
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 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				PyTorchBenchmarkArguments
     UpperCAmelCase__							:				PretrainedConfig
     UpperCAmelCase__							:				str 	=						"PyTorch"





     @property
     def 						UpperCAmelCase(self :       Tuple  )		->		Optional[Any]:
         return torch.__version__





     def 						UpperCAmelCase(self :       str							,       _A :       str							,       _A :       int							,       _A :       int  )		->		float:
         snake_case						=   self._prepare_inference_func(_A							,       _A							,       _A  )
         return self._measure_speed(_inference  )





     def 						UpperCAmelCase(self :       Dict							,       _A :       str							,       _A :       int							,       _A :       int  )		->		[Memory, Optional[MemorySummary]]:
         snake_case						=   self._prepare_inference_func(_A							,       _A							,       _A  )
         return self._measure_memory(_inference  )





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       str							,       _A :       int							,       _A :       int  )		->		float:
         snake_case						=   self._prepare_train_func(_A							,       _A							,       _A  )
         return self._measure_speed(_train  )





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       str							,       _A :       int							,       _A :       int  )		->		[Memory, Optional[MemorySummary]]:
         snake_case						=   self._prepare_train_func(_A							,       _A							,       _A  )
         return self._measure_memory(_train  )





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       str							,       _A :       int							,       _A :       int  )		->		Callable[[], None]:
         snake_case						=   self.config_dict[model_name]

         if self.args.torchscript:
             snake_case						=   True

         snake_case						=   (
             hasattr(_A							,       "architectures"  )
             and isinstance(config.architectures							,       _A  )
             and len(config.architectures  ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 snake_case						=   config.architectures[0]
                 snake_case						=   __import__("transformers"							,       fromlist=[model_class]  )
                 snake_case						=   getattr(_A							,       _A  )
                 snake_case						=   model_cls(_A  )
             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:
             snake_case						=   MODEL_MAPPING[config.__class__](_A  )

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

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

         def encoder_decoder_forward():
             with torch.no_grad():
                 snake_case						=   inference_model(_A							,       decoder_input_ids=_A  )
             return outputs

         def encoder_forward():
             with torch.no_grad():
                 snake_case						=   inference_model(_A  )
             return outputs

         snake_case						=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
         return _forward





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       str							,       _A :       int							,       _A :       int  )		->		Callable[[], None]:
         snake_case						=   self.config_dict[model_name]

         snake_case						=   (
             hasattr(_A							,       "architectures"  )
             and isinstance(config.architectures							,       _A  )
             and len(config.architectures  ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 snake_case						=   config.architectures[0]
                 snake_case						=   __import__("transformers"							,       fromlist=[model_class]  )
                 snake_case						=   getattr(_A							,       _A  )
                 snake_case						=   model_cls(_A  )
             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:
             snake_case						=   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_A  )

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

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

         # encoder-decoder has vocab size saved differently
         snake_case						=   config.vocab_size if hasattr(_A							,       "vocab_size"  ) else config.encoder.vocab_size
         snake_case						=   torch.randint(_A							,       (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():
             snake_case						=   train_model(_A							,       labels=_A  )[0]
             loss.backward()
             return loss

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

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





     def 						UpperCAmelCase(self :       List[str]							,       _A :       List[str]  )		->		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(
                     _A							,       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
             snake_case						=   timeit.repeat(
                 _A							,       repeat=self.args.repeat							,       number=1_0							,       )

             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(_A  ) / 10.0
         except RuntimeError as e:
             self.print_fn(f'Doesn\'t fit on GPU. {e}'  )
             return "N/A"





     def 						UpperCAmelCase(self :       Any							,       _A :       Callable[[], None]  )		->		[Memory, MemorySummary]:
         try:
             if self.args.trace_memory_line_by_line:
                 snake_case						=   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."  )
                     snake_case						=   "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()
                     snake_case						=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
                     snake_case						=   nvml.nvmlDeviceGetMemoryInfo(_A  )
                     snake_case						=   meminfo.used
                     snake_case						=   Memory(_A  )
                     # shutdown nvml
                     nvml.nvmlShutdown()
             else:
                 # cpu
                 snake_case						=   measure_peak_memory_cpu(_A  )
                 snake_case						=   Memory(_A  ) if isinstance(_A							,       _A  ) else memory_bytes

             if self.args.trace_memory_line_by_line:
                 snake_case						=   stop_memory_tracing(_A  )
             else:
                 snake_case						=   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_tokenizers_available, is_torch_available


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

   _A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

