"""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


SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)



class 	lowerCAmelCase_     (      UpperCAmelCase_       ):



				"""simple docstring"""
				_lowerCAmelCase  :     PyTorchBenchmarkArguments
				_lowerCAmelCase  :     PretrainedConfig
				_lowerCAmelCase  :     str      	=    "PyTorch"

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return torch.__version__

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self._prepare_inference_func(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_     )
										return self._measure_speed(_inference     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self._prepare_inference_func(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_     )
										return self._measure_memory(_inference     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self._prepare_train_func(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_     )
										return self._measure_speed(_train     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self._prepare_train_func(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_     )
										return self._measure_memory(_train     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.config_dict[model_name]

										if self.args.torchscript:
																snake_case             =      True

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

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

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

										def encoder_decoder_forward():
																with torch.no_grad():
																						snake_case             =      inference_model(SCREAMING_SNAKE_CASE_		,     decoder_input_ids=SCREAMING_SNAKE_CASE_     )
																return outputs

										def encoder_forward():
																with torch.no_grad():
																						snake_case             =      inference_model(SCREAMING_SNAKE_CASE_     )
																return outputs

										snake_case             =      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
										return _forward

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.config_dict[model_name]

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

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

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

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

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

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

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""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(
																						    SCREAMING_SNAKE_CASE_		,     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
																snake_case             =      timeit.repeat(
																    SCREAMING_SNAKE_CASE_		,     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(SCREAMING_SNAKE_CASE_     ) / 10.0
										except RuntimeError as e:
																self.print_fn(F"""Doesn't fit on GPU. {e}"""     )
																return "N/A"

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

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

																if self.args.trace_memory_line_by_line:
																						snake_case             =      stop_memory_tracing(SCREAMING_SNAKE_CASE_     )
																else:
																						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 __future__ import annotations

import random






class 						lowerCAmelCase_	:







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				6



  def __init__( self				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Union[str, Any]          =		list(range(1					,  Dice.NUM_SIDES + 1	)	)




  def 					_snake_case     ( self				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       return random.choice(self.sides	)






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase = 2	)			-> list[float]:
     A:		Union[str, Any]          =		[Dice() for i in range(__lowercase	)]
     A:		Union[str, Any]          =		[0] * (len(__lowercase	) * Dice.NUM_SIDES + 1)
     for _ in range(__lowercase	):
          count_of_sum[sum(dice.roll() for dice in dices	)] += 1
     A:		Dict          =		[round((count * 1_0_0) / num_throws		,				2	) for count in count_of_sum]
     return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()






