
import numpy as np
from matplotlib import pyplot as plt






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return 1 / (1 + np.exp(-1 * x							))







class 	A_       :




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=None		,   snake_case=None		,   snake_case=False	):
		lowercase					  =   units
		lowercase					  =   None
		lowercase					  =   None
		lowercase					  =   activation
		if learning_rate is None:
			lowercase					  =   0.3
		lowercase					  =   learning_rate
		lowercase					  =   is_input_layer





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   np.asmatrix(np.random.normal(0		,   0.5		,   (self.units, back_units)	)	)
		lowercase					  =   np.asmatrix(np.random.normal(0		,   0.5		,   self.units	)	).T
		if self.activation is None:
			lowercase					  =   sigmoid





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# activation function may be sigmoid or linear
		if self.activation == sigmoid:
			lowercase					  =   np.dot(self.output		,   (1 - self.output).T	)
			lowercase					  =   np.diag(np.diag(snake_case	)	)
		else:
			lowercase					  =   1
		return gradient_activation





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   xdata
		if self.is_input_layer:
			# input layer
			lowercase					  =   xdata
			lowercase					  =   xdata
			return xdata
		else:
			lowercase					  =   np.dot(self.weight		,   self.xdata	) - self.bias
			lowercase					  =   self.activation(self.wx_plus_b	)
			return self.output





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   self.cal_gradient()  # i * i 维
		lowercase					  =   np.asmatrix(np.dot(gradient.T		,   snake_case	)	)

		lowercase					  =   np.asmatrix(self.xdata	)
		lowercase					  =   -1
		lowercase					  =   self.weight

		lowercase					  =   np.dot(gradient.T		,   self._gradient_weight.T	)
		lowercase					  =   gradient * self._gradient_bias
		lowercase					  =   np.dot(snake_case		,   self._gradient_x	).T
		# upgrade: the Negative gradient direction
		lowercase					  =   self.weight - self.learn_rate * self.gradient_weight
		lowercase					  =   self.bias - self.learn_rate * self.gradient_bias.T
		# updates the weights and bias according to learning rate (0.3 if undefined)
		return self.gradient







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   []
		lowercase					  =   []
		lowercase					  =   plt.figure()
		lowercase					  =   self.fig_loss.add_subplot(1		,   1		,   1	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		self.layers.append(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, layer in enumerate(self.layers[:]	):
			if i < 1:
				lowercase					  =   True
			else:
				layer.initializer(self.layers[i - 1].units	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, layer in enumerate(self.layers[:]	):
			print(F'''------- layer {i} -------'''	)
			print('weight.shape '		,   np.shape(layer.weight	)	)
			print('bias.shape '		,   np.shape(layer.bias	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   train_round
		lowercase					  =   accuracy

		self.ax_loss.hlines(self.accuracy		,   0		,   self.train_round * 1.1	)

		lowercase					  =   np.shape(snake_case	)
		for _ in range(snake_case	):
			lowercase					  =   0
			for row in range(x_shape[0]	):
				lowercase					  =   np.asmatrix(xdata[row, :]	).T
				lowercase					  =   np.asmatrix(ydata[row, :]	).T

				# forward propagation
				for layer in self.layers:
					lowercase					  =   layer.forward_propagation(_xdata	)

				lowercase  , lowercase					  =   self.cal_loss(_ydata		,   _xdata	)
				lowercase					  =   all_loss + loss

				# back propagation: the input_layer does not upgrade
				for layer in self.layers[:0:-1]:
					lowercase					  =   layer.back_propagation(snake_case	)

			lowercase					  =   all_loss / x_shape[0]
			self.train_mse.append(snake_case	)

			self.plot_loss()

			if mse < self.accuracy:
				print('----达到精度----'	)
				return mse
		return None





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		lowercase					  =   np.sum(np.power((ydata - ydata_)		,   2	)	)
		lowercase					  =   2 * (ydata_ - ydata)
		# vector (shape is the same as _ydata.shape)
		return self.loss, self.loss_gradient





	def      SCREAMING_SNAKE_CASE__						(	self	):
		if self.ax_loss.lines:
			self.ax_loss.lines.remove(self.ax_loss.lines[0]	)
		self.ax_loss.plot(self.train_mse		,   'r-'	)
		plt.ion()
		plt.xlabel('step'	)
		plt.ylabel('loss'	)
		plt.show()
		plt.pause(0.1	)






def 							UpperCAmelCase_		(							):
	lowercase					  =   np.random.randn(10			,							10							)
	lowercase					  =   np.asarray(
	    [
	        [0.8, 0.4],
	        [0.4, 0.3],
	        [0.34, 0.45],
	        [0.67, 0.32],
	        [0.88, 0.67],
	        [0.78, 0.77],
	        [0.55, 0.66],
	        [0.55, 0.43],
	        [0.54, 0.1],
	        [0.1, 0.5],
	    ]							)
	lowercase					  =   BPNN()
	for i in (10, 20, 30, 2):
		model.add_layer(DenseLayer(__SCREAMING_SNAKE_CASE							)							)
	model.build()
	model.summary()
	model.train(xdata=__SCREAMING_SNAKE_CASE			,							ydata=__SCREAMING_SNAKE_CASE			,							train_round=100			,							accuracy=0.01							)


if __name__ == "__main__":
	example()



# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase     				=			logging.get_logger(__name__)


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	from transformers import Trainer







	class 	A_       (      __lowerCamelCase       ):




		'''simple docstring'''


		def __init__(	self		,   snake_case = 101	):
			lowercase					  =   length





		def __len__(	self	):
			return self.length





		def __getitem__(	self		,   snake_case	):
			return i







	class 	A_       :




		'''simple docstring'''


		def __call__(	self		,   snake_case	):
			return {"input_ids": torch.tensor(snake_case	), "labels": torch.tensor(snake_case	)}







	class 	A_       (      nn.Module       ):




		'''simple docstring'''


		def __init__(	self	):
			super().__init__()
			# Add some (unused) params otherwise DDP will complain.
			lowercase					  =   nn.Linear(120		,   80	)





		def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case=None	):
			if labels is not None:
				return torch.tensor(0.0		,   device=input_ids.device	), input_ids
			else:
				return input_ids






def 							UpperCAmelCase_		(							):
	lowercase					  =   HfArgumentParser((TrainingArguments,)							)
	sys.argv += ["--output_dir", "./examples"]
	lowercase					  =   parser.parse_args_into_dataclasses()[0]

	logger.warning(
	    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
	    F'''tpu_num_cores: {training_args.tpu_num_cores}'''			,							)

	# Essentially, what we want to verify in the distributed case is
	# that we get all samples back, in the right order.
	# (this is crucial for prediction for instance)
	for dataset_length in [1001, 256, 15]:
		lowercase					  =   DummyDataset(__SCREAMING_SNAKE_CASE							)

		def compute_metrics(__SCREAMING_SNAKE_CASE							) -> Dict:
			lowercase					  =   list(range(len(__SCREAMING_SNAKE_CASE							)							)							)
			lowercase					  =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

		lowercase					  =   Trainer(
		    model=DummyModel()			,							args=__SCREAMING_SNAKE_CASE			,							data_collator=DummyDataCollator()			,							eval_dataset=__SCREAMING_SNAKE_CASE			,							compute_metrics=__SCREAMING_SNAKE_CASE			,							)
		lowercase					  =   trainer.evaluate()
		logger.info(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   2

		lowercase					  =   trainer.evaluate()
		logger.info(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   None

	logger.info('🔥 All distributed tests successful'							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()

