code: 
  """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
  
  
  
style_context: 
  """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
  
  
  