


'''simple docstring'''

import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
 import torch

 from transformers import (
     MraForMaskedLM,
     MraForMultipleChoice,
     MraForQuestionAnswering,
     MraForSequenceClassification,
     MraForTokenClassification,
     MraModel,
 )
 from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Any  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       int=2  ,		UpperCamelCase       :       int=8  ,		UpperCamelCase       :       List[str]=True  ,		UpperCamelCase       :       List[str]=True  ,		UpperCamelCase       :       Optional[Any]=True  ,		UpperCamelCase       :       Union[str, Any]=True  ,		UpperCamelCase       :       Union[str, Any]=99  ,		UpperCamelCase       :       Tuple=16  ,		UpperCamelCase       :       Union[str, Any]=5  ,		UpperCamelCase       :       Optional[Any]=2  ,		UpperCamelCase       :       List[str]=36  ,		UpperCamelCase       :       List[Any]="gelu"  ,		UpperCamelCase       :       Optional[int]=0.0  ,		UpperCamelCase       :       List[str]=0.0  ,		UpperCamelCase       :       Optional[Any]=512  ,		UpperCamelCase       :       Any=16  ,		UpperCamelCase       :       Optional[Any]=2  ,		UpperCamelCase       :       Union[str, Any]=0.02  ,		UpperCamelCase       :       Tuple=3  ,		UpperCamelCase       :       Dict=4  ,		UpperCamelCase       :       Optional[Any]=None  ,		):

             '''simple docstring'''







             lowercase__      =					parent
             lowercase__      =					batch_size
             lowercase__      =					seq_length
             lowercase__      =					is_training
             lowercase__      =					use_input_mask
             lowercase__      =					use_token_type_ids
             lowercase__      =					use_labels
             lowercase__      =					vocab_size
             lowercase__      =					hidden_size
             lowercase__      =					num_hidden_layers
             lowercase__      =					num_attention_heads
             lowercase__      =					intermediate_size
             lowercase__      =					hidden_act
             lowercase__      =					hidden_dropout_prob
             lowercase__      =					attention_probs_dropout_prob
             lowercase__      =					max_position_embeddings
             lowercase__      =					type_vocab_size
             lowercase__      =					type_sequence_label_size
             lowercase__      =					initializer_range
             lowercase__      =					num_labels
             lowercase__      =					num_choices
             lowercase__      =					scope



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size					)

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

             lowercase__      =					None
             if self.use_token_type_ids:
                    lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.type_vocab_size					)

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

             lowercase__      =					self.get_config()

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



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             return MraConfig(
                 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  ,		type_vocab_size=self.type_vocab_size  ,		is_decoder=UpperCamelCase  ,		initializer_range=self.initializer_range  ,		)



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.get_config()
             lowercase__      =					300
             return config



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             (
                 (
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,
             )      =					self.prepare_config_and_inputs()

             lowercase__      =					True
             lowercase__      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
             lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		vocab_size=2					)

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
                 choice_labels,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					MraModel(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase  ,		token_type_ids=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)
             self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       str  ,		):

             '''simple docstring'''







             lowercase__      =					True
             lowercase__      =					MraModel(UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(
                 UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		encoder_hidden_states=UpperCamelCase  ,		encoder_attention_mask=UpperCamelCase  ,		)
             lowercase__      =					model(
                 UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		encoder_hidden_states=UpperCamelCase  ,		)
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase					)
             self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					MraForMaskedLM(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size)					)



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					MraForQuestionAnswering(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(
                 UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		start_positions=UpperCamelCase  ,		end_positions=UpperCamelCase  ,		)
             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  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					MraForSequenceClassification(UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels)					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					MraForTokenClassification(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.num_labels)					)



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					self.num_choices
             lowercase__      =					MraForMultipleChoice(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					input_ids.unsqueeze(1					).expand(-1  ,		self.num_choices  ,		-1					).contiguous()
             lowercase__      =					token_type_ids.unsqueeze(1					).expand(-1  ,		self.num_choices  ,		-1					).contiguous()
             lowercase__      =					input_mask.unsqueeze(1					).expand(-1  ,		self.num_choices  ,		-1					).contiguous()
             lowercase__      =					model(
                 UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		labels=UpperCamelCase  ,		)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_choices)					)






