
"""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 A_			(_UpperCAmelCase   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	42
  SCREAMING_SNAKE_CASE__    :		Union[str, Any]						  =	42
  SCREAMING_SNAKE_CASE__    :		Union[str, Any]						  =	"""PyTorch"""







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     return torch.__version__







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self._prepare_inference_func(lowercase_       ,     lowercase_       ,     lowercase_						)
     return self._measure_speed(_inference						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      List[Any]      =					self._prepare_inference_func(lowercase_       ,     lowercase_       ,     lowercase_						)
     return self._measure_memory(_inference						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self._prepare_train_func(lowercase_       ,     lowercase_       ,     lowercase_						)
     return self._measure_speed(_train						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      str      =					self._prepare_train_func(lowercase_       ,     lowercase_       ,     lowercase_						)
     return self._measure_memory(_train						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					self.config_dict[model_name]

     if self.args.torchscript:
        UpperCAmelCase_      :      Any      =					True

     UpperCAmelCase_      :      Optional[int]      =					(
         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:
           UpperCAmelCase_      :      int      =					config.architectures[0]
           UpperCAmelCase_      :      Any      =					__import__("transformers"       ,     fromlist=[model_class]						)
           UpperCAmelCase_      :      Tuple      =					getattr(lowercase_       ,     lowercase_						)
           UpperCAmelCase_      :      Dict      =					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:
        UpperCAmelCase_      :      Dict      =					MODEL_MAPPING[config.__class__](lowercase_						)

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

     # encoder-decoder has vocab size saved differently
     UpperCAmelCase_      :      List[str]      =					config.vocab_size if hasattr(lowercase_       ,     "vocab_size"						) else config.encoder.vocab_size
     UpperCAmelCase_      :      int      =					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():
           UpperCAmelCase_      :      Optional[int]      =					torch.jit.trace(lowercase_       ,     lowercase_						)
     else:
        UpperCAmelCase_      :      Tuple      =					model

     def encoder_decoder_forward():
        with torch.no_grad():
           UpperCAmelCase_      :      Any      =					inference_model(lowercase_       ,     decoder_input_ids=lowercase_						)
        return outputs

     def encoder_forward():
        with torch.no_grad():
           UpperCAmelCase_      :      Optional[Any]      =					inference_model(lowercase_						)
        return outputs

     UpperCAmelCase_      :      List[str]      =					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
     return _forward







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Any      =					self.config_dict[model_name]

     UpperCAmelCase_      :      Tuple      =					(
         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:
           UpperCAmelCase_      :      int      =					config.architectures[0]
           UpperCAmelCase_      :      Optional[int]      =					__import__("transformers"       ,     fromlist=[model_class]						)
           UpperCAmelCase_      :      Dict      =					getattr(lowercase_       ,     lowercase_						)
           UpperCAmelCase_      :      List[str]      =					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:
        UpperCAmelCase_      :      Any      =					MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase_						)

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

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

     # encoder-decoder has vocab size saved differently
     UpperCAmelCase_      :      int      =					config.vocab_size if hasattr(lowercase_       ,     "vocab_size"						) else config.encoder.vocab_size
     UpperCAmelCase_      :      Optional[int]      =					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():
        UpperCAmelCase_      :      Dict      =					train_model(lowercase_       ,     labels=lowercase_						)[0]
        loss.backward()
        return loss

     def compute_loss_and_backprob_encoder_decoder():
        UpperCAmelCase_      :      Dict      =					train_model(lowercase_       ,     decoder_input_ids=lowercase_       ,     labels=lowercase_						)[0]
        loss.backward()
        return loss

     UpperCAmelCase_      :      List[Any]      =					(
         compute_loss_and_backprob_encoder_decoder
         if config.is_encoder_decoder
         else compute_loss_and_backprob_encoder
     )
     return _train







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """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
        UpperCAmelCase_      :      Any      =					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  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""






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

        if self.args.trace_memory_line_by_line:
           UpperCAmelCase_      :      List[Any]      =					stop_memory_tracing(lowercase_						)
        else:
           UpperCAmelCase_      :      List[Any]      =					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

from typing import Any


class A_			:




  '''simple docstring'''







  def __init__(	self       ,     lowercase_       ,     lowercase_						):  # noqa: A002

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					item
     UpperCAmelCase_      :      Optional[Any]      =					next




class A_			:




  '''simple docstring'''







  def __init__(	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Node | None      =					None
     UpperCAmelCase_      :      Optional[int]      =					0







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      int      =					Node(lowercase_       ,     self.head						)
     self.size += 1







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     # Switched 'self.is_empty()' to 'self.head is None'
     # because mypy was considering the possibility that 'self.head'
     # can be None in below else part and giving error
     if self.head is None:
        return None
     else:
        UpperCAmelCase_      :      int      =					self.head.item
        UpperCAmelCase_      :      str      =					self.head.next
        self.size -= 1
        return item







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     return self.head is None







  def __str__(	self						):

     """simple docstring"""
     if self.is_empty():
        return ""
     else:
        UpperCAmelCase_      :      str      =					self.head
        UpperCAmelCase_      :      str      =					""
        UpperCAmelCase_      :      list[str]      =					[]
        while iterate:
           item_list.append(str(iterate.item						)						)
           UpperCAmelCase_      :      Tuple      =					iterate.next

        UpperCAmelCase_      :      Optional[Any]      =					" --> ".join(lowercase_						)

        return item_str







  def __len__(	self						):

     """simple docstring"""
     return self.size


