
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE							:Optional[Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"bloom"
	snake_case_    =					["past_key_values"]
	snake_case_    =					{
	    "num_hidden_layers": "n_layer",
	    "num_attention_heads": "n_head",
	}






	def __init__(	self :			Optional[Any]							,A :			List[Any]=25_08_80							,A :			Optional[int]=64							,A :			List[Any]=2							,A :			Optional[int]=8							,A :			str=1E-5							,A :			str=0.02							,A :			int=True							,A :			Optional[Any]=1							,A :			int=2							,A :			str=False							,A :			Dict=0.0							,A :			List[Any]=0.0							,A :			str=1							,A :			List[Any]=False							,**A :			List[Any]							,):
					__A             =			vocab_size
					# Backward compatibility with n_embed kwarg
					__A             =			kwargs.pop("n_embed"							,A			)
					__A             =			hidden_size if n_embed is None else n_embed
					__A             =			n_layer
					__A             =			n_head
					__A             =			layer_norm_epsilon
					__A             =			initializer_range
					__A             =			use_cache
					__A             =			pretraining_tp
					__A             =			apply_residual_connection_post_layernorm
					__A             =			hidden_dropout
					__A             =			attention_dropout

					__A             =			bos_token_id
					__A             =			eos_token_id
					__A             =			slow_but_exact

					super().__init__(bos_token_id=A							,eos_token_id=A							,**A			)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.12"  )






	def __init__(	self :			str							,A :			PretrainedConfig							,A :			str = "default"							,A :			List[PatchingSpec] = None							,A :			bool = False							,):
					super().__init__(A							,task=A							,patching_specs=A							,use_past=A			)
					if not getattr(self._config							,"pad_token_id"							,A			):
									# TODO: how to do that better?
									__A             =			0






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}			)
					if self.use_past:
									# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
									self.fill_with_past_key_values_(A							,direction="inputs"							,inverted_values_shape=A			)
									__A             =			{0: "batch", 1: "past_sequence + sequence"}
					else:
									__A             =			{0: "batch", 1: "sequence"}

					return common_inputs






	@property
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return self._config.n_layer






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self._config.n_head






	@property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return 1E-3






	def     UpperCamelCase_  (	self :			Any							,A :			"PreTrainedTokenizer"							,A :			int = -1							,A :			int = -1							,A :			bool = False							,A :			Optional["TensorType"] = None							,):
					__A             =			super(A							,self			).generate_dummy_inputs(
					    A							,batch_size=A							,seq_length=A							,is_pair=A							,framework=A			)

					# We need to order the input in the way they appears in the forward()
					__A             =			OrderedDict({"input_ids": common_inputs["input_ids"]}			)

					# Need to add the past_keys
					if self.use_past:
									if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
									else:
													import torch

													__A      ,							__A             =			common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													__A             =			seqlen + 2
													__A             =			self._config.hidden_size // self.num_attention_heads
													__A             =			(
													    batch * self.num_attention_heads,
													    head_dim,
													    past_key_values_length,
													)
													__A             =			(
													    batch * self.num_attention_heads,
													    past_key_values_length,
													    head_dim,
													)
													__A             =			[
													    (torch.zeros(A			), torch.zeros(A			)) for _ in range(self.num_layers			)
													]

					__A             =			common_inputs["attention_mask"]
					if self.use_past:
									__A             =			ordered_inputs["attention_mask"].dtype
									__A             =			torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(A							,A							,dtype=A			)]							,dim=1			)

					return ordered_inputs







	@property
	def     UpperCamelCase_  (	self :			int			):
					return 13


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			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.
					__A             =			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.
					__A             =			numpy.random.rand(
					    4							,3			)

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

					# Real output values provided.
					__A             =			output_array

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






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			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.
					__A             =			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.
					__A             =			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     UpperCamelCase_  (	self :			str			):
					__A             =			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			)							,)
					__A             =			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			)							,)
					__A             =			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     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					for iteration in range(1							,iterations + 1			):
									__A             =			self.feedforward()
									self.back_propagation()
									if give_loss:
													__A             =			numpy.mean(numpy.square(output - self.feedforward()			)			)
													print(f'''Iteration {iteration} Loss: {loss}'''			)







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

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

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

					__A             =			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      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return 1 / (1 + numpy.exp(-value						))


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return (value) * (1 - (value))


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			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.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			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=1_0		,       give_loss=a_						)

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


if __name__ == "__main__":
					example()