      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,
             )      =					config_and_inputs
             lowercase__      =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Union[str, Any]       =				(
          (
              MraModel,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__   :	Optional[int]       =				False
      lowerCAmelCase__   :	int       =				False
      lowerCAmelCase__   :	str       =				False
      lowerCAmelCase__   :	Tuple       =				False

      lowerCAmelCase__   :	Optional[Any]       =				()



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					MraModelTester(self					)
             lowercase__      =					ConfigTester(self  ,		config_class=UpperCamelCase  ,		hidden_size=37					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    lowercase__      =					type
                    self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase					)



      @slow
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      =					MraModel.from_pretrained(UpperCamelCase					)
                    self.assertIsNotNone(UpperCamelCase					)






      @unittest.skip(reason='''MRA does not output attentions'''					)
      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             return






@require_torch
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      @slow
      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					MraModel.from_pretrained('''uw-madison/mra-base-512-4'''					)
             lowercase__      =					torch.arange(256					).unsqueeze(0					)

             with torch.no_grad():
                    lowercase__      =					model(UpperCamelCase					)[0]

             lowercase__      =					torch.Size((1, 256, 768)					)
             self.assertEqual(output.shape  ,		UpperCamelCase					)

             lowercase__      =					torch.tensor(
                 [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]					)

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)



      @slow
      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''					)
             lowercase__      =					torch.arange(256					).unsqueeze(0					)

             with torch.no_grad():
                    lowercase__      =					model(UpperCamelCase					)[0]

             lowercase__      =					50265

             lowercase__      =					torch.Size((1, 256, vocab_size)					)
             self.assertEqual(output.shape  ,		UpperCamelCase					)

             lowercase__      =					torch.tensor(
                 [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]					)

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)






      @slow
      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''					)
             lowercase__      =					torch.arange(4096					).unsqueeze(0					)

             with torch.no_grad():
                    lowercase__      =					model(UpperCamelCase					)[0]

             lowercase__      =					50265

             lowercase__      =					torch.Size((1, 4096, vocab_size)					)
             self.assertEqual(output.shape  ,		UpperCamelCase					)

             lowercase__      =					torch.tensor(
                 [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]					)

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)








'''simple docstring'''

import unittest

import numpy as np

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A = None    ,					)			->			np.ndarray:






       """simple docstring"""







       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)

       if shape_a[0] != shape_b[0]:
              lowercase__      =					(
                  '''Expected the same number of rows for A and B. '''
                  f"Instead found A of size {shape_a} and B of size {shape_b}"
              )
              raise ValueError(A			)

       if shape_b[1] != shape_c[1]:
              lowercase__      =					(
                  '''Expected the same number of columns for B and C. '''
                  f"Instead found B of size {shape_b} and C of size {shape_c}"
              )
              raise ValueError(A			)

       lowercase__      =					pseudo_inv
       if a_inv is None:
              try:
                     lowercase__      =					np.linalg.inv(A			)
              except np.linalg.LinAlgError:
                     raise ValueError(
                         '''Input matrix A is not invertible. Cannot compute Schur complement.'''			)

       return mat_c - mat_b.T @ a_inv @ mat_b
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1], [6, 3]]					)

             lowercase__      =					schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

             lowercase__      =					np.block([[a, b], [b.T, c]]					)

             lowercase__      =					np.linalg.det(UpperCamelCase					)
             lowercase__      =					np.linalg.det(UpperCamelCase					)
             lowercase__      =					np.linalg.det(UpperCamelCase					)

             self.assertAlmostEqual(UpperCamelCase  ,		det_a * det_s					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1], [6, 3]]					)

             with self.assertRaises(UpperCamelCase					):
                    schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1, 3], [6, 3, 5]]					)

             with self.assertRaises(UpperCamelCase					):
                    schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()




