code: import timeit
  from typing import Callable, Optional
  
  from ..configuration_utils import PretrainedConfig
  from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
  from ..utils import is_pyanvml_available, is_torch_available, logging
  from .benchmark_utils import (
      Benchmark,
      Memory,
      MemorySummary,
      measure_peak_memory_cpu,
      start_memory_tracing,
      stop_memory_tracing,
  )
  
  
  if is_torch_available():
  				import torch
  
  				from .benchmark_args import PyTorchBenchmarkArguments
  
  
  if is_pyanvml_available():
  				import pyanvml.pyanvml as nvml
  
  
  A_:      Tuple        =			logging.get_logger(__name__)
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					42
  							lowerCAmelCase__       =					42
  							lowerCAmelCase__       =					"PyTorch"
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return torch.__version__
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self._prepare_inference_func(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  														return self._measure_speed(_inference  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self._prepare_inference_func(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  														return self._measure_memory(_inference  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self._prepare_train_func(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  														return self._measure_speed(_train  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self._prepare_train_func(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  														return self._measure_memory(_train  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.config_dict[model_name]
  
  														if self.args.torchscript:
  																					_lowercase				=	True
  
  														_lowercase				=	(
  														    hasattr(UpperCAmelCase     ,   """architectures"""  )
  														    and isinstance(config.architectures     ,   UpperCAmelCase  )
  														    and len(config.architectures  ) > 0
  														)
  														if not self.args.only_pretrain_model and has_model_class_in_config:
  																					try:
  																												_lowercase				=	config.architectures[0]
  																												_lowercase				=	__import__("""transformers"""     ,   fromlist=[model_class]  )
  																												_lowercase				=	getattr(UpperCAmelCase     ,   UpperCAmelCase  )
  																												_lowercase				=	model_cls(UpperCAmelCase  )
  																					except ImportError:
  																												raise ImportError(
  																												    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																												    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""  )
  														else:
  																					_lowercase				=	MODEL_MAPPING[config.__class__](UpperCAmelCase  )
  
  														model.eval()
  														model.to(self.args.device  )
  
  														# encoder-decoder has vocab size saved differently
  														_lowercase				=	config.vocab_size if hasattr(UpperCAmelCase     ,   """vocab_size"""  ) else config.encoder.vocab_size
  														_lowercase				=	torch.randint(UpperCAmelCase     ,   (batch_size, sequence_length)     ,   dtype=torch.long     ,   device=self.args.device  )
  
  														if self.args.fpaa:
  																					logger.info("""Running training in Mixed Precision..."""  )
  																					if not self.args.is_gpu:
  																												raise ValueError("""Mixed precision is possible only for GPU."""  )
  																					# amp seems to have memory leaks so that memory usage
  																					# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  																					model.half()
  
  														if self.args.torchscript:
  																					with torch.no_grad():
  																												_lowercase				=	torch.jit.trace(UpperCAmelCase     ,   UpperCAmelCase  )
  														else:
  																					_lowercase				=	model
  
  														def encoder_decoder_forward():
  																					with torch.no_grad():
  																												_lowercase				=	inference_model(UpperCAmelCase     ,   decoder_input_ids=UpperCAmelCase  )
  																					return outputs
  
  														def encoder_forward():
  																					with torch.no_grad():
  																												_lowercase				=	inference_model(UpperCAmelCase  )
  																					return outputs
  
  														_lowercase				=	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  														return _forward
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.config_dict[model_name]
  
  														_lowercase				=	(
  														    hasattr(UpperCAmelCase     ,   """architectures"""  )
  														    and isinstance(config.architectures     ,   UpperCAmelCase  )
  														    and len(config.architectures  ) > 0
  														)
  														if not self.args.only_pretrain_model and has_model_class_in_config:
  																					try:
  																												_lowercase				=	config.architectures[0]
  																												_lowercase				=	__import__("""transformers"""     ,   fromlist=[model_class]  )
  																												_lowercase				=	getattr(UpperCAmelCase     ,   UpperCAmelCase  )
  																												_lowercase				=	model_cls(UpperCAmelCase  )
  																					except ImportError:
  																												raise ImportError(
  																												    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																												    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""  )
  														else:
  																					_lowercase				=	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCAmelCase  )
  
  														if self.args.torchscript:
  																					raise NotImplementedError("""Training for torchscript is currently not implemented"""  )
  														else:
  																					_lowercase				=	model
  
  														model.train()
  														model.to(self.args.device  )
  
