




'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							__snake_case						 =   numpy.random.rand(
							    self.input_array.shape[1]       ,		4      )

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							__snake_case						 =   numpy.random.rand(3       ,		1      )

							# Real output values provided.
							__snake_case						 =   output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							__snake_case						 =   numpy.zeros(output_array.shape      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   sigmoid(
							    numpy.dot(self.input_array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return self.layer_between_second_hidden_layer_and_output




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.input_array.T       ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		self.first_hidden_layer_and_second_hidden_layer_weights.T       ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer      )       ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

							__snake_case						 =   sigmoid(
							    numpy.dot(self.array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return int(self.layer_between_second_hidden_layer_and_output > 0.6      )





def 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )    ,	dtype=numpy.floataa    ,	)

						# True output values for the given input values.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=a    ,	iterations=10    ,	give_loss=a							)

						return neural_network.predict(numpy.array(([1, 1, 1])    ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
	example()







'''simple docstring'''


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
	    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    OpenAIGPTConfig,
	    OpenAIGPTDoubleHeadsModel,
	    OpenAIGPTForSequenceClassification,
	    OpenAIGPTLMHeadModel,
	    OpenAIGPTModel,
	)
class 						a_				:




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]=1_3       ,		__lowerCAmelCase     :  Tuple=7       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Union[str, Any]=9_9       ,		__lowerCAmelCase     :  Any=3_2       ,		__lowerCAmelCase     :  Tuple=5       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  List[str]=3_7       ,		__lowerCAmelCase     :  List[Any]="gelu"       ,		__lowerCAmelCase     :  Union[str, Any]=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Any=5_1_2       ,		__lowerCAmelCase     :  Dict=1_6       ,		__lowerCAmelCase     :  Dict=2       ,		__lowerCAmelCase     :  Union[str, Any]=0.02       ,		__lowerCAmelCase     :  Optional[Any]=3       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  List[str]=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   scope
							__snake_case						 =   self.vocab_size - 1




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

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

							__snake_case						 =   None
							__snake_case						 =   None
							__snake_case						 =   None
							if self.use_labels:
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels      )
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.num_choices      )

							__snake_case						 =   OpenAIGPTConfig(
							    vocab_size=self.vocab_size       ,		n_embd=self.hidden_size       ,		n_layer=self.num_hidden_layers       ,		n_head=self.num_attention_heads       ,		n_positions=self.max_position_embeddings       ,		pad_token_id=self.pad_token_id       ,		)

							__snake_case						 =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,		2      )

							return (
							    config,
							    input_ids,
							    head_mask,
							    token_type_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		*__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   OpenAIGPTModel(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		head_mask=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase      )

							self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)      )




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		*__lowerCAmelCase     :  str      ):
							__snake_case						 =   OpenAIGPTLMHeadModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.loss.shape       ,		()      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		*__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   OpenAIGPTDoubleHeadsModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.loss.shape       ,		()      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int       ,		*__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   OpenAIGPTForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict





@require_torch
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[str]			  =      (
	    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     Union[str, Any]			  =      (
	    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
	lowercase_       :     str			  =      (
	    {
	        '''feature-extraction''': OpenAIGPTModel,
	        '''text-classification''': OpenAIGPTForSequenceClassification,
	        '''text-generation''': OpenAIGPTLMHeadModel,
	        '''zero-shot''': OpenAIGPTForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Any      ):
							if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
													# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
													# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
													# tiny config could not be created.
													return True

							return False




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]=False      ):
							__snake_case						 =   super()._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase       ,		return_labels=__lowerCAmelCase      )

							if return_labels:
													if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   torch.zeros(
																			    self.model_tester.batch_size       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )
							return inputs_dict




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   OpenAIGPTModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		n_embd=3_7      )




	def   lowercase__   (       self     :  int      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_double_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  str      ):
							for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   OpenAIGPTModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )





@require_torch
class 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'      )
							model.to(__lowerCAmelCase      )
							__snake_case						 =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )  # the president is
							__snake_case						 =   [
							    4_8_1,
							    4_7_3_5,
							    5_4_4,
							    2_4_6,
							    9_6_3,
							    8_7_0,
							    7_6_2,
							    2_3_9,
							    2_4_4,
							    4_0_4_7_7,
							    2_4_4,
							    2_4_9,
							    7_1_9,
							    8_8_1,
							    4_8_7,
							    5_4_4,
							    2_4_0,
							    2_4_4,
							    6_0_3,
							    4_8_1,
							]  # the president is a very good man. " \n " i\'m sure he is, " said the

							__snake_case						 =   model.generate(__lowerCAmelCase       ,		do_sample=__lowerCAmelCase      )
							self.assertListEqual(output_ids[0].tolist()       ,		__lowerCAmelCase      )

