

"""simple docstring"""





import unittest

from transformers import MPNetConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MPNetForMaskedLM,
       MPNetForMultipleChoice,
       MPNetForQuestionAnswering,
       MPNetForSequenceClassification,
       MPNetForTokenClassification,
       MPNetModel,
   )




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[Any]=1_3,					UpperCAmelCase__							:			Union[str, Any]=7,					UpperCAmelCase__							:			Tuple=True,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			Optional[int]=False,					UpperCAmelCase__							:			Union[str, Any]=True,					UpperCAmelCase__							:			Tuple=9_9,					UpperCAmelCase__							:			Optional[Any]=6_4,					UpperCAmelCase__							:			str=5,					UpperCAmelCase__							:			Optional[Any]=4,					UpperCAmelCase__							:			List[str]=6_4,					UpperCAmelCase__							:			Any="gelu",					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			Tuple=0.1,					UpperCAmelCase__							:			Optional[Any]=5_1_2,					UpperCAmelCase__							:			Union[str, Any]=1_6,					UpperCAmelCase__							:			List[Any]=2,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			str=3,					UpperCAmelCase__							:			List[str]=4,					UpperCAmelCase__							:			Dict=None,					):
    __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 					_lowercase (			self							:			List[str]	):
    return MPNetConfig.from_pretrained("microsoft/mpnet-base"	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __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
    __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, input_mask, sequence_labels, token_labels, choice_labels


 def 					_lowercase (			self							:			Dict	):
    return MPNetConfig(
        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,					initializer_range=self.initializer_range,					)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						MPNetModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)
    self.parent.assertEqual(result.pooler_output.shape,					(self.batch_size, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			str	):
    __lowercase								=						MPNetForQuestionAnswering(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=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							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.num_labels
    __lowercase								=						MPNetForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.num_choices
    __lowercase								=						MPNetForMultipleChoice(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						input_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__,					labels=UpperCAmelCase__,					)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						self.num_labels
    __lowercase								=						MPNetForTokenClassification(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)



 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.prepare_config_and_inputs()
    ((__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase))								=						config_and_inputs
    __lowercase								=						{"input_ids": input_ids, "attention_mask": input_mask}
    return config, inputs_dict







@require_torch
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	(
     (
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
         MPNetForSequenceClassification,
         MPNetForTokenClassification,
         MPNetModel,
     )
     if is_torch_available()
     else ()
 )
 __UpperCAmelCase	:					Optional[int]   		=	(
     {
         "feature-extraction": MPNetModel,
         "fill-mask": MPNetForMaskedLM,
         "question-answering": MPNetForQuestionAnswering,
         "text-classification": MPNetForSequenceClassification,
         "token-classification": MPNetForTokenClassification,
         "zero-shot": MPNetForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __UpperCAmelCase	:					Any   		=	False
 __UpperCAmelCase	:					List[Any]   		=	True


 def 					_lowercase (			self							:			int	):
    __lowercase								=						MPNetModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Dict	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase__	)



 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase__	)







@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						MPNetModel.from_pretrained("microsoft/mpnet-base"	)
    __lowercase								=						torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
    __lowercase								=						model(UpperCAmelCase__	)[0]
    __lowercase								=						torch.Size((1, 1_1, 7_6_8)	)
    self.assertEqual(output.shape,					UpperCAmelCase__	)
    __lowercase								=						torch.tensor(
        [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]	)
    # compare the actual values for a slice.
    self.assertTrue(torch.allclose(output[:, :3, :3],					UpperCAmelCase__,					atol=1E-4	)	)












"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







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



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







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


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [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]]))




