


'''simple docstring'''



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 						_snake_case   (       a__    ,				a__    ,				a__		):
					lowerCAmelCase :Dict						=   [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']




					@register_to_config
					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = 5_0257							,							_lowerCamelCase = 1024							,							_lowerCamelCase = 768							,							_lowerCamelCase = 12							,							_lowerCamelCase = 12							,							_lowerCamelCase = None							,							_lowerCamelCase = "gelu_new"							,							_lowerCamelCase = 0.1							,							_lowerCamelCase = 0.1							,							_lowerCamelCase = 0.1							,							_lowerCamelCase = 1e-5							,							_lowerCamelCase = 0.02							,							_lowerCamelCase = True							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							):
								super().__init__()

								UpperCAmelCase__					:   str       				=       prefix_length

								if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
											raise ValueError(
											    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
											    f''' `n_embd`: {n_embd} are not equal.''')

								UpperCAmelCase__					:   int       				=       prefix_inner_dim
								UpperCAmelCase__					:   Optional[Any]       				=       prefix_hidden_dim

								UpperCAmelCase__					:   int       				=       (
								    nn.Linear(self.prefix_inner_dim							,							self.prefix_hidden_dim)
								    if self.prefix_hidden_dim is not None
								    else nn.Identity()
								)
								UpperCAmelCase__					:   Tuple       				=       (
								    nn.Linear(self.prefix_hidden_dim							,							_lowerCamelCase) if self.prefix_hidden_dim is not None else nn.Identity()
								)

								UpperCAmelCase__					:   Optional[Any]       				=       GPTaConfig(
								    vocab_size=_lowerCamelCase							,							n_positions=_lowerCamelCase							,							n_embd=_lowerCamelCase							,							n_layer=_lowerCamelCase							,							n_head=_lowerCamelCase							,							n_inner=_lowerCamelCase							,							activation_function=_lowerCamelCase							,							resid_pdrop=_lowerCamelCase							,							embd_pdrop=_lowerCamelCase							,							attn_pdrop=_lowerCamelCase							,							layer_norm_epsilon=_lowerCamelCase							,							initializer_range=_lowerCamelCase							,							scale_attn_weights=_lowerCamelCase							,							use_cache=_lowerCamelCase							,							scale_attn_by_inverse_layer_idx=_lowerCamelCase							,							reorder_and_upcast_attn=_lowerCamelCase							,							)
								UpperCAmelCase__					:   int       				=       GPTaLMHeadModel(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.transformer.transformer.wte(_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       self.encode_prefix(_lowerCamelCase)
								UpperCAmelCase__					:   List[str]       				=       self.decode_prefix(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       torch.cat((prefix_embeds, embedding_text)							,							dim=1)

								if labels is not None:
											UpperCAmelCase__					:   List[Any]       				=       self.get_dummy_token(input_ids.shape[0]							,							input_ids.device)
											UpperCAmelCase__					:   Optional[int]       				=       torch.cat((dummy_token, input_ids)							,							dim=1)
								UpperCAmelCase__					:   List[Any]       				=       self.transformer(inputs_embeds=_lowerCamelCase							,							labels=_lowerCamelCase							,							attention_mask=_lowerCamelCase)
								if self.prefix_hidden_dim is not None:
											return out, hidden
								else:
											return out




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								return torch.zeros(_lowerCamelCase							,							self.prefix_length							,							dtype=torch.intaa							,							device=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.encode_prefix(_lowerCamelCase)




					@torch.no_grad()
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   int       				=       torch.split(_lowerCamelCase							,							1							,							dim=0)
								UpperCAmelCase__					:   Optional[int]       				=       []
								UpperCAmelCase__					:   Union[str, Any]       				=       []
								for feature in features:
											UpperCAmelCase__					:   Dict       				=       self.decode_prefix(feature.to(_lowerCamelCase))  # back to the clip feature
											# Only support beam search for now
											UpperCAmelCase__				,    UpperCAmelCase__					:   int       				=       self.generate_beam(
											    input_embeds=_lowerCamelCase							,							device=_lowerCamelCase							,							eos_token_id=_lowerCamelCase)
											generated_tokens.append(output_tokens[0])
											generated_seq_lengths.append(seq_lengths[0])
								UpperCAmelCase__					:   Dict       				=       torch.stack(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       torch.stack(_lowerCamelCase)
								return generated_tokens, generated_seq_lengths




