

import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						# Initialise PyTorch model
						snake_case_     =			ElectraConfig.from_json_file(SCREAMING_SNAKE_CASE__ )
						print(F'''Building PyTorch model from configuration: {config}''' )

						if discriminator_or_generator == "discriminator":
												snake_case_     =			ElectraForPreTraining(SCREAMING_SNAKE_CASE__ )
						elif discriminator_or_generator == "generator":
												snake_case_     =			ElectraForMaskedLM(SCREAMING_SNAKE_CASE__ )
						else:
												raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

						# Load weights from tf checkpoint
						load_tf_weights_in_electra(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	discriminator_or_generator=SCREAMING_SNAKE_CASE__ )

						# Save pytorch-model
						print(F'''Save PyTorch model to {pytorch_dump_path}''' )
						torch.save(model.state_dict()						,	SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					lowerCAmelCase_ 			=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
					)
					parser.add_argument(
					    '''--config_file''',
					    default=None,
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
					)
					parser.add_argument(
					    '''--discriminator_or_generator''',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
					        '''\'generator\'.'''
					    ),
					)
					lowerCAmelCase_ 			=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
					)


import random





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       list[list[float]]							,     _UpperCamelCase				:       list[int]							,     _UpperCamelCase				:       float = 0.01							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = -1							,     )   ->None:
									snake_case_     =			sample
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									snake_case_     =			target
									if len(self.target							) == 0:
															raise ValueError('''Target data can not be empty'''							)
									if len(self.sample							) != len(self.target							):
															raise ValueError('''Sample data and Target data do not have matching lengths'''							)
									snake_case_     =			learning_rate
									snake_case_     =			epoch_number
									snake_case_     =			bias
									snake_case_     =			len(_UpperCamelCase							)
									snake_case_     =			len(sample[0]							)  # number of columns in dataset
									snake_case_     =			[]







			def        snake_case__(  self				:       List[str]							)   ->None:
									for sample in self.sample:
															sample.insert(0							,     self.bias							)

									for _ in range(self.col_sample							):
															self.weight.append(random.random()							)

									self.weight.insert(0							,     self.bias							)

									snake_case_     =			0

									while True:
															snake_case_     =			False
															for i in range(self.number_sample							):
																					snake_case_     =			0
																					for j in range(self.col_sample + 1							):
																											snake_case_     =			u + self.weight[j] * self.sample[i][j]
																					snake_case_     =			self.sign(_UpperCamelCase							)
																					if y != self.target[i]:
																											for j in range(self.col_sample + 1							):
																																	snake_case_     =			(
																																	    self.weight[j]
																																	    + self.learning_rate
																																	    * (self.target[i] - y)
																																	    * self.sample[i][j]
																																	)
																											snake_case_     =			True
            # print('Epoch: \n',epoch_count)
															snake_case_     =			epoch_count + 1
															# if you want control the epoch or just by error
															if not has_misclassified:
																					print(('''\nEpoch:\n''', epoch_count)							)
																					print('''------------------------\n'''							)
																					# if epoch_count > self.epoch_number or not error:
																					break







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       list[float]							)   ->None:
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									sample.insert(0							,     self.bias							)
									snake_case_     =			0
									for i in range(self.col_sample + 1							):
															snake_case_     =			u + self.weight[i] * sample[i]

									snake_case_     =			self.sign(_UpperCamelCase							)

									if y == -1:
															print(('''Sample: ''', sample)							)
															print('''classification: P1'''							)
									else:
															print(('''Sample: ''', sample)							)
															print('''classification: P2'''							)







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       float							)   ->int:
									return 1 if u >= 0 else -1






lowerCAmelCase_ 			=			[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

lowerCAmelCase_ 			=			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase_ 			=			Perceptron(
					    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
					)
					network.training()
					print('''Finished training perceptron''')
					print('''Enter values to predict or q to exit''')
					while True:
										lowerCAmelCase_ 			=			[]
										for i in range(len(samples[0])):
															lowerCAmelCase_ 			=			input('''value: ''').strip()
															if user_input == "q":
																				break
															lowerCAmelCase_ 			=			float(user_input)
															sample.insert(i, observation)
										network.sort(sample)