



'''simple docstring'''






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 							lowercase       (  __SCREAMING_SNAKE_CASE				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_=13  ,a_=7  ,a_=True  ,a_=True  ,a_=False  ,a_=True  ,a_=99  ,a_=32  ,a_=5  ,a_=4  ,a_=64  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=16  ,a_=2  ,a_=0.02  ,a_=3  ,a_=4  ,a_=None  ,a_=2  ,a_=2  ,a_=2  ,a_=2  ,a_=4  ,a_=1  ,)		->   Optional[int]:
        _UpperCAmelCase  :						Optional[Any]			     = parent
        _UpperCAmelCase  :						Dict			     = batch_size
        _UpperCAmelCase  :						Tuple			     = seq_length
        _UpperCAmelCase  :						List[str]			     = is_training
        _UpperCAmelCase  :						Optional[int]			     = use_input_mask
        _UpperCAmelCase  :						List[str]			     = use_token_type_ids
        _UpperCAmelCase  :						Dict			     = use_labels
        _UpperCAmelCase  :						Dict			     = vocab_size
        _UpperCAmelCase  :						List[str]			     = hidden_size
        _UpperCAmelCase  :						Optional[int]			     = num_hidden_layers
        _UpperCAmelCase  :						Any			     = num_attention_heads
        _UpperCAmelCase  :						int			     = intermediate_size
        _UpperCAmelCase  :						List[Any]			     = hidden_act
        _UpperCAmelCase  :						int			     = hidden_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = max_position_embeddings
        _UpperCAmelCase  :						Optional[Any]			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = type_sequence_label_size
        _UpperCAmelCase  :						List[Any]			     = initializer_range
        _UpperCAmelCase  :						List[str]			     = num_labels
        _UpperCAmelCase  :						Optional[Any]			     = num_choices
        _UpperCAmelCase  :						Optional[int]			     = scope
        _UpperCAmelCase  :						List[str]			     = q_groups
        _UpperCAmelCase  :						Optional[int]			     = k_groups
        _UpperCAmelCase  :						List[str]			     = v_groups
        _UpperCAmelCase  :						Any			     = post_attention_groups
        _UpperCAmelCase  :						List[str]			     = intermediate_groups
        _UpperCAmelCase  :						Union[str, Any]			     = output_groups







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						List[str]			     = ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size							)

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

        _UpperCAmelCase  :						Optional[Any]			     = None
        _UpperCAmelCase  :						str			     = None
        _UpperCAmelCase  :						Dict			     = None
        if self.use_labels:
               _UpperCAmelCase  :						List[Any]			     = 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  :						Tuple			     = ids_tensor([self.batch_size]  ,self.num_choices							)

        _UpperCAmelCase  :						Tuple			     = self.get_config()

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







 def _snake_case (							self							)		->   int:
        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 _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = SqueezeBertModel(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Union[str, Any]			     = model(a_  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = model(a_							)
        self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   Tuple:
        _UpperCAmelCase  :						Optional[Any]			     = SqueezeBertForMaskedLM(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Dict			     = model(a_  ,attention_mask=a_  ,labels=a_							)
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   Optional[Any]:
        _UpperCAmelCase  :						Optional[Any]			     = SqueezeBertForQuestionAnswering(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						List[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 _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   Optional[int]:
        _UpperCAmelCase  :						Tuple			     = self.num_labels
        _UpperCAmelCase  :						Optional[Any]			     = SqueezeBertForSequenceClassification(a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Tuple			     = model(a_  ,attention_mask=a_  ,labels=a_							)
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = self.num_labels
        _UpperCAmelCase  :						Any			     = SqueezeBertForTokenClassification(config=a_							)
        model.to(a_							)
        model.eval()

        _UpperCAmelCase  :						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 _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   str:
        _UpperCAmelCase  :						Any			     = self.num_choices
        _UpperCAmelCase  :						Dict			     = SqueezeBertForMultipleChoice(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						List[str]			     = input_ids.unsqueeze(1							).expand(-1  ,self.num_choices  ,-1							).contiguous()
        _UpperCAmelCase  :						Union[str, Any]			     = input_mask.unsqueeze(1							).expand(-1  ,self.num_choices  ,-1							).contiguous()
        _UpperCAmelCase  :						List[str]			     = model(
            a_  ,attention_mask=a_  ,labels=a_  ,)
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Any			     = self.prepare_config_and_inputs()
        ((_UpperCAmelCase)     ,(_UpperCAmelCase)     ,(_UpperCAmelCase)     ,(_UpperCAmelCase)     ,(_UpperCAmelCase)     ,(_UpperCAmelCase))  :						List[str]			     = config_and_inputs
        _UpperCAmelCase  :						Union[str, Any]			     = {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 							lowercase       (  __SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					unittest.TestCase				):


 """simple docstring"""


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







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						Any			     = SqueezeBertModelTester(self							)
        _UpperCAmelCase  :						List[str]			     = ConfigTester(self  ,config_class=a_  ,dim=37							)







 def _snake_case (							self							)		->   str:
        self.config_tester.run_common_tests()







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Tuple			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_model(*a_							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Tuple			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_masked_lm(*a_							)







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Optional[Any]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_question_answering(*a_							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						List[str]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_sequence_classification(*a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Optional[Any]			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_token_classification(*a_							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_multiple_choice(*a_							)







 @slow
 def _snake_case (							self							)		->   List[str]:
        for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase  :						Optional[int]			     = SqueezeBertModel.from_pretrained(a_							)
               self.assertIsNotNone(a_							)







@require_sentencepiece
@require_tokenizers
@require_torch
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						Optional[int]			     = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""							)

        _UpperCAmelCase  :						List[str]			     = torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]							)
        _UpperCAmelCase  :						Union[str, Any]			     = model(a_							)[0]
        _UpperCAmelCase  :						List[Any]			     = torch.Size((1, 3)							)
        self.assertEqual(output.shape  ,a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = torch.tensor([[0.6401, -0.0349, -0.6041]]							)
        self.assertTrue(torch.allclose(a_  ,a_  ,atol=1E-4							)							)





import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   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.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

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

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

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

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

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

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= 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(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