					@torch.no_grad()
					def 			snake_case__  ( self							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase = 5							,							_lowerCamelCase = 67							,							_lowerCamelCase = 1.0							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       eos_token_id
								UpperCAmelCase__					:   Dict       				=       None
								UpperCAmelCase__					:   Any       				=       None
								UpperCAmelCase__					:   Dict       				=       torch.ones(_lowerCamelCase							,							device=_lowerCamelCase							,							dtype=torch.int)
								UpperCAmelCase__					:   List[str]       				=       torch.zeros(_lowerCamelCase							,							device=_lowerCamelCase							,							dtype=torch.bool)

								if input_embeds is not None:
											UpperCAmelCase__					:   str       				=       input_embeds
								else:
											UpperCAmelCase__					:   int       				=       self.transformer.transformer.wte(_lowerCamelCase)

								for i in range(_lowerCamelCase):
											UpperCAmelCase__					:   str       				=       self.transformer(inputs_embeds=_lowerCamelCase)
											UpperCAmelCase__					:   Tuple       				=       outputs.logits
											UpperCAmelCase__					:   List[str]       				=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
											UpperCAmelCase__					:   Any       				=       logits.softmax(-1).log()

											if scores is None:
														UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       logits.topk(_lowerCamelCase							,							-1)
														UpperCAmelCase__					:   int       				=       generated.expand(_lowerCamelCase							,							*generated.shape[1:])
														UpperCAmelCase__				,    UpperCAmelCase__					:   str       				=       next_tokens.permute(1							,							0), scores.squeeze(0)
														if tokens is None:
																	UpperCAmelCase__					:   Union[str, Any]       				=       next_tokens
														else:
																	UpperCAmelCase__					:   List[Any]       				=       tokens.expand(_lowerCamelCase							,							*tokens.shape[1:])
																	UpperCAmelCase__					:   Optional[Any]       				=       torch.cat((tokens, next_tokens)							,							dim=1)
											else:
														UpperCAmelCase__					:   Optional[int]       				=       -float(np.inf)
														UpperCAmelCase__					:   Optional[int]       				=       0
														UpperCAmelCase__					:   Union[str, Any]       				=       scores[:, None] + logits
														seq_lengths[~is_stopped] += 1
														UpperCAmelCase__					:   Optional[Any]       				=       scores_sum / seq_lengths[:, None]
														UpperCAmelCase__				,    UpperCAmelCase__					:   Dict       				=       scores_sum_average.view(-1).topk(_lowerCamelCase							,							-1)
														UpperCAmelCase__					:   Optional[int]       				=       next_tokens // scores_sum.shape[1]
														UpperCAmelCase__					:   int       				=       seq_lengths[next_tokens_source]
														UpperCAmelCase__					:   Union[str, Any]       				=       next_tokens % scores_sum.shape[1]
														UpperCAmelCase__					:   Any       				=       next_tokens.unsqueeze(1)
														UpperCAmelCase__					:   str       				=       tokens[next_tokens_source]
														UpperCAmelCase__					:   Dict       				=       torch.cat((tokens, next_tokens)							,							dim=1)
														UpperCAmelCase__					:   Union[str, Any]       				=       generated[next_tokens_source]
														UpperCAmelCase__					:   Tuple       				=       scores_sum_average * seq_lengths
														UpperCAmelCase__					:   int       				=       is_stopped[next_tokens_source]

											UpperCAmelCase__					:   List[str]       				=       self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]							,							1							,							-1)
											UpperCAmelCase__					:   Dict       				=       torch.cat((generated, next_token_embed)							,							dim=1)
											UpperCAmelCase__					:   List[Any]       				=       is_stopped + next_tokens.eq(_lowerCamelCase).squeeze()
											if is_stopped.all():
														break

								UpperCAmelCase__					:   Dict       				=       scores / seq_lengths
								UpperCAmelCase__					:   Dict       				=       scores.argsort(descending=_lowerCamelCase)
								# tokens tensors are already padded to max_seq_length
								UpperCAmelCase__					:   List[str]       				=       [tokens[i] for i in order]
								UpperCAmelCase__					:   Optional[Any]       				=       torch.stack(_lowerCamelCase							,							dim=0)
								UpperCAmelCase__					:   Tuple       				=       torch.tensor([seq_lengths[i] for i in order]							,							dtype=seq_lengths.dtype)
								return output_texts, seq_lengths



'''simple docstring'''



import numpy


class 						_snake_case   :




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       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__					:   Optional[Any]       				=       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__					:   Optional[int]       				=       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__					:   Any       				=       numpy.random.rand(3							,							1)

								# Real output values provided.
								UpperCAmelCase__					:   Tuple       				=       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):
								UpperCAmelCase__					:   List[str]       				=       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__					:   Any       				=       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__					:   Tuple       				=       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):
								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__					:   str       				=       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__					:   Any       				=       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							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								for iteration in range(1							,							iterations + 1):
											UpperCAmelCase__					:   Optional[Any]       				=       self.feedforward()
											self.back_propagation()
											if give_loss:
														UpperCAmelCase__					:   str       				=       numpy.mean(numpy.square(output - self.feedforward()))
														print(f'''Iteration {iteration} Loss: {loss}''')




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       input_arr

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

								UpperCAmelCase__					:   List[Any]       				=       sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer							,							self.first_hidden_layer_and_second_hidden_layer_weights							,							))

								UpperCAmelCase__					:   Optional[Any]       				=       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       _UpperCamelCase (						UpperCamelCase__   ):
			return 1 / (1 + numpy.exp(-value   ))







def       _UpperCamelCase (						UpperCamelCase__   ):
			return (value) * (1 - (value))







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       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__					:   str       				=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	, dtype=numpy.floataa   )

			# Calling neural network class.
			UpperCAmelCase__					:   List[Any]       				=       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()