  														# encoder-decoder has vocab size saved differently
  														_lowercase				=	config.vocab_size if hasattr(UpperCAmelCase     ,   """vocab_size"""  ) else config.encoder.vocab_size
  														_lowercase				=	torch.randint(UpperCAmelCase     ,   (batch_size, sequence_length)     ,   dtype=torch.long     ,   device=self.args.device  )
  
  														if self.args.fpaa:
  																					logger.info("""Running training in Mixed Precision..."""  )
  																					if not self.args.is_gpu:
  																												raise ValueError("""Mixed precision is possible only for GPU."""  )
  
  																					# amp seems to have memory leaks so that memory usage
  																					# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  																					model.half()
  
  														def compute_loss_and_backprob_encoder():
  																					_lowercase				=	train_model(UpperCAmelCase     ,   labels=UpperCAmelCase  )[0]
  																					loss.backward()
  																					return loss
  
  														def compute_loss_and_backprob_encoder_decoder():
  																					_lowercase				=	train_model(UpperCAmelCase     ,   decoder_input_ids=UpperCAmelCase     ,   labels=UpperCAmelCase  )[0]
  																					loss.backward()
  																					return loss
  
  														_lowercase				=	(
  														    compute_loss_and_backprob_encoder_decoder
  														    if config.is_encoder_decoder
  														    else compute_loss_and_backprob_encoder
  														)
  														return _train
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														try:
  																					if self.args.is_tpu or self.args.torchscript:
  																												# run additional 10 times to stabilize compilation for tpu and torchscript
  																												logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""  )
  																												timeit.repeat(
  																												    UpperCAmelCase     ,   repeat=1     ,   number=5     ,   )
  
  																					# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
  																					_lowercase				=	timeit.repeat(
  																					    UpperCAmelCase     ,   repeat=self.args.repeat     ,   number=10     ,   )
  
  																					if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
  																												import torch_xla.debug.metrics as met
  
  																												self.print_fn(met.metrics_report()  )
  
  																					return min(UpperCAmelCase  ) / 10.0
  														except RuntimeError as e:
  																					self.print_fn(F'''Doesn\'t fit on GPU. {e}'''  )
  																					return "N/A"
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														try:
  																					if self.args.trace_memory_line_by_line:
  																												_lowercase				=	start_memory_tracing("""transformers"""  )
  
  																					if self.args.is_tpu:
  																												# tpu
  																												raise NotImplementedError(
  																												    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
  																												    """ `--no-memory` or `args.memory=False`"""  )
  																					elif self.args.is_gpu:
  																												if not is_pyanvml_available():
  																																			logger.warning(
  																																			    """py3nvml not installed, we won't log GPU memory usage. """
  																																			    """Install py3nvml (pip install py3nvml) to log information about GPU."""  )
  																																			_lowercase				=	"""N/A"""
  																												else:
  																																			logger.info(
  																																			    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
  																																			    """ on the same GPU."""  )
  																																			# init nvml
  																																			nvml.nvmlInit()
  																																			func()
  																																			_lowercase				=	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
  																																			_lowercase				=	nvml.nvmlDeviceGetMemoryInfo(UpperCAmelCase  )
  																																			_lowercase				=	meminfo.used
  																																			_lowercase				=	Memory(UpperCAmelCase  )
  																																			# shutdown nvml
  																																			nvml.nvmlShutdown()
  																					else:
  																												# cpu
  																												_lowercase				=	measure_peak_memory_cpu(UpperCAmelCase  )
  																												_lowercase				=	Memory(UpperCAmelCase  ) if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ) else memory_bytes
  
  																					if self.args.trace_memory_line_by_line:
  																												_lowercase				=	stop_memory_tracing(UpperCAmelCase  )
  																					else:
  																												_lowercase				=	None
  
  																					return memory, summary
  														except RuntimeError as e:
  																					self.print_fn(F'''Doesn\'t fit on GPU. {e}'''  )
  																					return "N/A", None
  
  
  
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)
  
  
  