

'''simple docstring'''


import numpy



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Optional[Any]       	=					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.
								UpperCAmelCase_		:      Tuple       	=					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.
								UpperCAmelCase_		:      List[str]       	=					numpy.random.rand(
								    4    ,			3)

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

								# Real output values provided.
								UpperCAmelCase_		:      str       	=					output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase_		:      Union[str, Any]       	=					numpy.zeros(output_array.shape)


							def 						_snake_case							(			self)							->				numpy.ndarray:
								UpperCAmelCase_		:      Any       	=					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.
								UpperCAmelCase_		:      Tuple       	=					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.
								UpperCAmelCase_		:      int       	=					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 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      Optional[int]       	=					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)    ,			)
								UpperCAmelCase_		:      Any       	=					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)    ,			)
								UpperCAmelCase_		:      Tuple       	=					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 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				None:
								for iteration in range(1    ,			iterations + 1):
									UpperCAmelCase_		:      int       	=					self.feedforward()
									self.back_propagation()
									if give_loss:
										UpperCAmelCase_		:      List[Any]       	=					numpy.mean(numpy.square(output - self.feedforward()))
										print(F"""Iteration {iteration} Loss: {loss}""")





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					input_arr

								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(self.array    ,			self.input_layer_and_first_hidden_layer_weights))

								UpperCAmelCase_		:      Optional[int]       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								UpperCAmelCase_		:      int       	=					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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return 1 / (1 + numpy.exp(-value  ))







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return (value) * (1 - (value))







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					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.
	UpperCAmelCase_		:      Dict       	=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,dtype=numpy.floataa  )

	# Calling neural network class.
	UpperCAmelCase_		:      List[str]       	=					TwoHiddenLayerNeuralNetwork(
	    input_array=UpperCamelCase   ,output_array=UpperCamelCase  )

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=UpperCamelCase   ,iterations=1_0   ,give_loss=UpperCamelCase  )

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


if __name__ == "__main__":
						example()









'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=99    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=512    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=0.02    ,			_snake_case=3    ,			_snake_case=4    ,			_snake_case=None    ,			)							->				str:
								UpperCAmelCase_		:      Dict       	=					parent
								UpperCAmelCase_		:      Tuple       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					seq_length
								UpperCAmelCase_		:      Dict       	=					is_training
								UpperCAmelCase_		:      Tuple       	=					use_input_mask
								UpperCAmelCase_		:      List[Any]       	=					use_token_type_ids
								UpperCAmelCase_		:      Optional[int]       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      Any       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      Optional[int]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Any       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      int       	=					type_sequence_label_size
								UpperCAmelCase_		:      List[str]       	=					initializer_range
								UpperCAmelCase_		:      Any       	=					num_labels
								UpperCAmelCase_		:      Optional[Any]       	=					num_choices
								UpperCAmelCase_		:      str       	=					scope


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

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

								UpperCAmelCase_		:      Union[str, Any]       	=					None
								if self.use_token_type_ids:
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size)

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

								UpperCAmelCase_		:      Tuple       	=					self.get_config()

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


							def 						_snake_case							(			self)							->				Optional[Any]:
								return OpenLlamaConfig(
								    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=_snake_case    ,			initializer_range=self.initializer_range    ,			use_stable_embedding=_snake_case    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					OpenLlamaModel(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Dict       	=					model(_snake_case    ,			attention_mask=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				int:
								UpperCAmelCase_		:      Union[str, Any]       	=					True
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaModel(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			)
								UpperCAmelCase_		:      Any       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Any:
								UpperCAmelCase_		:      Optional[int]       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					True
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      int       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()

								# first forward pass
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			use_cache=_snake_case    ,			)
								UpperCAmelCase_		:      Dict       	=					outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase_		:      str       	=					ids_tensor((self.batch_size, 3)    ,			config.vocab_size)
								UpperCAmelCase_		:      int       	=					ids_tensor((self.batch_size, 3)    ,			vocab_size=2)

								# append to next input_ids and
								UpperCAmelCase_		:      Tuple       	=					torch.cat([input_ids, next_tokens]    ,			dim=-1)
								UpperCAmelCase_		:      List[str]       	=					torch.cat([input_mask, next_mask]    ,			dim=-1)

								UpperCAmelCase_		:      Optional[Any]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]

								# select random slice
								UpperCAmelCase_		:      Optional[Any]       	=					ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
								UpperCAmelCase_		:      Optional[int]       	=					output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase_		:      Union[str, Any]       	=					output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-3))





							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    
								)		:      Union[str, Any]       	=					config_and_inputs
								UpperCAmelCase_		:      List[str]       	=					{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class 					lowercase						(  a_,					a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Dict=   (
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							_lowerCamelCase		:      str=   (OpenLlamaForCausalLM,) if is_torch_available() else ()
							_lowerCamelCase		:      Optional[int]=   (
							    {
							        "feature-extraction": OpenLlamaModel,
							        "text-classification": OpenLlamaForSequenceClassification,
							        "text-generation": OpenLlamaForCausalLM,
							        "zero-shot": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase		:      Tuple=   False
							_lowerCamelCase		:      int=   False


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Any       	=					OpenLlamaModelTester(self)
								UpperCAmelCase_		:      Union[str, Any]       	=					ConfigTester(self    ,			config_class=_snake_case    ,			hidden_size=37)


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


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_		:      List[Any]       	=					type
									self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[str]       	=					3
								UpperCAmelCase_		:      List[str]       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Optional[Any]       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Tuple       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      Optional[Any]       	=					3
								UpperCAmelCase_		:      int       	=					'single_label_classification'
								UpperCAmelCase_		:      int       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Any       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      int       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      int       	=					3
								UpperCAmelCase_		:      Any       	=					'multi_label_classification'
								UpperCAmelCase_		:      Tuple       	=					input_dict['input_ids']
								UpperCAmelCase_		:      str       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]    ,			self.model_tester.type_sequence_label_size).to(torch.float)
								UpperCAmelCase_		:      Optional[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[Any]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass





							@parameterized.expand([('linear',), ('dynamic',)])
							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[Any]       	=					ids_tensor([1, 10]    ,			config.vocab_size)
								UpperCAmelCase_		:      List[str]       	=					ids_tensor([1, int(config.max_position_embeddings * 1.5)]    ,			config.vocab_size)

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      int       	=					OpenLlamaModel(_snake_case)
								original_model.to(_snake_case)
								original_model.eval()
								UpperCAmelCase_		:      Optional[Any]       	=					original_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Tuple       	=					original_model(_snake_case).last_hidden_state

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      Dict       	=					{'type': scaling_type, 'factor': 10.0}
								UpperCAmelCase_		:      Union[str, Any]       	=					OpenLlamaModel(_snake_case)
								scaled_model.to(_snake_case)
								scaled_model.eval()
								UpperCAmelCase_		:      Dict       	=					scaled_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Optional[int]       	=					scaled_model(_snake_case).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
									self.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))
								else:
									self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))






