
"""simple docstring"""




import numpy as np

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="utf-8",					SCREAMING_SNAKE_CASE_="surrogatepass"      ):
					SCREAMING_SNAKE_CASE           =     bin(int.from_bytes(text.encode(lowercase_,					lowercase_      ),					'big'      )      )[2:]
					return bits.zfill(8 * ((len(lowercase_      ) + 7) // 8)      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="utf-8",					SCREAMING_SNAKE_CASE_="surrogatepass"      ):
					SCREAMING_SNAKE_CASE           =     int(lowercase_,					2      )
					return n.to_bytes((n.bit_length() + 7) // 8,					'big'      ).decode(lowercase_,					lowercase_      ) or "\0"

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):







					if size_par + len(lowercase_      ) <= 2**size_par - (len(lowercase_      ) - 1):
										raise ValueError('size of parity don\'t match with size of data'      )

					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     [bin(lowercase_      )[2:] for x in range(1,					size_par + len(lowercase_      ) + 1      )]

					# sorted information data for the size of the output data
					SCREAMING_SNAKE_CASE           =     []
					# data position template + parity
					SCREAMING_SNAKE_CASE           =     []
					# parity bit counter
					SCREAMING_SNAKE_CASE           =     0
					# counter position of data bits
					SCREAMING_SNAKE_CASE           =     0

					for x in range(1,					size_par + len(lowercase_      ) + 1      ):
										# Performs a template of bit positions - who should be given,
										# and who should be parity
										if qtd_bp < size_par:
															if (np.log(lowercase_      ) / np.log(2      )).is_integer():
																				data_out_gab.append('P'      )
																				SCREAMING_SNAKE_CASE           =     qtd_bp + 1
															else:
																				data_out_gab.append('D'      )
										else:
															data_out_gab.append('D'      )

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_ord.append(data[cont_data]      )
															cont_data += 1
										else:
															data_ord.append(lowercase_      )

    # Calculates parity
					SCREAMING_SNAKE_CASE           =     0  # parity bit counter
					for bp in range(1,					size_par + 1      ):
										# Bit counter one for a given parity
										SCREAMING_SNAKE_CASE           =     0
										# counter to control the loop reading
										SCREAMING_SNAKE_CASE           =     0
										for x in data_ord:
															if x is not None:
																				try:
																									SCREAMING_SNAKE_CASE           =     (bin_pos[cont_loop])[-1 * (bp)]
																				except IndexError:
																									SCREAMING_SNAKE_CASE           =     '0'
																				if aux == "1" and x == "1":
																									cont_bo += 1
															cont_loop += 1
										parity.append(cont_bo % 2      )

										qtd_bp += 1

					# Mount the message
					SCREAMING_SNAKE_CASE           =     0  # parity bit counter
					for x in range(0,					size_par + len(lowercase_      )      ):
										if data_ord[x] is None:
															data_out.append(str(parity[cont_bp]      )      )
															cont_bp += 1
										else:
															data_out.append(data_ord[x]      )

					return data_out



def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     []
					# Parity bit counter
					SCREAMING_SNAKE_CASE           =     0
					# Counter p data bit reading
					SCREAMING_SNAKE_CASE           =     0
					# list of parity received
					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []

					for x in range(1,					len(lowercase_      ) + 1      ):
										# Performs a template of bit positions - who should be given,
										#  and who should be parity
										if qtd_bp < size_par and (np.log(lowercase_      ) / np.log(2      )).is_integer():
															data_out_gab.append('P'      )
															SCREAMING_SNAKE_CASE           =     qtd_bp + 1
										else:
															data_out_gab.append('D'      )

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_output.append(data[cont_data]      )
										else:
															parity_received.append(data[cont_data]      )
										cont_data += 1

					# -----------calculates the parity with the data
					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     [bin(lowercase_      )[2:] for x in range(1,					size_par + len(lowercase_      ) + 1      )]

					#  sorted information data for the size of the output data
					SCREAMING_SNAKE_CASE           =     []
					# Data position feedback + parity
					SCREAMING_SNAKE_CASE           =     []
					# Parity bit counter
					SCREAMING_SNAKE_CASE           =     0
					# Counter p data bit reading
					SCREAMING_SNAKE_CASE           =     0

					for x in range(1,					size_par + len(lowercase_      ) + 1      ):
										# Performs a template position of bits - who should be given,
										# and who should be parity
										if qtd_bp < size_par and (np.log(lowercase_      ) / np.log(2      )).is_integer():
															data_out_gab.append('P'      )
															SCREAMING_SNAKE_CASE           =     qtd_bp + 1
										else:
															data_out_gab.append('D'      )

