






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	: Dict      =				logging.get_logger(__name__)
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				PyTorchBenchmarkArguments
      a						:				PretrainedConfig
      a						:				str							 =			"PyTorch"
      @property
      def 			UpperCAmelCase   (   self      :    int      )							->   int:
        return torch.__version__
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    int      )							->   float:
        __UpperCAmelCase							:			Dict     =						self._prepare_inference_func(__lowercase	,   __lowercase	,   __lowercase      )
        return self._measure_speed(_inference      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    int      )							->   [Memory, Optional[MemorySummary]]:
        __UpperCAmelCase							:			List[Any]     =						self._prepare_inference_func(__lowercase	,   __lowercase	,   __lowercase      )
        return self._measure_memory(_inference      )
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    int      )							->   float:
        __UpperCAmelCase							:			int     =						self._prepare_train_func(__lowercase	,   __lowercase	,   __lowercase      )
        return self._measure_speed(_train      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    int      )							->   [Memory, Optional[MemorySummary]]:
        __UpperCAmelCase							:			Tuple     =						self._prepare_train_func(__lowercase	,   __lowercase	,   __lowercase      )
        return self._measure_memory(_train      )
      def 			UpperCAmelCase   (   self      :    Union[str, Any]	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    int      )							->   Callable[[], None]:
        __UpperCAmelCase							:			Optional[int]     =						self.config_dict[model_name]

        if self.args.torchscript:
          __UpperCAmelCase							:			List[Any]     =						True

        __UpperCAmelCase							:			List[Any]     =						(
            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							:			Optional[Any]     =						config.architectures[0]
            __UpperCAmelCase							:			int     =						__import__("""transformers"""	,   fromlist=[model_class]      )
            __UpperCAmelCase							:			int     =						getattr(__lowercase	,   __lowercase      )
            __UpperCAmelCase							:			Tuple     =						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							:			Optional[Any]     =						MODEL_MAPPING[config.__class__](__lowercase      )

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

        # encoder-decoder has vocab size saved differently
        __UpperCAmelCase							:			Dict     =						config.vocab_size if hasattr(__lowercase	,   """vocab_size"""      ) else config.encoder.vocab_size
        __UpperCAmelCase							:			List[str]     =						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							:			Any     =						torch.jit.trace(__lowercase	,   __lowercase      )
        else:
          __UpperCAmelCase							:			Union[str, Any]     =						model

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

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

        __UpperCAmelCase							:			Optional[int]     =						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    int      )							->   Callable[[], None]:
        __UpperCAmelCase							:			str     =						self.config_dict[model_name]

        __UpperCAmelCase							:			Dict     =						(
            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							:			Union[str, Any]     =						__import__("""transformers"""	,   fromlist=[model_class]      )
            __UpperCAmelCase							:			Optional[Any]     =						getattr(__lowercase	,   __lowercase      )
            __UpperCAmelCase							:			Union[str, Any]     =						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							:			List[str]     =						MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowercase      )

        if self.args.torchscript:
          raise NotImplementedError("""Training for torchscript is currently not implemented"""      )
        else:
          __UpperCAmelCase							:			List[str]     =						model

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

        # encoder-decoder has vocab size saved differently
        __UpperCAmelCase							:			List[Any]     =						config.vocab_size if hasattr(__lowercase	,   """vocab_size"""      ) else config.encoder.vocab_size
        __UpperCAmelCase							:			Any     =						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							:			Optional[Any]     =						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							:			Tuple     =						(
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    Union[str, Any]      )							->   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(
                __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							:			List[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      :    int	,   __lowercase      :    Callable[[], None]      )							->   [Memory, MemorySummary]:
        try:
          if self.args.trace_memory_line_by_line:
            __UpperCAmelCase							:			Optional[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."""      )
              __UpperCAmelCase							:			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()
              __UpperCAmelCase							:			Optional[Any]     =						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
              __UpperCAmelCase							:			Dict     =						nvml.nvmlDeviceGetMemoryInfo(__lowercase      )
              __UpperCAmelCase							:			Optional[int]     =						meminfo.used
              __UpperCAmelCase							:			str     =						Memory(__lowercase      )
              # shutdown nvml
              nvml.nvmlShutdown()
          else:
            # cpu
            __UpperCAmelCase							:			Dict     =						measure_peak_memory_cpu(__lowercase      )
            __UpperCAmelCase							:			List[str]     =						Memory(__lowercase      ) if isinstance(__lowercase	,   __lowercase      ) else memory_bytes

          if self.args.trace_memory_line_by_line:
            __UpperCAmelCase							:			Optional[Any]     =						stop_memory_tracing(__lowercase      )
          else:
            __UpperCAmelCase							:			Any     =						None

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


