


'''simple docstring'''


import math
import random






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = False   ):
		if deriv:
				return value * (1 - value)
		return 1 / (1 + math.exp(-value   ))


# Initial Value
lowerCamelCase__    	=     0.02






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Union[str, Any]								=      float(2 * (random.randint(1      , 100   )) - 1   )

		for _ in range(__lowerCAmelCase   ):
				# Forward propagation
				_UpperCAmelCase		:       int								=      sigmoid_function(INITIAL_VALUE * weight   )
				# How much did we miss?
				_UpperCAmelCase		:       int								=      (expected / 100) - layer_a
				# Error delta
				_UpperCAmelCase		:       Union[str, Any]								=      layer_1_error * sigmoid_function(__lowerCAmelCase      , __lowerCAmelCase   )
				# Update weight
				weight += INITIAL_VALUE * layer_1_delta

		return layer_a * 100


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						lowerCamelCase__    	=     int(input('Expected value: '))
						lowerCamelCase__    	=     int(input('Number of propagations: '))
						print(forward_propagation(expected, number_propagations))







'''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	):




			def __init__(		self  :     int	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str=13	,  lowerCamelCase__  :     Dict=7	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     List[str]=True	,  lowerCamelCase__  :     Dict=True	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Tuple=99	,  lowerCamelCase__  :     Optional[int]=32	,  lowerCamelCase__  :     str=5	,  lowerCamelCase__  :     List[Any]=4	,  lowerCamelCase__  :     Any=37	,  lowerCamelCase__  :     List[Any]="gelu"	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Tuple=0.1	,  lowerCamelCase__  :     Optional[int]=5_12	,  lowerCamelCase__  :     Any=16	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     Optional[Any]=0.0_2	,  lowerCamelCase__  :     Optional[int]=4	,  )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      parent
					_UpperCAmelCase		:       Optional[int]								=      batch_size
					_UpperCAmelCase		:       List[Any]								=      seq_length
					_UpperCAmelCase		:       Dict								=      is_training
					_UpperCAmelCase		:       int								=      use_attention_mask
					_UpperCAmelCase		:       List[Any]								=      use_token_type_ids
					_UpperCAmelCase		:       int								=      use_labels
					_UpperCAmelCase		:       str								=      vocab_size
					_UpperCAmelCase		:       Tuple								=      hidden_size
					_UpperCAmelCase		:       Dict								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       List[Any]								=      hidden_act
					_UpperCAmelCase		:       Union[str, Any]								=      hidden_dropout_prob
					_UpperCAmelCase		:       List[str]								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       Optional[int]								=      max_position_embeddings
					_UpperCAmelCase		:       Tuple								=      type_vocab_size
					_UpperCAmelCase		:       int								=      type_sequence_label_size
					_UpperCAmelCase		:       List[str]								=      initializer_range
					_UpperCAmelCase		:       Union[str, Any]								=      num_choices




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

					_UpperCAmelCase		:       Any								=      None
					if self.use_attention_mask:
							_UpperCAmelCase		:       Optional[Any]								=      random_attention_mask([self.batch_size, self.seq_length] )

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

					_UpperCAmelCase		:       Tuple								=      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 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[int]:



					'''simple docstring'''







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






			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      config_and_inputs

					_UpperCAmelCase		:       List[Any]								=      True
					_UpperCAmelCase		:       List[str]								=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					_UpperCAmelCase		:       int								=      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__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     True

			lowerCAmelCase :	Tuple          =     (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      FlaxRobertaPreLayerNormModelTester(self )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->int:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:       Any								=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      model(np.ones((1, 1) ) )
							self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@slow
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       str								=      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		:       Tuple								=      model(lowerCamelCase__ )[0]
					_UpperCAmelCase		:       int								=      [1, 11, 5_02_65]
					self.assertEqual(list(output.shape )	,  lowerCamelCase__ )
					# compare the actual values for a slice.
					_UpperCAmelCase		:       int								=      np.array(
					    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )






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



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      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		:       Optional[Any]								=      model(lowerCamelCase__ )[0]
					# compare the actual values for a slice.
					_UpperCAmelCase		:       str								=      np.array(
					    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )



