





'''simple docstring'''


def       A							(      _UpperCAmelCase   :				list						,_UpperCAmelCase   :				list					)  ->				list:


  '''simple docstring'''




  __lowerCAmelCase		:    int			     =  len(_UpperCAmelCase					)
  __lowerCAmelCase		:    int			     =  len(_UpperCAmelCase					)
  __lowerCAmelCase		:    int			     =  (
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  __lowerCAmelCase		:    list			     =  []
  for char_count in range(_UpperCAmelCase					):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count]					)
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count]					)

  return output_result_list


if __name__ == "__main__":
    print(alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5]), end=" ")












'''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


A_				     =							logging.get_logger(__name__)


class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      _snake_case           =				42
      _snake_case           =				42
      _snake_case           =				"PyTorch"




      @property
      def 	snake_case  (     self )				->  Dict:
        return torch.__version__




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  float:
        __lowerCAmelCase		:    Any			     =  self._prepare_inference_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_speed(_inference )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  [Memory, Optional[MemorySummary]]:
        __lowerCAmelCase		:    Dict			     =  self._prepare_inference_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_memory(_inference )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  float:
        __lowerCAmelCase		:    Optional[Any]			     =  self._prepare_train_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_speed(_train )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  [Memory, Optional[MemorySummary]]:
        __lowerCAmelCase		:    Optional[int]			     =  self._prepare_train_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_memory(_train )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Callable[[], None]:
        __lowerCAmelCase		:    str			     =  self.config_dict[model_name]

        if self.args.torchscript:
          __lowerCAmelCase		:    Tuple			     =  True

        __lowerCAmelCase		:    Union[str, Any]			     =  (
            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:
            __lowerCAmelCase		:    Dict			     =  config.architectures[0]
            __lowerCAmelCase		:    List[str]			     =  __import__('transformers'     , fromlist=[model_class] )
            __lowerCAmelCase		:    Tuple			     =  getattr(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    Dict			     =  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:
          __lowerCAmelCase		:    List[Any]			     =  MODEL_MAPPING[config.__class__](SCREAMING_SNAKE_CASE )

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

        # encoder-decoder has vocab size saved differently
        __lowerCAmelCase		:    List[str]			     =  config.vocab_size if hasattr(SCREAMING_SNAKE_CASE     , 'vocab_size' ) else config.encoder.vocab_size
        __lowerCAmelCase		:    Optional[Any]			     =  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():
            __lowerCAmelCase		:    List[Any]			     =  torch.jit.trace(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        else:
          __lowerCAmelCase		:    str			     =  model

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

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

        __lowerCAmelCase		:    Optional[Any]			     =  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Callable[[], None]:
        __lowerCAmelCase		:    int			     =  self.config_dict[model_name]

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

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

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

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

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

        __lowerCAmelCase		:    Dict			     =  (
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  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(
                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
          __lowerCAmelCase		:    Tuple			     =  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 ) / 1_0.0
        except RuntimeError as e:
          self.print_fn(F"""Doesn't fit on GPU. {e}""" )
          return "N/A"







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  [Memory, MemorySummary]:
        try:
          if self.args.trace_memory_line_by_line:
            __lowerCAmelCase		:    Tuple			     =  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.' )
              __lowerCAmelCase		:    Union[str, Any]			     =  '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()
              __lowerCAmelCase		:    int			     =  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
              __lowerCAmelCase		:    List[str]			     =  nvml.nvmlDeviceGetMemoryInfo(SCREAMING_SNAKE_CASE )
              __lowerCAmelCase		:    Tuple			     =  meminfo.used
              __lowerCAmelCase		:    Dict			     =  Memory(SCREAMING_SNAKE_CASE )
              # shutdown nvml
              nvml.nvmlShutdown()
          else:
            # cpu
            __lowerCAmelCase		:    Tuple			     =  measure_peak_memory_cpu(SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    str			     =  Memory(SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) else memory_bytes

          if self.args.trace_memory_line_by_line:
            __lowerCAmelCase		:    int			     =  stop_memory_tracing(SCREAMING_SNAKE_CASE )
          else:
            __lowerCAmelCase		:    Any			     =  None

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





