				,     snake_case_=4				,     snake_case_=1				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							seq_length
         _A     =							is_training
         _A     =							use_input_mask
         _A     =							use_token_type_ids
         _A     =							use_labels
         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							intermediate_size
         _A     =							hidden_act
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							type_vocab_size
         _A     =							type_sequence_label_size
         _A     =							initializer_range
         _A     =							num_labels
         _A     =							num_choices
         _A     =							scope
         _A     =							q_groups
         _A     =							k_groups
         _A     =							v_groups
         _A     =							post_attention_groups
         _A     =							intermediate_groups
         _A     =							output_groups




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

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

         _A     =							None
         _A     =							None
         _A     =							None
         if self.use_labels:
             _A     =							ids_tensor([self.batch_size]				,     self.type_sequence_label_size							)
             _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels							)
             _A     =							ids_tensor([self.batch_size]				,     self.num_choices							)

         _A     =							self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def        lowerCAmelCase__		(    self							):
         return SqueezeBertConfig(
             embedding_size=self.hidden_size				,     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				,     attention_probs_dropout_prob=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     initializer_range=self.initializer_range				,     q_groups=self.q_groups				,     k_groups=self.k_groups				,     v_groups=self.v_groups				,     post_attention_groups=self.post_attention_groups				,     intermediate_groups=self.intermediate_groups				,     output_groups=self.output_groups				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							SqueezeBertModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     snake_case_							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							SqueezeBertForMaskedLM(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							SqueezeBertForQuestionAnswering(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							SqueezeBertForSequenceClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							SqueezeBertForTokenClassification(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()

         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_choices
         _A     =							SqueezeBertForMultipleChoice(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							input_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							input_mask.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     labels=snake_case_				,     )
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         ((_A),			(_A),			(_A),			(_A),			(_A),			(_A))     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     __magic_name__   =			(
         {
             'feature-extraction': SqueezeBertModel,
             'fill-mask': SqueezeBertForMaskedLM,
             'question-answering': SqueezeBertForQuestionAnswering,
             'text-classification': SqueezeBertForSequenceClassification,
             'token-classification': SqueezeBertForTokenClassification,
             'zero-shot': SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			True
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							SqueezeBertModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     dim=37							)




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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							SqueezeBertModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)







@require_sentencepiece
@require_tokenizers
@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'							)

         _A     =							torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]							)
         _A     =							model(snake_case_							)[0]
         _A     =							torch.Size((1, 3)							)
         self.assertEqual(output.shape				,     snake_case_							)
         _A     =							torch.tensor([[0.6401, -0.0349, -0.6041]]							)
         self.assertTrue(torch.allclose(snake_case_				,     snake_case_				,     atol=1E-4							)							)













import numpy as np





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case = 1E-12       ,			__snake_case = 100       ,			):
     assert np.shape(__snake_case							)[0] == np.shape(__snake_case							)[1]
     # Ensure proper dimensionality.
     assert np.shape(__snake_case							)[0] == np.shape(__snake_case							)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(__snake_case							) == np.iscomplexobj(__snake_case							)
     __lowerCAmelCase             =       np.iscomplexobj(__snake_case							)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(__snake_case       ,			input_matrix.conj().T							)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __lowerCAmelCase             =       False
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       1E12

     while not convergence:
          # Multiple matrix by the vector.
          __lowerCAmelCase             =       np.dot(__snake_case       ,			__snake_case							)
          # Normalize the resulting output vector.
          __lowerCAmelCase             =       w / np.linalg.norm(__snake_case							)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __lowerCAmelCase             =       vector.conj().T if is_complex else vector.T
          __lowerCAmelCase             =       np.dot(__snake_case       ,			np.dot(__snake_case       ,			__snake_case							)							)

          # Check convergence.
          __lowerCAmelCase             =       np.abs(lambda_ - lambda_previous							) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __lowerCAmelCase             =       True

          __lowerCAmelCase             =       lambda_

     if is_complex:
          __lowerCAmelCase             =       np.real(lambda_							)

     return lambda_, vector





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
     __lowerCAmelCase             =       np.array([41, 4, 20]							)
     __lowerCAmelCase             =       real_input_matrix.astype(np.complexaaa							)
     __lowerCAmelCase             =       np.triu(1J * complex_input_matrix       ,			1							)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __lowerCAmelCase             =       np.array([41, 4, 20]							).astype(np.complexaaa							)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __lowerCAmelCase             =       real_input_matrix
               __lowerCAmelCase             =       real_vector
          elif problem_type == "complex":
               __lowerCAmelCase             =       complex_input_matrix
               __lowerCAmelCase             =       complex_vector

          # Our implementation.
          __lowerCAmelCase      ,			__lowerCAmelCase             =       power_iteration(__snake_case       ,			__snake_case							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __lowerCAmelCase      ,			__lowerCAmelCase             =       np.linalg.eigh(__snake_case							)
          # Last eigenvalue is the maximum one.
          __lowerCAmelCase             =       eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __lowerCAmelCase             =       eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max							) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(__snake_case							) - np.abs(__snake_case							)							) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()






