


import unittest

from transformers import DebertaConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class 					lowerCAmelCase_ (       _a):




						def __init__(	self						:   List[Any]   ,     __A						:   Optional[Any]   ,     __A						:   List[Any]=13   ,     __A						:   str=7   ,     __A						:   List[str]=True   ,     __A						:   Dict=True   ,     __A						:   Optional[Any]=True   ,     __A						:   List[str]=True   ,     __A						:   Optional[int]=99   ,     __A						:   Any=32   ,     __A						:   Tuple=5   ,     __A						:   List[Any]=4   ,     __A						:   List[Any]=37   ,     __A						:   Dict="gelu"   ,     __A						:   Dict=0.1   ,     __A						:   Dict=0.1   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=16   ,     __A						:   Optional[int]=2   ,     __A						:   List[str]=0.02   ,     __A						:   Union[str, Any]=False   ,     __A						:   str=True   ,     __A						:   int="None"   ,     __A						:   Union[str, Any]=3   ,     __A						:   Dict=4   ,     __A						:   List[str]=None   ,     )      ->Dict:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Tuple						)      ->int:






									"""simple docstring"""






									a__ :int						  =    ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size						)

									a__ :Union[str, Any]						  =    None
									if self.use_input_mask:
												a__ :List[Any]						  =    ids_tensor([self.batch_size, self.seq_length]   ,     vocab_size=2						)

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

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

									a__ :Any						  =    self.get_config()

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




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






									"""simple docstring"""






									return DebertaConfig(
									    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   ,     initializer_range=self.initializer_range   ,     relative_attention=self.relative_attention   ,     position_biased_input=self.position_biased_input   ,     pos_att_type=self.pos_att_type   ,     )




						def  _snake_case					(	self						:   List[str]						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    self.get_config()
									a__ :Tuple						  =    300
									return config




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, Any]						)      ->List[Any]:






									"""simple docstring"""






									self.parent.assertListEqual(list(result.loss.size()						)   ,     []						)




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Union[str, Any]   ,     __A						:   Any   ,     __A						:   Dict   ,     __A						:   Optional[Any]   ,     __A						:   Optional[int]   ,     __A						:   Dict   ,     __A						:   Tuple						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    DebertaModel(config=__A						)
									model.to(__A						)
									model.eval()
									a__ :Optional[int]						  =    model(__A   ,     attention_mask=__A   ,     token_type_ids=__A						)[0]
									a__ :List[Any]						  =    model(__A   ,     token_type_ids=__A						)[0]
									a__ :str						  =    model(__A						)[0]

									self.parent.assertListEqual(list(sequence_output.size()						)   ,     [self.batch_size, self.seq_length, self.hidden_size]						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   List[str]   ,     __A						:   str   ,     __A						:   List[Any]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[int]   ,     __A						:   List[str]   ,     __A						:   Dict						)      ->str:






									"""simple docstring"""






									a__ :Any						  =    DebertaForMaskedLM(config=__A						)
									model.to(__A						)
									model.eval()
									a__ :Optional[int]						  =    model(__A   ,     attention_mask=__A   ,     token_type_ids=__A   ,     labels=__A						)

									self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length, self.vocab_size)						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Optional[int]   ,     __A						:   Optional[int]   ,     __A						:   List[str]   ,     __A						:   Optional[int]   ,     __A						:   str   ,     __A						:   Dict   ,     __A						:   int						)      ->List[str]:






									"""simple docstring"""






									a__ :int						  =    self.num_labels
									a__ :Union[str, Any]						  =    DebertaForSequenceClassification(__A						)
									model.to(__A						)
									model.eval()
									a__ :Any						  =    model(__A   ,     attention_mask=__A   ,     token_type_ids=__A   ,     labels=__A						)
									self.parent.assertListEqual(list(result.logits.size()						)   ,     [self.batch_size, self.num_labels]						)
									self.check_loss_output(__A						)




						def  _snake_case					(	self						:   List[Any]   ,     __A						:   Tuple   ,     __A						:   Optional[int]   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any]   ,     __A						:   Any   ,     __A						:   Any						)      ->Dict:






									"""simple docstring"""






									a__ :str						  =    self.num_labels
									a__ :Optional[int]						  =    DebertaForTokenClassification(config=__A						)
									model.to(__A						)
									model.eval()
									a__ :Any						  =    model(__A   ,     attention_mask=__A   ,     token_type_ids=__A   ,     labels=__A						)
									self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length, self.num_labels)						)




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Union[str, Any]   ,     __A						:   str   ,     __A						:   Optional[Any]   ,     __A						:   str   ,     __A						:   List[Any]   ,     __A						:   Any   ,     __A						:   Optional[int]						)      ->str:






									"""simple docstring"""






									a__ :Tuple						  =    DebertaForQuestionAnswering(config=__A						)
									model.to(__A						)
									model.eval()
									a__ :List[Any]						  =    model(
									    __A   ,     attention_mask=__A   ,     token_type_ids=__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						:   Tuple						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.prepare_config_and_inputs()
									(
									    (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  
									) :List[Any]						  =    config_and_inputs
									a__ :Union[str, Any]						  =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict






@require_torch
class 					lowerCAmelCase_ (       _a			,_a			,unittest.TestCase):
						lowerCamelCase_         =     (
						    (
						        DebertaModel,
						        DebertaForMaskedLM,
						        DebertaForSequenceClassification,
						        DebertaForTokenClassification,
						        DebertaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCamelCase_         =     (
						    {
						        'feature-extraction': DebertaModel,
						        'fill-mask': DebertaForMaskedLM,
						        'question-answering': DebertaForQuestionAnswering,
						        'text-classification': DebertaForSequenceClassification,
						        'token-classification': DebertaForTokenClassification,
						        'zero-shot': DebertaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCamelCase_         =     True
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False




						def  _snake_case					(	self						:   str						)      ->str:






									"""simple docstring"""






									a__ :int						  =    DebertaModelTester(self						)
									a__ :List[Any]						  =    ConfigTester(self   ,     config_class=__A   ,     hidden_size=37						)




						def  _snake_case					(	self						:   int						)      ->Optional[Any]:






									"""simple docstring"""






									self.config_tester.run_common_tests()




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






									"""simple docstring"""






									a__ :List[str]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_model(*__A						)




						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									a__ :Any						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_sequence_classification(*__A						)




						def  _snake_case					(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									a__ :List[Any]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_masked_lm(*__A						)




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :int						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_question_answering(*__A						)




						def  _snake_case					(	self						:   Tuple						)      ->Any:






									"""simple docstring"""






									a__ :Any						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_token_classification(*__A						)






						@slow
						def  _snake_case					(	self						:   List[str]						)      ->Optional[int]:






									"""simple docstring"""






									for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a__ :int						  =    DebertaModel.from_pretrained(__A						)
												self.assertIsNotNone(__A						)






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




						@unittest.skip(reason="Model not available yet"						)
						def  _snake_case					(	self						:   List[str]						)      ->Dict:






									"""simple docstring"""






									pass






						@slow
						def  _snake_case					(	self						:   Any						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    DebertaModel.from_pretrained("microsoft/deberta-base"						)

									a__ :List[Any]						  =    torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
									a__ :Optional[int]						  =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
									with torch.no_grad():
												a__ :Tuple						  =    model(__A   ,     attention_mask=__A						)[0]
									# compare the actual values for a slice.
									a__ :Optional[int]						  =    torch.tensor(
									    [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]						)
									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,     __A   ,     atol=1E-4						)   ,     F'''{output[:, 1:4, 1:4]}'''						)






def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list[list]:





			"""simple docstring"""
			a__ :Any						  =    current_set.copy()
			for row_index, row in enumerate(a				):
						a__ :List[str]						  =    row[0]
						for column_index, column in enumerate(a				):
									if magnitude == 0:
												a__ :Tuple						  =    column
												continue
									a__ :Tuple						  =    column / magnitude
    # Subtract to cancel term
			a__ :Any						  =    current_set[0]
			a__ :Dict						  =    [first_row]
			a__ :Dict						  =    current_set[1::]
			for row in current_set:
						a__ :int						  =    []
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(a				)
									continue
						for column_index in range(len(a				)				):
									temp_row.append(first_row[column_index] - row[column_index]				)
						final_set.append(a				)
			# Create next recursion iteration set
			if len(final_set[0]				) != 3:
						a__ :List[str]						  =    final_set[0]
						a__ :Optional[Any]						  =    []
						a__ :List[Any]						  =    []
						for row in final_set[1::]:
									current_first_column.append(row[0]				)
									next_iteration.append(row[1::]				)
						a__ :Dict						  =    simplify(a				)
						for i in range(len(a				)				):
									resultant[i].insert(0	,  current_first_column[i]				)
						resultant.insert(0	,  a				)
						a__ :Optional[int]						  =    resultant
			return final_set


def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list:





			"""simple docstring"""
			if len(a				) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			a__ :Any						  =    len(a				) + 1
			if any(len(a				) != _length for item in equations				):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			for row in equations:
						if any(not isinstance(a	,  (int, float)				) for column in row				):
									raise ValueError("solve_simultaneous() requires lists of integers"				)
			if len(a				) == 1:
						return [equations[0][-1] / equations[0][0]]
			a__ :Union[str, Any]						  =    equations.copy()
			if any(0 in row for row in data_set				):
						a__ :int						  =    data_set.copy()
						a__ :Dict						  =    []
						for row_index, row in enumerate(a				):
									if 0 not in row:
												a__ :Union[str, Any]						  =    data_set.pop(a				)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
						data_set.insert(0	,  a				)
			a__ :Optional[int]						  =    data_set.copy()
			a__ :Any						  =    simplify(a				)
			a__ :str						  =    simplified[::-1]
			a__ :list						  =    []
			for row in simplified:
						a__ :Optional[int]						  =    row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0				)
												continue
									solutions.append(current_solution / row[-2]				)
									continue
						a__ :Dict						  =    row.copy()[: len(a				) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0				)
						if len(a				) == 0:
									solutions.append(0				)
									continue
						a__ :List[Any]						  =    temp_row[1::]
						a__ :Union[str, Any]						  =    temp_row[::-1]
						for column_index, column in enumerate(a				):
									current_solution -= column * solutions[column_index]
						solutions.append(a				)
			a__ :List[str]						  =    []
			for item in solutions:
						final.append(float(round(a	,  5				)				)				)
			return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		snake_case__			 =						[
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))


