





"""simple docstring"""




import numpy as np
from matplotlib import pyplot as plt





def  a_       (							_lowercase     ):
						return 1 / (1 + np.exp(-1 * x     ))




class 				_a		:






				def __init__(				self	:					Dict,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					int=None,				lowerCAmelCase__	:					Optional[int]=None,				lowerCAmelCase__	:					List[Any]=False							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					units
										_UpperCamelCase    :      Union[str, Any]						     =					None
										_UpperCamelCase    :      Optional[int]						     =					None
										_UpperCamelCase    :      Tuple						     =					activation
										if learning_rate is None:
																_UpperCamelCase    :      List[Any]						     =					0.3
										_UpperCamelCase    :      int						     =					learning_rate
										_UpperCamelCase    :      List[str]						     =					is_input_layer






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					Union[str, Any]							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					np.asmatrix(np.random.normal(0,				0.5,				(self.units, back_units)							)							)
										_UpperCamelCase    :      Union[str, Any]						     =					np.asmatrix(np.random.normal(0,				0.5,				self.units							)							).T
										if self.activation is None:
																_UpperCamelCase    :      int						     =					sigmoid






				def        snake_case			(				self	:					Tuple							)			->   List[str]:




										'''simple docstring'''



										if self.activation == sigmoid:
																_UpperCamelCase    :      int						     =					np.dot(self.output,				(1 - self.output).T							)
																_UpperCamelCase    :      int						     =					np.diag(np.diag(lowerCAmelCase__							)							)
										else:
																_UpperCamelCase    :      str						     =					1
										return gradient_activation






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					int							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					xdata
										if self.is_input_layer:
																# input layer
																_UpperCamelCase    :      Dict						     =					xdata
																_UpperCamelCase    :      Optional[int]						     =					xdata
																return xdata
										else:
																_UpperCamelCase    :      Optional[int]						     =					np.dot(self.weight,				self.xdata							) - self.bias
																_UpperCamelCase    :      Any						     =					self.activation(self.wx_plus_b							)
																return self.output






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					Union[str, Any]							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					self.cal_gradient()  # i * i 维
										_UpperCamelCase    :      Union[str, Any]						     =					np.asmatrix(np.dot(gradient.T,				lowerCAmelCase__							)							)

										_UpperCamelCase    :      List[str]						     =					np.asmatrix(self.xdata							)
										_UpperCamelCase    :      Any						     =					-1
										_UpperCamelCase    :      Any						     =					self.weight

										_UpperCamelCase    :      str						     =					np.dot(gradient.T,				self._gradient_weight.T							)
										_UpperCamelCase    :      Dict						     =					gradient * self._gradient_bias
										_UpperCamelCase    :      Optional[int]						     =					np.dot(lowerCAmelCase__,				self._gradient_x							).T
										# upgrade: the Negative gradient direction
										_UpperCamelCase    :      Any						     =					self.weight - self.learn_rate * self.gradient_weight
										_UpperCamelCase    :      Dict						     =					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		:






				def __init__(				self	:					Dict							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					[]
										_UpperCamelCase    :      Optional[Any]						     =					[]
										_UpperCamelCase    :      str						     =					plt.figure()
										_UpperCamelCase    :      Any						     =					self.fig_loss.add_subplot(1,				1,				1							)






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					List[Any]							)			->   Optional[Any]:




										'''simple docstring'''



										self.layers.append(lowerCAmelCase__							)






				def        snake_case			(				self	:					List[str]							)			->   Tuple:




										'''simple docstring'''



										for i, layer in enumerate(self.layers[:]							):
																if i < 1:
																						_UpperCamelCase    :      Union[str, Any]						     =					True
																else:
																						layer.initializer(self.layers[i - 1].units							)






				def        snake_case			(				self	:					Tuple							)			->   Optional[Any]:




										'''simple docstring'''



										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        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Optional[int]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					train_round
										_UpperCamelCase    :      Union[str, Any]						     =					accuracy

