


"""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 					__magic_name__		:






							def __init__(   self  :    Any  ,     snake_case__  :    Dict  ,     snake_case__  :    int=2  ,     snake_case__  :    List[Any]=8  ,     snake_case__  :    Dict=True  ,     snake_case__  :    Optional[int]=True  ,     snake_case__  :    Any=True  ,     snake_case__  :    int=True  ,     snake_case__  :    Any=9_9  ,     snake_case__  :    Any=1_6  ,     snake_case__  :    Optional[Any]=5  ,     snake_case__  :    List[str]=2  ,     snake_case__  :    Optional[int]=3_6  ,     snake_case__  :    int="gelu"  ,     snake_case__  :    Tuple=0.0  ,     snake_case__  :    List[str]=0.0  ,     snake_case__  :    Dict=5_1_2  ,     snake_case__  :    Any=1_6  ,     snake_case__  :    Tuple=2  ,     snake_case__  :    Any=0.02  ,     snake_case__  :    Tuple=3  ,     snake_case__  :    Optional[int]=4  ,     snake_case__  :    Optional[Any]=None  ,     ):

														'''simple docstring'''






														lowercase		:Any       							=				parent
														lowercase		:Tuple       							=				batch_size
														lowercase		:Dict       							=				seq_length
														lowercase		:str       							=				is_training
														lowercase		:Tuple       							=				use_input_mask
														lowercase		:int       							=				use_token_type_ids
														lowercase		:str       							=				use_labels
														lowercase		:Any       							=				vocab_size
														lowercase		:Tuple       							=				hidden_size
														lowercase		:Optional[Any]       							=				num_hidden_layers
														lowercase		:Optional[Any]       							=				num_attention_heads
														lowercase		:int       							=				intermediate_size
														lowercase		:int       							=				hidden_act
														lowercase		:Optional[int]       							=				hidden_dropout_prob
														lowercase		:Any       							=				attention_probs_dropout_prob
														lowercase		:int       							=				max_position_embeddings
														lowercase		:Optional[Any]       							=				type_vocab_size
														lowercase		:Dict       							=				type_sequence_label_size
														lowercase		:Tuple       							=				initializer_range
														lowercase		:str       							=				num_labels
														lowercase		:str       							=				num_choices
														lowercase		:Optional[int]       							=				scope






							def  __snake_case						(   self  :    Tuple				):

														'''simple docstring'''






														lowercase		:str       							=				ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

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

														lowercase		:Any       							=				None
														if self.use_token_type_ids:
																					lowercase		:List[str]       							=				ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size				)

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

														lowercase		:List[Any]       							=				self.get_config()

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






							def  __snake_case						(   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  __snake_case						(   self  :    Optional[int]				):

														'''simple docstring'''






														lowercase		:List[str]       							=				self.get_config()
														lowercase		:Optional[int]       							=				3_0_0
														return config






							def  __snake_case						(   self  :    Any				):

														'''simple docstring'''






														(
														    lowercase    
														)		:Any       							=				self.prepare_config_and_inputs()

														lowercase		:Union[str, Any]       							=				True
														lowercase		:Any       							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
														lowercase		:Any       							=				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  __snake_case						(   self  :    int  ,     snake_case__  :    int  ,     snake_case__  :    int  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    str  ,     snake_case__  :    str				):

														'''simple docstring'''






														lowercase		:Any       							=				MraModel(config=UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:str       							=				model(UpperCamelCase_  ,     attention_mask=UpperCamelCase_  ,     token_type_ids=UpperCamelCase_				)
														lowercase		:Optional[int]       							=				model(UpperCamelCase_  ,     token_type_ids=UpperCamelCase_				)
														lowercase		:Tuple       							=				model(UpperCamelCase_				)
														self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)				)






							def  __snake_case						(   self  :    str  ,     snake_case__  :    Dict  ,     snake_case__  :    str  ,     snake_case__  :    str  ,     snake_case__  :    str  ,     snake_case__  :    List[str]  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    Any  ,     snake_case__  :    Tuple  ,     snake_case__  :    Any  ,     ):

														'''simple docstring'''






														lowercase		:Optional[Any]       							=				True
														lowercase		:Union[str, Any]       							=				MraModel(UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:Optional[Any]       							=				model(
														    UpperCamelCase_  ,     attention_mask=UpperCamelCase_  ,     token_type_ids=UpperCamelCase_  ,     encoder_hidden_states=UpperCamelCase_  ,     encoder_attention_mask=UpperCamelCase_  ,     )
														lowercase		:Optional[Any]       							=				model(
														    UpperCamelCase_  ,     attention_mask=UpperCamelCase_  ,     token_type_ids=UpperCamelCase_  ,     encoder_hidden_states=UpperCamelCase_  ,     )
														lowercase		:List[str]       							=				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  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Dict  ,     snake_case__  :    Tuple  ,     snake_case__  :    List[str]  ,     snake_case__  :    str  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Union[str, Any]				):

