

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					,  __snake_case					,  __snake_case=2					,  __snake_case=8					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=99					,  __snake_case=16					,  __snake_case=5					,  __snake_case=2					,  __snake_case=36					,  __snake_case="gelu"					,  __snake_case=0.0					,  __snake_case=0.0					,  __snake_case=512					,  __snake_case=16					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=3					,  __snake_case=4					,  __snake_case=None					,  )   -> List[Any]:




						'''simple docstring'''
						__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





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__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
						if self.use_token_type_ids:
									__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size					)

						__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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''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=__snake_case					,  initializer_range=self.initializer_range					,  )





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =self.get_config()
						__a        =300
						return config





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						(
						    __a 
						)        =self.prepare_config_and_inputs()

						__a        =True
						__a        =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
						__a        =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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> List[Any]:




						'''simple docstring'''
						__a        =MraModel(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					)
						__a        =model(__snake_case					,  token_type_ids=__snake_case					)
						__a        =model(__snake_case					)
						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> List[str]:




						'''simple docstring'''
						__a        =True
						__a        =MraModel(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  )
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					,  encoder_hidden_states=__snake_case					,  )
						__a        =model(__snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					)
						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						__a        =MraForMaskedLM(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					,  labels=__snake_case					)
						self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						__a        =MraForQuestionAnswering(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  token_type_ids=__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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> List[Any]:




						'''simple docstring'''
						__a        =self.num_labels
						__a        =MraForSequenceClassification(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					,  labels=__snake_case					)
						self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        =self.num_labels
						__a        =MraForTokenClassification(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					,  token_type_ids=__snake_case					,  labels=__snake_case					)
						self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.num_labels)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						__a        =self.num_choices
						__a        =MraForMultipleChoice(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =input_ids.unsqueeze(1					).expand(-1					,  self.num_choices					,  -1					).contiguous()
						__a        =token_type_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					,  token_type_ids=__snake_case					,  labels=__snake_case					,  )
						self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_choices)					)





			def 		__magic_name__    (		self					)   -> int:




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






@require_torch
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False

			SCREAMING_SNAKE_CASE							   =						()





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =MraModelTester(self					)
						__a        =ConfigTester(self					,  config_class=__snake_case					,  hidden_size=37					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						self.config_tester.run_common_tests()





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									__a        =type
									self.model_tester.create_and_check_model(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__snake_case					)





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__a        =MraModel.from_pretrained(__snake_case					)
									self.assertIsNotNone(__snake_case					)





			@unittest.skip(reason='MRA does not output attentions'					)
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						return






@require_torch
class 	__magic_name__    (			unittest.TestCase      ):





			@slow
			def 		__magic_name__    (		self					)   -> List[Any]:




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

						with torch.no_grad():
									__a        =model(__snake_case					)[0]

						__a        =torch.Size((1, 256, 768)					)
						self.assertEqual(output.shape					,  __snake_case					)

						__a        =torch.tensor(
						    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]					)

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





			@slow
			def 		__magic_name__    (		self					)   -> Any:




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

						with torch.no_grad():
									__a        =model(__snake_case					)[0]

						__a        =5_0265

						__a        =torch.Size((1, 256, vocab_size)					)
						self.assertEqual(output.shape					,  __snake_case					)

						__a        =torch.tensor(
						    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]					)

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





			@slow
			def 		__magic_name__    (		self					)   -> str:




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

						with torch.no_grad():
									__a        =model(__snake_case					)[0]

						__a        =5_0265

						__a        =torch.Size((1, 4096, vocab_size)					)
						self.assertEqual(output.shape					,  __snake_case					)

						__a        =torch.tensor(
						    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]					)

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












"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()