code: import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
      datefmt='%m/%d/%Y %H:%M:%S',
      level=logging.INFO,
  )
  A_:      Dict        =			logging.getLogger(__name__)
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							np.random.seed(args.seed					)
  							torch.manual_seed(args.seed					)
  							if args.n_gpu > 0:
  														torch.cuda.manual_seed_all(args.seed					)
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	argparse.ArgumentParser()
  							parser.add_argument(
  							    """--model_name_or_path""" ,default=__A ,type=__A ,required=__A ,)
  							parser.add_argument("""--prompt""" ,type=__A ,default=""""""					)
  							parser.add_argument("""--length""" ,type=__A ,default=20					)
  							parser.add_argument("""--stop_token""" ,type=__A ,default=__A ,help="""Token at which text generation is stopped"""					)
  							parser.add_argument(
  							    """--temperature""" ,type=__A ,default=1.0 ,help="""temperature of 1.0 has no effect, lower tend toward greedy sampling""" ,)
  							parser.add_argument(
  							    """--repetition_penalty""" ,type=__A ,default=1.0 ,help="""primarily useful for CTRL model; in that case, use 1.2"""					)
  							parser.add_argument("""--k""" ,type=__A ,default=0					)
  							parser.add_argument("""--penalty_alpha""" ,type=__A ,default=0.0					)
  							parser.add_argument("""--p""" ,type=__A ,default=0.9					)
  
  							parser.add_argument("""--prefix""" ,type=__A ,default="""""" ,help="""Text added prior to input."""					)
  							parser.add_argument("""--padding_text""" ,type=__A ,default="""""" ,help="""Deprecated, the use of `--prefix` is preferred."""					)
  							parser.add_argument("""--xlm_language""" ,type=__A ,default="""""" ,help="""Optional language when used with the XLM model."""					)
  
  							parser.add_argument("""--seed""" ,type=__A ,default=42 ,help="""random seed for initialization"""					)
  							parser.add_argument("""--no_cuda""" ,action="""store_true""" ,help="""Avoid using CUDA when available"""					)
  							parser.add_argument(
  							    """--fp16""" ,action="""store_true""" ,help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" ,)
  							_lowercase				=	parser.parse_args()
  
  							_lowercase				=	torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
  							_lowercase				=	0 if args.no_cuda else torch.cuda.device_count()
  
  							logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''					)
  
  							set_seed(__A					)
  
  							# Initialize the model and tokenizer
  							_lowercase				=	AutoTokenizer.from_pretrained(args.model_name_or_path					)
  							_lowercase				=	AutoModelForCausalLM.from_pretrained(args.model_name_or_path					)
  
  							# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  							# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  							model.to(args.device					)
  
  							if args.fpaa:
  														model.half()
  
  							logger.info(__A					)
  							_lowercase				=	args.prompt if args.prompt else input("""Model prompt >>> """					)
  
  							_lowercase				=	tokenizer(__A ,return_tensors="""pt""" ,add_special_tokens=__A					)
  							_lowercase				=	{key: value.to(args.device					) for key, value in inputs.items()}
  
  							_lowercase				=	model.generate(
  							    **__A ,max_length=args.length + len(inputs["""input_ids"""][0]					) ,penalty_alpha=args.penalty_alpha ,top_k=args.k ,)
  
  							_lowercase				=	[]
  							for generated_sequence_idx, generated_sequence in enumerate(__A					):
  														print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''					)
  														_lowercase				=	generated_sequence.tolist()
  
  														# Decode text
  														_lowercase				=	tokenizer.decode(__A ,clean_up_tokenization_spaces=__A ,add_special_tokens=__A					)
  
  														# Remove all text after the stop token
  														_lowercase				=	text[: text.find(args.stop_token					) if args.stop_token else None]
  
  														# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
  														_lowercase				=	(
  														    prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0] ,clean_up_tokenization_spaces=__A					)					) :]
  														)
  
  														generated_sequences.append(__A					)
  														print(__A					)
  
  							return generated_sequences
  
  
  if __name__ == "__main__":
  				main()
  
  
  
style_context: import random
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	sample
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														_lowercase				=	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"""  )
  														_lowercase				=	learning_rate
  														_lowercase				=	epoch_number
  														_lowercase				=	bias
  														_lowercase				=	len(UpperCAmelCase  )
  														_lowercase				=	len(sample[0]  )  # number of columns in dataset
  														_lowercase				=	[]
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														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  )
  
  														_lowercase				=	0
  
  														while True:
  																					_lowercase				=	False
  																					for i in range(self.number_sample  ):
  																												_lowercase				=	0
  																												for j in range(self.col_sample + 1  ):
  																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
  																												_lowercase				=	self.sign(UpperCAmelCase  )
  																												if y != self.target[i]:
  																																			for j in range(self.col_sample + 1  ):
  																																										_lowercase				=	(
  																																										    self.weight[j]
  																																										    + self.learning_rate
  																																										    * (self.target[i] - y)
  																																										    * self.sample[i][j]
  																																										)
  																																			_lowercase				=	True
              # print('Epoch: \n',epoch_count)
  																					_lowercase				=	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     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														sample.insert(0     ,   self.bias  )
  														_lowercase				=	0
  														for i in range(self.col_sample + 1  ):
  																					_lowercase				=	u + self.weight[i] * sample[i]
  
  														_lowercase				=	self.sign(UpperCAmelCase  )
  
  														if y == -1:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P1"""  )
  														else:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P2"""  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
      [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
      [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
      [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
      [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
      [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
      [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
      [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
      [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
      [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
      [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
      [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
      [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
      [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
      [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
      [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
      [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
      [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
      [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
      [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
      [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
      [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
      [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
      [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
      [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
      [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
      [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
      [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
      [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
      [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
  ]
  
  A_:      Optional[int]        =			[
      -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()
  
  				A_:      str        =			Perceptron(
  				    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
  				)
  				network.training()
  				print('Finished training perceptron')
  				print('Enter values to predict or q to exit')
  				while True:
  								A_:      list        =			[]
  								for i in range(len(samples[0])):
  												A_:      Dict        =			input('value: ').strip()
  												if user_input == "q":
  																break
  												A_:      Union[str, Any]        =			float(user_input)
  												sample.insert(i, observation)
  								network.sort(sample)
  
  
  