														'''simple docstring'''






														lowercase		:int       							=				MraForMaskedLM(config=UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:str       							=				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  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Dict  ,     snake_case__  :    List[str]  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Any				):

														'''simple docstring'''






														lowercase		:Union[str, Any]       							=				MraForQuestionAnswering(config=UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:Union[str, Any]       							=				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  __snake_case						(   self  :    Any  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    str  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Any				):

														'''simple docstring'''






														lowercase		:Union[str, Any]       							=				self.num_labels
														lowercase		:Dict       							=				MraForSequenceClassification(UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:Optional[int]       							=				model(UpperCamelCase_  ,     attention_mask=UpperCamelCase_  ,     token_type_ids=UpperCamelCase_  ,     labels=UpperCamelCase_				)
														self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)				)






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

														'''simple docstring'''






														lowercase		:Dict       							=				self.num_labels
														lowercase		:int       							=				MraForTokenClassification(config=UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:str       							=				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  __snake_case						(   self  :    str  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Dict  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    List[Any]				):

														'''simple docstring'''






														lowercase		:Optional[Any]       							=				self.num_choices
														lowercase		:int       							=				MraForMultipleChoice(config=UpperCamelCase_				)
														model.to(UpperCamelCase_				)
														model.eval()
														lowercase		:Any       							=				input_ids.unsqueeze(1				).expand(-1  ,     self.num_choices  ,     -1				).contiguous()
														lowercase		:int       							=				token_type_ids.unsqueeze(1				).expand(-1  ,     self.num_choices  ,     -1				).contiguous()
														lowercase		:Dict       							=				input_mask.unsqueeze(1				).expand(-1  ,     self.num_choices  ,     -1				).contiguous()
														lowercase		:Tuple       							=				model(
														    UpperCamelCase_  ,     attention_mask=UpperCamelCase_  ,     token_type_ids=UpperCamelCase_  ,     labels=UpperCamelCase_  ,     )
														self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_choices)				)







							def  __snake_case						(   self  :    List[Any]				):

														'''simple docstring'''






														lowercase		:int       							=				self.prepare_config_and_inputs()
														(
														    lowercase    
														)		:str       							=				config_and_inputs
														lowercase		:List[str]       							=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
														return config, inputs_dict







@require_torch
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
							__A			:      Optional[Any]        =   (
							    (
							        MraModel,
							        MraForMaskedLM,
							        MraForMultipleChoice,
							        MraForQuestionAnswering,
							        MraForSequenceClassification,
							        MraForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							__A			:      int        =   False
							__A			:      Any        =   False
							__A			:      Tuple        =   False
							__A			:      Tuple        =   False

							__A			:      Union[str, Any]        =   ()






							def  __snake_case						(   self  :    int				):

														'''simple docstring'''






														lowercase		:Tuple       							=				MraModelTester(self				)
														lowercase		:Optional[int]       							=				ConfigTester(self  ,     config_class=UpperCamelCase_  ,     hidden_size=3_7				)






							def  __snake_case						(   self  :    Dict				):

														'''simple docstring'''






														self.config_tester.run_common_tests()






							def  __snake_case						(   self  :    Optional[int]				):

														'''simple docstring'''






														lowercase		:Optional[Any]       							=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase_				)






							def  __snake_case						(   self  :    List[Any]				):

														'''simple docstring'''






														lowercase		:List[str]       							=				self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					lowercase		:Optional[int]       							=				type
																					self.model_tester.create_and_check_model(*UpperCamelCase_				)






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

														'''simple docstring'''






														lowercase		:List[Any]       							=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_				)






							def  __snake_case						(   self  :    Any				):

														'''simple docstring'''






														lowercase		:List[str]       							=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_				)






							def  __snake_case						(   self  :    Optional[int]				):

														'''simple docstring'''






														lowercase		:Any       							=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_				)






							def  __snake_case						(   self  :    Any				):

														'''simple docstring'''






														lowercase		:List[str]       							=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_				)






							def  __snake_case						(   self  :    Any				):

														'''simple docstring'''






														lowercase		:Optional[Any]       							=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_				)






							@slow
							def  __snake_case						(   self  :    str				):

														'''simple docstring'''






														for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowercase		:List[Any]       							=				MraModel.from_pretrained(UpperCamelCase_				)
																					self.assertIsNotNone(UpperCamelCase_				)







							@unittest.skip(reason='''MRA does not output attentions'''				)
							def  __snake_case						(   self  :    Optional[Any]				):

														'''simple docstring'''






														return







@require_torch
class 					__magic_name__		(			unittest.TestCase     ):






							@slow
							def  __snake_case						(   self  :    Union[str, Any]				):