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

										_UpperCamelCase    :      Any						     =					np.shape(lowerCAmelCase__							)
										for _ in range(lowerCAmelCase__							):
																_UpperCamelCase    :      List[Any]						     =					0
																for row in range(x_shape[0]							):
																						_UpperCamelCase    :      Dict						     =					np.asmatrix(xdata[row, :]							).T
																						_UpperCamelCase    :      List[str]						     =					np.asmatrix(ydata[row, :]							).T

																						# forward propagation
																						for layer in self.layers:
																												_UpperCamelCase    :      Optional[int]						     =					layer.forward_propagation(_xdata							)

																						_UpperCamelCase     ,							_UpperCamelCase    :      Dict						     =					self.cal_loss(_ydata,				_xdata							)
																						_UpperCamelCase    :      Union[str, Any]						     =					all_loss + loss

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

																_UpperCamelCase    :      List[str]						     =					all_loss / x_shape[0]
																self.train_mse.append(lowerCAmelCase__							)

																self.plot_loss()

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






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Optional[int]							)			->   Optional[Any]:




										'''simple docstring'''



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






				def        snake_case			(				self	:					int							)			->   Dict:




										'''simple docstring'''



										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  a_       (							):
						_UpperCamelCase    :      List[str]						     =					np.random.randn(10 ,		10     )
						_UpperCamelCase    :      int						     =					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],
						    ]     )
						_UpperCamelCase    :      Optional[int]						     =					BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(_lowercase     )     )
						model.build()
						model.summary()
						model.train(xdata=_lowercase ,		ydata=_lowercase ,		train_round=100 ,		accuracy=0.01     )


if __name__ == "__main__":
						example()










"""simple docstring"""




import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase_     =logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCamelCase_     ="""lfs-multipart-upload"""




class 				_a		(   _lowerCAmelCase       ):






				@staticmethod
				def        snake_case			(				lowerCAmelCase__	:					ArgumentParser							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					parser.add_parser(
										    '''lfs-enable-largefiles''',				help=(
										        '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
										    ),				)
										enable_parser.add_argument('''path''',				type=lowerCAmelCase__,				help='''Local path to repository you want to configure.'''							)
										enable_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsEnableCommand(lowerCAmelCase__							)							)

										_UpperCamelCase    :      Any						     =					parser.add_parser(
										    lowerCAmelCase__,				help=(
										        '''Deprecated: use `huggingface-cli` instead. '''
										        '''Command will get called by git-lfs, do not call it directly.'''
										    ),				)
										upload_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsUploadCommand(lowerCAmelCase__							)							)




class 				_a		:






				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					str							)			->   str:




										'''simple docstring'''



										warnings.warn(
										    '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''							)
										_UpperCamelCase    :      Union[str, Any]						     =					os.path.abspath(self.args.path							)
										if not os.path.isdir(lowerCAmelCase__							):
																print('''This does not look like a valid git repo.'''							)
																exit(1							)
										subprocess.run(
										    '''git config lfs.customtransfer.multipart.path transformers-cli'''.split(),				check=lowerCAmelCase__,				cwd=lowerCAmelCase__							)
										subprocess.run(
										    f"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split(),				check=lowerCAmelCase__,				cwd=lowerCAmelCase__,				)
										print('''Local repo set up for largefiles'''							)





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      Tuple						     =					json.dumps(_lowercase     ) + '''\n'''
						sys.stdout.write(_lowercase     )
						sys.stdout.flush()





def  a_       (							):
						_UpperCamelCase    :      Optional[Any]						     =					json.loads(sys.stdin.readline().strip()     )

						if "terminate" in (msg.get('''type'''     ), msg.get('''event'''     )):
												# terminate message received
												return None

						if msg.get('''event'''     ) not in ("download", "upload"):
												logger.critical('''Received unexpected message'''     )
												sys.exit(1     )

						return msg




class 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					filepath
										_UpperCamelCase    :      Tuple						     =					seek_from
										_UpperCamelCase    :      Optional[int]						     =					read_limit
										_UpperCamelCase    :      List[str]						     =					0






				def __enter__(				self	:					Union[str, Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					open(self.filepath,				'''rb'''							)
										self.f.seek(self.seek_from							)
										return self






				def __len__(				self	:					str							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					os.fstat(self.f.fileno()							).st_size
										return min(self.read_limit,				total_length - self.seek_from							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					List[str]=-1							)			->   int:




