
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


__magic_name__  :  int     			=   logging.get_logger(__name__)


class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  lowerCAmelCase_								=		42
  lowerCAmelCase_								=		42
  lowerCAmelCase_								=		"PyTorch"

  @property
  def 					lowercase_						(					self					):
      return torch.__version__

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self._prepare_inference_func(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)
      return self._measure_speed(_inference					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self._prepare_inference_func(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)
      return self._measure_memory(_inference					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self._prepare_train_func(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)
      return self._measure_speed(_train					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self._prepare_train_func(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)
      return self._measure_memory(_train					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self.config_dict[model_name]

      if self.args.torchscript:
          A_								=					True

      A_								=					(
          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_								=					config.architectures[0]
              A_								=					__import__("transformers"						,    fromlist=[model_class]					)
              A_								=					getattr(__UpperCAmelCase						,    __UpperCAmelCase					)
              A_								=					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_								=					MODEL_MAPPING[config.__class__](__UpperCAmelCase					)

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

      # encoder-decoder has vocab size saved differently
      A_								=					config.vocab_size if hasattr(__UpperCAmelCase						,    "vocab_size"					) else config.encoder.vocab_size
      A_								=					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_								=					torch.jit.trace(__UpperCAmelCase						,    __UpperCAmelCase					)
      else:
          A_								=					model

      def encoder_decoder_forward():
          with torch.no_grad():
              A_								=					inference_model(__UpperCAmelCase						,    decoder_input_ids=__UpperCAmelCase					)
          return outputs

      def encoder_forward():
          with torch.no_grad():
              A_								=					inference_model(__UpperCAmelCase					)
          return outputs

      A_								=					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
      return _forward

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self.config_dict[model_name]

      A_								=					(
          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_								=					config.architectures[0]
              A_								=					__import__("transformers"						,    fromlist=[model_class]					)
              A_								=					getattr(__UpperCAmelCase						,    __UpperCAmelCase					)
              A_								=					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_								=					MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCAmelCase					)

      if self.args.torchscript:
          raise NotImplementedError("Training for torchscript is currently not implemented"					)
      else:
          A_								=					model

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

      # encoder-decoder has vocab size saved differently
      A_								=					config.vocab_size if hasattr(__UpperCAmelCase						,    "vocab_size"					) else config.encoder.vocab_size
      A_								=					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_								=					train_model(__UpperCAmelCase						,    labels=__UpperCAmelCase					)[0]
          loss.backward()
          return loss

      def compute_loss_and_backprob_encoder_decoder():
          A_								=					train_model(__UpperCAmelCase						,    decoder_input_ids=__UpperCAmelCase						,    labels=__UpperCAmelCase					)[0]
          loss.backward()
          return loss

      A_								=					(
          compute_loss_and_backprob_encoder_decoder
          if config.is_encoder_decoder
          else compute_loss_and_backprob_encoder
      )
      return _train

  def 					lowercase_						(					self						,    __UpperCamelCase					):



      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_								=					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						,    __UpperCamelCase					):



      try:
          if self.args.trace_memory_line_by_line:
              A_								=					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_								=					"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_								=					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
                  A_								=					nvml.nvmlDeviceGetMemoryInfo(__UpperCAmelCase					)
                  A_								=					meminfo.used
                  A_								=					Memory(__UpperCAmelCase					)
                  # shutdown nvml
                  nvml.nvmlShutdown()
          else:
              # cpu
              A_								=					measure_peak_memory_cpu(__UpperCAmelCase					)
              A_								=					Memory(__UpperCAmelCase					) if isinstance(__UpperCAmelCase						,    __UpperCAmelCase					) else memory_bytes

          if self.args.trace_memory_line_by_line:
              A_								=					stop_memory_tracing(__UpperCAmelCase					)
          else:
              A_								=					None

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












import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
