
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


import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_snake_case(  *SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=2				) ->    Optional[Any]:
       from .. import __version__

       lowercase							:     int       	=      take_from
       lowercase							:     Tuple       	=      ()
       if not isinstance(args[0]					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(SCREAMING_SNAKE_CASE__				).base_version				) >= version.parse(SCREAMING_SNAKE_CASE__				):
                     raise ValueError(
                         f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                         f" version {__version__} is >= {version_name}"				)

              lowercase							:     int       	=      None
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     Union[str, Any]       	=      f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
              elif hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     values += (getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     int       	=      f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
              elif deprecated_kwargs is None:
                     lowercase							:     Dict       	=      f"`{attribute}` is deprecated and will be removed in version {version_name}."

              if warning is not None:
                     lowercase							:     Dict       	=      warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message					,	SCREAMING_SNAKE_CASE__					,	stacklevel=SCREAMING_SNAKE_CASE__				)

       if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     str       	=      inspect.getouterframes(inspect.currentframe()				)[1]
              lowercase							:     List[str]       	=      call_frame.filename
              lowercase							:     Tuple       	=      call_frame.lineno
              lowercase							:     List[str]       	=      call_frame.function
              lowercase	,			lowercase							:     Optional[Any]       	=      next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"				)

       if len(SCREAMING_SNAKE_CASE__				) == 0:
              return
       elif len(SCREAMING_SNAKE_CASE__				) == 1:
              return values[0]
       return values
