




'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
    )

class   lowerCAmelCase_      (       unittest.TestCase      ):







  '''simple docstring'''







  def __init__(  self			:		Tuple  ,						_UpperCAmelCase			:		Any  ,						_UpperCAmelCase			:		List[str]=13  ,						_UpperCAmelCase			:		str=7  ,						_UpperCAmelCase			:		str=True  ,						_UpperCAmelCase			:		int=True  ,						_UpperCAmelCase			:		Union[str, Any]=True  ,						_UpperCAmelCase			:		Optional[int]=True  ,						_UpperCAmelCase			:		Any=99  ,						_UpperCAmelCase			:		Optional[Any]=32  ,						_UpperCAmelCase			:		Optional[int]=5  ,						_UpperCAmelCase			:		int=4  ,						_UpperCAmelCase			:		Optional[int]=37  ,						_UpperCAmelCase			:		Optional[Any]="gelu"  ,						_UpperCAmelCase			:		Dict=0.1  ,						_UpperCAmelCase			:		str=0.1  ,						_UpperCAmelCase			:		int=5_12  ,						_UpperCAmelCase			:		int=16  ,						_UpperCAmelCase			:		Optional[Any]=2  ,						_UpperCAmelCase			:		Union[str, Any]=0.02  ,						_UpperCAmelCase			:		Union[str, Any]=4  ,						):






   """simple docstring"""






   UpperCAmelCase__								=			parent
   UpperCAmelCase__								=			batch_size
   UpperCAmelCase__								=			seq_length
   UpperCAmelCase__								=			is_training
   UpperCAmelCase__								=			use_attention_mask
   UpperCAmelCase__								=			use_token_type_ids
   UpperCAmelCase__								=			use_labels
   UpperCAmelCase__								=			vocab_size
   UpperCAmelCase__								=			hidden_size
   UpperCAmelCase__								=			num_hidden_layers
   UpperCAmelCase__								=			num_attention_heads
   UpperCAmelCase__								=			intermediate_size
   UpperCAmelCase__								=			hidden_act
   UpperCAmelCase__								=			hidden_dropout_prob
   UpperCAmelCase__								=			attention_probs_dropout_prob
   UpperCAmelCase__								=			max_position_embeddings
   UpperCAmelCase__								=			type_vocab_size
   UpperCAmelCase__								=			type_sequence_label_size
   UpperCAmelCase__								=			initializer_range
   UpperCAmelCase__								=			num_choices


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Dict       ):






   """simple docstring"""






   UpperCAmelCase__								=			ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size       )

   UpperCAmelCase__								=			None
   if self.use_attention_mask:
    UpperCAmelCase__								=			random_attention_mask([self.batch_size, self.seq_length]       )

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

   UpperCAmelCase__								=			RobertaPreLayerNormConfig(
       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=_lowerCAmelCase  ,						initializer_range=self.initializer_range  ,						)

   return config, input_ids, token_type_ids, attention_mask


  def 							SCREAMING_SNAKE_CASE__	(  self			:		int       ):






   """simple docstring"""






   UpperCAmelCase__								=			self.prepare_config_and_inputs()
   UpperCAmelCase__								=			config_and_inputs
   UpperCAmelCase__								=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
   return config, inputs_dict



  def 							SCREAMING_SNAKE_CASE__	(  self			:		int       ):






   """simple docstring"""






   UpperCAmelCase__								=			self.prepare_config_and_inputs()
   UpperCAmelCase__								=			config_and_inputs

   UpperCAmelCase__								=			True
   UpperCAmelCase__								=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
   UpperCAmelCase__								=			ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2       )

   return (
       config,
       input_ids,
       token_type_ids,
       encoder_hidden_states,
       encoder_attention_mask,
   )

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class   lowerCAmelCase_      (       lowerCamelCase_	,     unittest.TestCase      ):







  '''simple docstring'''







  lowerCAmelCase_ :    List[str]    =  True

  lowerCAmelCase_ :    str    =  (
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Dict       ):






   """simple docstring"""






   UpperCAmelCase__								=			FlaxRobertaPreLayerNormModelTester(self       )



  @slow
  def 							SCREAMING_SNAKE_CASE__	(  self			:		List[str]       ):






   """simple docstring"""






   for model_class_name in self.all_model_classes:
    UpperCAmelCase__								=			model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,						from_pt=_lowerCAmelCase       )
    UpperCAmelCase__								=			model(np.ones((1, 1)       )       )
    self.assertIsNotNone(_lowerCAmelCase       )

@require_flax
class   lowerCAmelCase_      (       unittest.TestCase      ):







  '''simple docstring'''







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






   """simple docstring"""






   UpperCAmelCase__								=			FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,						from_pt=_lowerCAmelCase       )

   UpperCAmelCase__								=			np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  ,						dtype=jnp.intaa       )
   UpperCAmelCase__								=			model(_lowerCAmelCase       )[0]
   UpperCAmelCase__								=			[1, 11, 5_02_65]
   self.assertEqual(list(output.shape       )  ,						_lowerCAmelCase       )
   # compare the actual values for a slice.
   UpperCAmelCase__								=			np.array(
       [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]  ,						dtype=np.floataa       )
   self.assertTrue(np.allclose(output[:, :3, :3]  ,						_lowerCAmelCase  ,						atol=1E-4       )       )



  @slow
  def 							SCREAMING_SNAKE_CASE__	(  self			:		Tuple       ):






   """simple docstring"""






   UpperCAmelCase__								=			FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,						from_pt=_lowerCAmelCase       )

   UpperCAmelCase__								=			np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  ,						dtype=jnp.intaa       )
   UpperCAmelCase__								=			model(_lowerCAmelCase       )[0]
   # compare the actual values for a slice.
   UpperCAmelCase__								=			np.array(
       [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]  ,						dtype=np.floataa       )
   self.assertTrue(np.allclose(output[:, :3, :3]  ,						_lowerCAmelCase  ,						atol=1E-4       )       )






import numpy

# List of input, output pairs
_UpperCAmelCase		:  List[str]	=   (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_UpperCAmelCase		:  Optional[Any]	=   (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase		:  Tuple	=   [2, 4, 1, 5]
_UpperCAmelCase		:  Union[str, Any]	=   len(train_data)
_UpperCAmelCase		:  Dict	=   0.0_0_9



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase="train"      ):
  return calculate_hypothesis_value(lowerCamelCase,	lowerCamelCase      ) - output(
      lowerCamelCase,	lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:str							=  0
  for i in range(len(lowerCamelCase      ) - 1      ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None



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



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:int							=  summation_of_cost_derivative(lowerCamelCase,	lowerCamelCase      ) / m
  return cost_derivative_value



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



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


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



