
'''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 :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=2					,						UpperCamelCase=8					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=99					,						UpperCamelCase=16					,						UpperCamelCase=5					,						UpperCamelCase=2					,						UpperCamelCase=36					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.0					,						UpperCamelCase=0.0					,						UpperCamelCase=512					,						UpperCamelCase=16					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=3					,						UpperCamelCase=4					,						UpperCamelCase=None					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= seq_length
								_SCREAMING_SNAKE_CASE				= is_training
								_SCREAMING_SNAKE_CASE				= use_input_mask
								_SCREAMING_SNAKE_CASE				= use_token_type_ids
								_SCREAMING_SNAKE_CASE				= use_labels
								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= type_sequence_label_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= num_labels
								_SCREAMING_SNAKE_CASE				= num_choices
								_SCREAMING_SNAKE_CASE				= scope




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

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

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

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

								_SCREAMING_SNAKE_CASE				= self.get_config()

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




		def     lowercase       (				self      ):
								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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_config()
								_SCREAMING_SNAKE_CASE				= 300
								return config




		def     lowercase       (				self      ):
								(
								    (
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							
								)				= self.prepare_config_and_inputs()

								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= MraModel(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						token_type_ids=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						token_type_ids=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )
								self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= True
								_SCREAMING_SNAKE_CASE				= MraModel(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						token_type_ids=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						encoder_attention_mask=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= model(
								    UpperCamelCase					,						attention_mask=UpperCamelCase					,						token_type_ids=UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= MraForMaskedLM(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= MraForQuestionAnswering(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.num_labels
								_SCREAMING_SNAKE_CASE				= MraForSequenceClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						token_type_ids=UpperCamelCase					,						labels=UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.prepare_config_and_inputs()
								(
								    (
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							
								)				= config_and_inputs
								_SCREAMING_SNAKE_CASE				= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					(
		    (
		        MraModel,
		        MraForMaskedLM,
		        MraForMultipleChoice,
		        MraForQuestionAnswering,
		        MraForSequenceClassification,
		        MraForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		a							:					Dict       =					False
		a							:					Optional[Any]       =					False
		a							:					Optional[Any]       =					False
		a							:					List[Any]       =					False

		a							:					Any       =					()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MraModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=UpperCamelCase					,						hidden_size=37      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCamelCase      )




		@slow
		def     lowercase       (				self      ):
								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE				= MraModel.from_pretrained(UpperCamelCase      )
														self.assertIsNotNone(UpperCamelCase      )




		@unittest.skip(reason="MRA does not output attentions"      )
		def     lowercase       (				self      ):
								return


@require_torch
class 			lowerCAmelCase (				unittest.TestCase			):




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MraModel.from_pretrained("uw-madison/mra-base-512-4"      )
								_SCREAMING_SNAKE_CASE				= torch.arange(256      ).unsqueeze(0      )

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

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

								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"      )
								_SCREAMING_SNAKE_CASE				= torch.arange(256      ).unsqueeze(0      )

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

								_SCREAMING_SNAKE_CASE				= 50_265

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

								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"      )
								_SCREAMING_SNAKE_CASE				= torch.arange(4_096      ).unsqueeze(0      )

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

								_SCREAMING_SNAKE_CASE				= 50_265

								_SCREAMING_SNAKE_CASE				= torch.Size((1, 4_096, vocab_size)      )
								self.assertEqual(output.shape					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= 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 numpy

# List of input, output pairs
_snake_case				:   Optional[Any]     =				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case				:   Any     =				(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case				:   Any     =				[2, 4, 1, 5]
_snake_case				:   str     =				len(train_data)
_snake_case				:   int     =				0.009



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]="train"  ):
						return calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) - output(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(len(_SCREAMING_SNAKE_CASE  ) - 1  ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str  ):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]  )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]  )
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=m  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(_SCREAMING_SNAKE_CASE  ):
												if index == -1:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  )
												else:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  ) * train_data[i][0][index]
						return summation_value



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= summation_of_cost_derivative(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) / m
						return cost_derivative_value



def 			_a		(	):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_SCREAMING_SNAKE_CASE				= 0.000_002
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						while True:
												j += 1
												_SCREAMING_SNAKE_CASE				= [0, 0, 0, 0]
												for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )  ):
																		_SCREAMING_SNAKE_CASE				= get_cost_derivative(i - 1  )
																		_SCREAMING_SNAKE_CASE				= (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			atol=_SCREAMING_SNAKE_CASE	,			rtol=_SCREAMING_SNAKE_CASE	,			):
																		break
												_SCREAMING_SNAKE_CASE				= temp_parameter_vector
						print(("Number of iterations:", j)  )



def 			_a		(	):
						for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
												print(("Actual output value:", output(_SCREAMING_SNAKE_CASE	,			"test"  ))  )
												print(("Hypothesis output:", calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			"test"  ))  )


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()