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_ord.append(data_output[cont_data]      )
															cont_data += 1
										else:
															data_ord.append(lowercase_      )

    # Calculates parity
					SCREAMING_SNAKE_CASE           =     0  # parity bit counter
					for bp in range(1,					size_par + 1      ):
										# Bit counter one for a certain parity
										SCREAMING_SNAKE_CASE           =     0
										# Counter to control loop reading
										SCREAMING_SNAKE_CASE           =     0
										for x in data_ord:
															if x is not None:
																				try:
																									SCREAMING_SNAKE_CASE           =     (bin_pos[cont_loop])[-1 * (bp)]
																				except IndexError:
																									SCREAMING_SNAKE_CASE           =     '0'
																				if aux == "1" and x == "1":
																									cont_bo += 1
															cont_loop += 1
										parity.append(str(cont_bo % 2      )      )

										qtd_bp += 1

					# Mount the message
					SCREAMING_SNAKE_CASE           =     0  # Parity bit counter
					for x in range(0,					size_par + len(lowercase_      )      ):
										if data_ord[x] is None:
															data_out.append(str(parity[cont_bp]      )      )
															cont_bp += 1
										else:
															data_out.append(data_ord[x]      )

					SCREAMING_SNAKE_CASE           =     parity_received == parity
					return data_output, ack


"""simple docstring"""




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


snake_case			       =		logging.get_logger(__name__)




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				PyTorchBenchmarkArguments
				UpperCAmelCase_    :				PretrainedConfig
				UpperCAmelCase_    :				str						  =    "PyTorch"


				@property
				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									return torch.__version__


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									if self.args.torchscript:
														SCREAMING_SNAKE_CASE           =     True

									SCREAMING_SNAKE_CASE           =     (
									    hasattr(lowercase__		,     'architectures'      )
									    and isinstance(config.architectures		,     lowercase__      )
									    and len(config.architectures      ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     model_cls(lowercase__      )
														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:
														SCREAMING_SNAKE_CASE           =     MODEL_MAPPING[config.__class__](lowercase__      )

									model.eval()
									model.to(self.args.device      )

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE           =     torch.randint(lowercase__		,     (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():
																			SCREAMING_SNAKE_CASE           =     torch.jit.trace(lowercase__		,     lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									def encoder_decoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__		,     decoder_input_ids=lowercase__      )
														return outputs

									def encoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__      )
														return outputs

									SCREAMING_SNAKE_CASE           =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									SCREAMING_SNAKE_CASE           =     (
									    hasattr(lowercase__		,     'architectures'      )
									    and isinstance(config.architectures		,     lowercase__      )
									    and len(config.architectures      ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     model_cls(lowercase__      )
														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:
														SCREAMING_SNAKE_CASE           =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase__      )

									if self.args.torchscript:
														raise NotImplementedError('Training for torchscript is currently not implemented'      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									model.train()
									model.to(self.args.device      )

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE           =     torch.randint(lowercase__		,     (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():
														SCREAMING_SNAKE_CASE           =     train_model(lowercase__		,     labels=lowercase__      )[0]
														loss.backward()
														return loss

									def compute_loss_and_backprob_encoder_decoder():
														SCREAMING_SNAKE_CASE           =     train_model(lowercase__		,     decoder_input_ids=lowercase__		,     labels=lowercase__      )[0]
														loss.backward()
														return loss

									SCREAMING_SNAKE_CASE           =     (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train


				def 							A (    self		,     lowercase__      )   ->   float:
									"""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(
																			    lowercase__		,     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
														SCREAMING_SNAKE_CASE           =     timeit.repeat(
														    lowercase__		,     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(lowercase__      ) / 10.0
									except RuntimeError as e:
														self.print_fn(f'''Doesn\'t fit on GPU. {e}'''      )
														return "N/A"




				def 							A (    self		,     lowercase__      )   ->   [Memory, MemorySummary]:
									"""simple docstring"""







									try:
														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     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.'      )
																								SCREAMING_SNAKE_CASE           =     '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()
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetMemoryInfo(lowercase__      )
																								SCREAMING_SNAKE_CASE           =     meminfo.used
																								SCREAMING_SNAKE_CASE           =     Memory(lowercase__      )
																								# shutdown nvml
																								nvml.nvmlShutdown()
														else:
																			# cpu
																			SCREAMING_SNAKE_CASE           =     measure_peak_memory_cpu(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     Memory(lowercase__      ) if isinstance(lowercase__		,     lowercase__      ) else memory_bytes

														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     stop_memory_tracing(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     None

														return memory, summary
									except RuntimeError as e:
														self.print_fn(f'''Doesn\'t fit on GPU. {e}'''      )
														return "N/A", None
