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


UpperCAmelCase							   =      logging.get_logger(__name__)






class 							_SCREAMING_SNAKE_CASE(   UpperCamelCase_	):
   UpperCamelCase_				:						Tuple 			=   42
   UpperCamelCase_				:						Optional[Any] 			=   42
   UpperCamelCase_				:						Optional[int] 			=   'PyTorch'
   @property
   def _lowercase(    self    )	->Optional[int]:
        return torch.__version__
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->float:
        a  :    Any				=						self._prepare_inference_func(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
        return self._measure_speed(_inference    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->[Memory, Optional[MemorySummary]]:
        a  :    Any				=						self._prepare_inference_func(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
        return self._measure_memory(_inference    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->float:
        a  :    Tuple				=						self._prepare_train_func(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
        return self._measure_speed(_train    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->[Memory, Optional[MemorySummary]]:
        a  :    List[Any]				=						self._prepare_train_func(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
        return self._measure_memory(_train    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Callable[[], None]:
        a  :    List[Any]				=						self.config_dict[model_name]

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

        a  :    str				=						(
            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:
                  a  :    Any				=						config.architectures[0]
                  a  :    Union[str, Any]				=						__import__('''transformers'''							,     fromlist=[model_class]    )
                  a  :    Any				=						getattr(__UpperCamelCase							,     __UpperCamelCase    )
                  a  :    Union[str, Any]				=						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:
             a  :    Tuple				=						MODEL_MAPPING[config.__class__](__UpperCamelCase    )

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

        # encoder-decoder has vocab size saved differently
        a  :    Dict				=						config.vocab_size if hasattr(__UpperCamelCase							,     '''vocab_size'''    ) else config.encoder.vocab_size
        a  :    str				=						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():
                  a  :    Tuple				=						torch.jit.trace(__UpperCamelCase							,     __UpperCamelCase    )
        else:
             a  :    Optional[int]				=						model

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

        def encoder_forward():
             with torch.no_grad():
                  a  :    Any				=						inference_model(__UpperCamelCase    )
             return outputs

        a  :    Tuple				=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Callable[[], None]:
        a  :    str				=						self.config_dict[model_name]

        a  :    int				=						(
            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:
                  a  :    Tuple				=						config.architectures[0]
                  a  :    Optional[int]				=						__import__('''transformers'''							,     fromlist=[model_class]    )
                  a  :    Tuple				=						getattr(__UpperCamelCase							,     __UpperCamelCase    )
                  a  :    Union[str, Any]				=						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:
             a  :    str				=						MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase    )

        if self.args.torchscript:
             raise NotImplementedError('''Training for torchscript is currently not implemented'''    )
        else:
             a  :    Union[str, Any]				=						model

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

        # encoder-decoder has vocab size saved differently
        a  :    Any				=						config.vocab_size if hasattr(__UpperCamelCase							,     '''vocab_size'''    ) else config.encoder.vocab_size
        a  :    Any				=						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():
             a  :    List[Any]				=						train_model(__UpperCamelCase							,     labels=__UpperCamelCase    )[0]
             loss.backward()
             return loss

        def compute_loss_and_backprob_encoder_decoder():
             a  :    Optional[int]				=						train_model(__UpperCamelCase							,     decoder_input_ids=__UpperCamelCase							,     labels=__UpperCamelCase    )[0]
             loss.backward()
             return loss

        a  :    int				=						(
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train
   def _lowercase(    self							,     lowerCAmelCase_    )	->float:


        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
             a  :    List[str]				=						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 _lowercase(    self							,     lowerCAmelCase_    )	->[Memory, MemorySummary]:


        try:
             if self.args.trace_memory_line_by_line:
                  a  :    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.'''    )
                       a  :    Tuple				=						'''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()
                       a  :    str				=						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
                       a  :    Optional[int]				=						nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase    )
                       a  :    List[str]				=						meminfo.used
                       a  :    Tuple				=						Memory(__UpperCamelCase    )
                       # shutdown nvml
                       nvml.nvmlShutdown()
             else:
                  # cpu
                  a  :    Optional[int]				=						measure_peak_memory_cpu(__UpperCamelCase    )
                  a  :    Dict				=						Memory(__UpperCamelCase    ) if isinstance(__UpperCamelCase							,     __UpperCamelCase    ) else memory_bytes

             if self.args.trace_memory_line_by_line:
                  a  :    Dict				=						stop_memory_tracing(__UpperCamelCase    )
             else:
                  a  :    Union[str, Any]				=						None

             return memory, summary
        except RuntimeError as e:
             self.print_fn(F"""Doesn\'t fit on GPU. {e}"""    )
             return "N/A", None
def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:
     return 1 if input_a == 0 else 0







def 	UpperCAmelCase       (		)	->	None:
     assert not_gate(0       ) == 1
     assert not_gate(1       ) == 0


if __name__ == "__main__":
  print(not_gate(0))
  print(not_gate(1))