




"""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


_lowercase						   =     logging.get_logger(__name__)

class    __lowercase							(      A_						):


				'''simple docstring'''


				_lowerCAmelCase						      =42
				_lowerCAmelCase						      =42
				_lowerCAmelCase						      ="PyTorch"

				@property
				def    _lowerCamelCase       ( self :  Tuple   ):
										return torch.__version__

				def    _lowerCamelCase       ( self :  str   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int   ):
										snake_case__							:       Tuple				      =       self._prepare_inference_func(_A   ,_A   ,_A   )
										return self._measure_speed(_inference   )

				def    _lowerCamelCase       ( self :  Dict   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int   ):
										snake_case__							:       Optional[int]				      =       self._prepare_inference_func(_A   ,_A   ,_A   )
										return self._measure_memory(_inference   )

				def    _lowerCamelCase       ( self :  Optional[Any]   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int   ):
										snake_case__							:       Optional[int]				      =       self._prepare_train_func(_A   ,_A   ,_A   )
										return self._measure_speed(_train   )

				def    _lowerCamelCase       ( self :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int   ):
										snake_case__							:       Union[str, Any]				      =       self._prepare_train_func(_A   ,_A   ,_A   )
										return self._measure_memory(_train   )

				def    _lowerCamelCase       ( self :  Optional[Any]   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int   ):
										snake_case__							:       Optional[int]				      =       self.config_dict[model_name]

										if self.args.torchscript:
																snake_case__							:       Dict				      =       True

										snake_case__							:       Optional[Any]				      =       (
										    hasattr(_A   ,'''architectures'''   )
										    and isinstance(config.architectures   ,_A   )
										    and len(config.architectures   ) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																						snake_case__							:       Any				      =       config.architectures[0]
																						snake_case__							:       Optional[Any]				      =       __import__('''transformers'''   ,fromlist=[model_class]   )
																						snake_case__							:       str				      =       getattr(_A   ,_A   )
																						snake_case__							:       List[Any]				      =       model_cls(_A   )
																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__							:       List[Any]				      =       MODEL_MAPPING[config.__class__](_A   )

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

										# encoder-decoder has vocab size saved differently
										snake_case__							:       Optional[int]				      =       config.vocab_size if hasattr(_A   ,'''vocab_size'''   ) else config.encoder.vocab_size
										snake_case__							:       Tuple				      =       torch.randint(_A   ,(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__							:       List[Any]				      =       torch.jit.trace(_A   ,_A   )
										else:
																snake_case__							:       Any				      =       model

										def encoder_decoder_forward():
																with torch.no_grad():
																						snake_case__							:       Tuple				      =       inference_model(_A   ,decoder_input_ids=_A   )
																return outputs

										def encoder_forward():
																with torch.no_grad():
																						snake_case__							:       Any				      =       inference_model(_A   )
																return outputs

										snake_case__							:       Optional[int]				      =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
										return _forward

				def    _lowerCamelCase       ( self :  Optional[Any]   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int   ):
										snake_case__							:       Optional[int]				      =       self.config_dict[model_name]

										snake_case__							:       Union[str, Any]				      =       (
										    hasattr(_A   ,'''architectures'''   )
										    and isinstance(config.architectures   ,_A   )
										    and len(config.architectures   ) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																						snake_case__							:       Tuple				      =       config.architectures[0]
																						snake_case__							:       Optional[int]				      =       __import__('''transformers'''   ,fromlist=[model_class]   )
																						snake_case__							:       Optional[int]				      =       getattr(_A   ,_A   )
																						snake_case__							:       Optional[Any]				      =       model_cls(_A   )
																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__							:       List[str]				      =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_A   )

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

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

										# encoder-decoder has vocab size saved differently
										snake_case__							:       Optional[int]				      =       config.vocab_size if hasattr(_A   ,'''vocab_size'''   ) else config.encoder.vocab_size
										snake_case__							:       List[str]				      =       torch.randint(_A   ,(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__							:       Optional[int]				      =       train_model(_A   ,labels=_A   )[0]
																loss.backward()
																return loss

										def compute_loss_and_backprob_encoder_decoder():
																snake_case__							:       Tuple				      =       train_model(_A   ,decoder_input_ids=_A   ,labels=_A   )[0]
																loss.backward()
																return loss

										snake_case__							:       Union[str, Any]				      =       (
										    compute_loss_and_backprob_encoder_decoder
										    if config.is_encoder_decoder
										    else compute_loss_and_backprob_encoder
										)
										return _train

				def    _lowerCamelCase       ( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  List[str]   ):
										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(
																						    _A   ,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__							:       List[str]				      =       timeit.repeat(
																    _A   ,repeat=self.args.repeat   ,number=1_0   ,)

																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(_A   ) / 10.0
										except RuntimeError as e:
																self.print_fn(F'''Doesn\'t fit on GPU. {e}'''   )
																return "N/A"





				def    _lowerCamelCase       ( self :  Any   ,SCREAMING_SNAKE_CASE_ :  Callable[[], None]   ):
										try:
																if self.args.trace_memory_line_by_line:
																						snake_case__							:       Union[str, Any]				      =       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__							:       Dict				      =       '''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__							:       List[str]				      =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
																												snake_case__							:       List[Any]				      =       nvml.nvmlDeviceGetMemoryInfo(_A   )
																												snake_case__							:       int				      =       meminfo.used
																												snake_case__							:       Dict				      =       Memory(_A   )
																												# shutdown nvml
																												nvml.nvmlShutdown()
																else:
																						# cpu
																						snake_case__							:       Optional[Any]				      =       measure_peak_memory_cpu(_A   )
																						snake_case__							:       Any				      =       Memory(_A   ) if isinstance(_A   ,_A   ) else memory_bytes

																if self.args.trace_memory_line_by_line:
																						snake_case__							:       Optional[int]				      =       stop_memory_tracing(_A   )
																else:
																						snake_case__							:       Optional[int]				      =       None

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




_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

