
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase  :					int						    =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None				) ->    int:
       return field(default_factory=lambda: default					,	metadata=SCREAMING_SNAKE_CASE__				)




@dataclass
class __snake_case		:
    _a		:						List[str]=				list_field(
        default=[]					,	metadata={
            "help": (
                "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                " of all available models"
            )
        }					,	)

    _a		:						List[int]=				list_field(
        default=[8]					,	metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}     )

    _a		:						List[int]=				list_field(
        default=[8, 32, 128, 512]					,	metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}					,	)

    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Use FP16 to accelerate inference."}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Benchmark training of model"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Verbose memory tracing"}     )
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={
            "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
        }					,	)
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Trace memory line by line"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Save result to a CSV file"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Save all print statements in a log file"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Whether to print environment information"}     )
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={
            "help": (
                "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                " for debugging / testing and on TPU."
            )
        }					,	)
    _a		:						str=				field(
        default=f"""inference_time_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving time results to csv."}					,	)
    _a		:						str=				field(
        default=f"""inference_memory_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving memory results to csv."}					,	)
    _a		:						str=				field(
        default=f"""train_time_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving time results to csv for training."}					,	)
    _a		:						str=				field(
        default=f"""train_memory_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving memory results to csv for training."}					,	)
    _a		:						str=				field(
        default=f"""env_info_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving environment information."}					,	)
    _a		:						str=				field(
        default=f"""log_{round(time()     )}.csv"""					,	metadata={"help": "Log filename used if print statements are saved in log."}					,	)
    _a		:						int=				field(default=3					,	metadata={"help": "Times an experiment will be run."}     )
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={
            "help": (
                "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                " model weights."
            )
        }					,	)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           warnings.warn(
               f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""		,snake_case		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return json.dumps(dataclasses.asdict(self      )		,indent=2      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if len(self.models      ) <= 0:
                  raise ValueError(
                      """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                      """ bert-base-cased` or `args.models = ['bert-base-cased']."""      )
           return self.models
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if not self.multi_process:
                  return False
           elif self.is_tpu:
                  logger.info("""Multiprocessing is currently not possible on TPU."""      )
                  return False
           else:
                  return True







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NllbMoeForConditionalGeneration''',
     '''NllbMoeModel''',
     '''NllbMoePreTrainedModel''',
     '''NllbMoeTop2Router''',
     '''NllbMoeSparseMLP''',
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 lowerCAmelCase					:     Tuple												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


