
"""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[Any]:
									"""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


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					if gon_side < 3 or gon_side > 5:
										raise ValueError('gon_side must be in the range [3, 5]'     )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 3 != 0:
										raise ValueError('a gon ring should have a length that is a multiple of 3'     )

					if min(numbers[::3]     ) != numbers[0]:
										return False

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

					return all(sum(numbers[i : i + 3]     ) == total for i in range(3,	len(SCREAMING_SNAKE_CASE_     ),	3     )     )


if __name__ == "__main__":
	print(solution())