														'''simple docstring'''






														lowercase		:Optional[Any]       							=				MraModel.from_pretrained('''uw-madison/mra-base-512-4'''				)
														lowercase		:Optional[Any]       							=				torch.arange(2_5_6				).unsqueeze(0				)

														with torch.no_grad():
																					lowercase		:str       							=				model(UpperCamelCase_				)[0]

														lowercase		:str       							=				torch.Size((1, 2_5_6, 7_6_8)				)
														self.assertEqual(output.shape  ,     UpperCamelCase_				)

														lowercase		:List[Any]       							=				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  __snake_case						(   self  :    Tuple				):

														'''simple docstring'''






														lowercase		:Optional[int]       							=				MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''				)
														lowercase		:Union[str, Any]       							=				torch.arange(2_5_6				).unsqueeze(0				)

														with torch.no_grad():
																					lowercase		:Union[str, Any]       							=				model(UpperCamelCase_				)[0]

														lowercase		:int       							=				5_0_2_6_5

														lowercase		:Optional[int]       							=				torch.Size((1, 2_5_6, vocab_size)				)
														self.assertEqual(output.shape  ,     UpperCamelCase_				)

														lowercase		:Dict       							=				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  __snake_case						(   self  :    Union[str, Any]				):

														'''simple docstring'''






														lowercase		:Union[str, Any]       							=				MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''				)
														lowercase		:Optional[int]       							=				torch.arange(4_0_9_6				).unsqueeze(0				)

														with torch.no_grad():
																					lowercase		:List[str]       							=				model(UpperCamelCase_				)[0]

														lowercase		:Optional[Any]       							=				5_0_2_6_5

														lowercase		:Dict       							=				torch.Size((1, 4_0_9_6, vocab_size)				)
														self.assertEqual(output.shape  ,     UpperCamelCase_				)

														lowercase		:int       							=				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"""



from __future__ import annotations

from decimal import Decimal

from numpy import array






def       lowerCamelCase					(a_						:list[list[float]])     ->							list[list[float]]:
       lowercase		:List[str]       							=				Decimal

       # Check if the provided matrix has 2 rows and 2 columns
       # since this implementation only works for 2x2 matrices
       if len(a_) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
              # Calculate the determinant of the matrix
              lowercase		:List[str]       							=				float(
                  d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
              if determinant == 0:
                     raise ValueError('''This matrix has no inverse.''')

              # Creates a copy of the matrix with swapped positions of the elements
              lowercase		:Optional[Any]       							=				[[0.0, 0.0], [0.0, 0.0]]
              lowercase							,  lowercase		:List[Any]       							=				matrix[1][1], matrix[0][0]
              lowercase							,  lowercase		:Union[str, Any]       							=				-matrix[1][0], -matrix[0][1]

              # Calculate the inverse of the matrix
              return [
                  [(float(d(a_)) / determinant) or 0.0 for n in row] for row in swapped_matrix
              ]
       elif (
           len(a_) == 3
           and len(matrix[0]) == 3
           and len(matrix[1]) == 3
           and len(matrix[2]) == 3
       ):
              # Calculate the determinant of the matrix using Sarrus rule
              lowercase		:Any       							=				float(
                  (
                      (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
                      + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
                      + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
                  )
                  - (
                      (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
                      + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
                      + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
                  ))
              if determinant == 0:
                     raise ValueError('''This matrix has no inverse.''')

              # Creating cofactor matrix
              lowercase		:Optional[Any]       							=				[
                  [d(0.0), d(0.0), d(0.0)],
                  [d(0.0), d(0.0), d(0.0)],
                  [d(0.0), d(0.0), d(0.0)],
              ]
              lowercase		:Optional[int]       							=				(d(matrix[1][1]) * d(matrix[2][2])) - (
                  d(matrix[1][2]) * d(matrix[2][1])
              )
              lowercase		:str       							=				-(
                  (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[1][0]) * d(matrix[2][1])) - (
                  d(matrix[1][1]) * d(matrix[2][0])
              )
              lowercase		:Union[str, Any]       							=				-(
                  (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[0][0]) * d(matrix[2][2])) - (
                  d(matrix[0][2]) * d(matrix[2][0])
              )
              lowercase		:List[str]       							=				-(
                  (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[0][1]) * d(matrix[1][2])) - (
                  d(matrix[0][2]) * d(matrix[1][1])
              )
              lowercase		:Union[str, Any]       							=				-(
                  (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
              )
              lowercase		:Any       							=				(d(matrix[0][0]) * d(matrix[1][1])) - (
                  d(matrix[0][1]) * d(matrix[1][0])
              )

              # Transpose the cofactor matrix (Adjoint matrix)
              lowercase		:Union[str, Any]       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            lowercase		:Any       							=				cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
              lowercase		:str       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            inverse_matrix[i][j] /= d(a_)

        # Calculate the inverse of the matrix
              return [[float(d(a_)) or 0.0 for n in row] for row in inverse_matrix]
       raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''')

