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



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)