										'''simple docstring'''



										if self.n_seen >= self.read_limit:
																return b""
										_UpperCamelCase    :      str						     =					self.read_limit - self.n_seen
										_UpperCamelCase    :      Dict						     =					self.f.read(remaining_amount if n < 0 else min(lowerCAmelCase__,				lowerCAmelCase__							)							)
										self.n_seen += len(lowerCAmelCase__							)
										return data






				def __iter__(				self	:					int							)			->   List[Any]:




										'''simple docstring'''



										yield self.read(n=4 * 1_0_2_4 * 1_0_2_4							)






				def __exit__(				self	:					Optional[Any],				*lowerCAmelCase__	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										self.f.close()




class 				_a		:






				def __init__(				self	:					List[str],				lowerCAmelCase__	:					Optional[Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					json.loads(sys.stdin.readline().strip()							)
										if not (init_msg.get('''event'''							) == "init" and init_msg.get('''operation'''							) == "upload"):
																write_msg({'''error''': {'''code''': 3_2, '''message''': '''Wrong lfs init operation'''}}							)
																sys.exit(1							)

										# The transfer process should use the information it needs from the
										# initiation structure, and also perform any one-off setup tasks it
										# needs to do. It should then respond on stdout with a simple empty
										# confirmation structure, as follows:
										write_msg({}							)

										# After the initiation exchange, git-lfs will send any number of
										# transfer requests to the stdin of the transfer process, in a serial sequence.
										while True:
																_UpperCamelCase    :      int						     =					read_msg()
																if msg is None:
																						# When all transfers have been processed, git-lfs will send
																						# a terminate event to the stdin of the transfer process.
																						# On receiving this message the transfer process should
																						# clean up and terminate. No response is expected.
																						sys.exit(0							)

																_UpperCamelCase    :      Union[str, Any]						     =					msg['''oid''']
																_UpperCamelCase    :      List[str]						     =					msg['''path''']
																_UpperCamelCase    :      Optional[int]						     =					msg['''action''']['''href''']
																_UpperCamelCase    :      Optional[int]						     =					msg['''action''']['''header''']
																_UpperCamelCase    :      Tuple						     =					int(header.pop('''chunk_size'''							)							)
																_UpperCamelCase    :      List[str]						     =					list(header.values()							)

																_UpperCamelCase    :      str						     =					[]
																for i, presigned_url in enumerate(lowerCAmelCase__							):
																						with FileSlice(lowerCAmelCase__,				seek_from=i * chunk_size,				read_limit=lowerCAmelCase__							) as data:
																												_UpperCamelCase    :      Any						     =					requests.put(lowerCAmelCase__,				data=lowerCAmelCase__							)
																												r.raise_for_status()
																												parts.append(
																												    {
																												        '''etag''': r.headers.get('''etag'''							),
																												        '''partNumber''': i + 1,
																												    }							)
																												# In order to support progress reporting while data is uploading / downloading,
																												# the transfer process should post messages to stdout
																												write_msg(
																												    {
																												        '''event''': '''progress''',
																												        '''oid''': oid,
																												        '''bytesSoFar''': (i + 1) * chunk_size,
																												        '''bytesSinceLast''': chunk_size,
																												    }							)
																												# Not precise but that's ok.

																_UpperCamelCase    :      str						     =					requests.post(
																    lowerCAmelCase__,				json={
																        '''oid''': oid,
																        '''parts''': parts,
																    },				)
																r.raise_for_status()

																write_msg({'''event''': '''complete''', '''oid''': oid}							)



