from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
    import torch

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm


_lowercase							:      Optional[Any]      							=logging.get_logger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Any            =				[
      "no_inference",
      "no_cuda",
      "no_tpu",
      "no_speed",
      "no_memory",
      "no_env_print",
      "no_multi_process",
  ]







  def __init__(				self    :					List[str]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				str:
         for deprecated_arg in self.deprecated_args:
                if deprecated_arg in kwargs:
                       A						:     str         =deprecated_arg[3:]
                       setattr(self      ,	SCREAMING_SNAKE_CASE__      ,	not kwargs.pop(SCREAMING_SNAKE_CASE__     )     )
                       logger.warning(
                           f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
                           f' {positive_arg}={kwargs[positive_arg]}'     )

         A						:     Dict         =kwargs.pop('torchscript'      ,	self.torchscript     )
         A						:     List[str]         =kwargs.pop('torch_xla_tpu_print_metrics'      ,	self.torch_xla_tpu_print_metrics     )
         A						:     Union[str, Any]         =kwargs.pop('fp16_opt_level'      ,	self.fpaa_opt_level     )
         super().__init__(**SCREAMING_SNAKE_CASE__     )

  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Trace the models using torchscript"}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Print Xla/PyTorch tpu metrics"}			)
  lowercase		:     str            =				field(
      default="O1"   ,						metadata={
          "help": (
              "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
              "See details at https://nvidia.github.io/apex/amp.html"
          )
      }   ,						)







  @cached_property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Tuple["torch.device", int]:
         requires_backends(self      ,	['torch']     )
         logger.info('PyTorch: setting up devices'     )
         if not self.cuda:
                A						:     List[str]         =torch.device('cpu'     )
                A						:     Union[str, Any]         =0
         elif is_torch_tpu_available():
                A						:     Union[str, Any]         =xm.xla_device()
                A						:     Union[str, Any]         =0
         else:
                A						:     List[str]         =torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )
                A						:     Tuple         =torch.cuda.device_count()
         return device, n_gpu







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Union[str, Any]:
         return is_torch_tpu_available() and self.tpu







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				int:
         requires_backends(self      ,	['torch']     )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				"torch.device":
         requires_backends(self      ,	['torch']     )
         return self._setup_devices[0]







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         requires_backends(self      ,	['torch']     )
         return self._setup_devices[1]







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[int]:
         return self.n_gpu > 0







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

from packaging import version



def A__  (			*lowercase:     Tuple,		lowercase:     Optional[Union[Dict, Any]] = None,		lowercase:     Dict=True,		lowercase:     Any=2					)				->		List[Any]:
       from .. import __version__

       A						:     Optional[Any]         =take_from
       A						:     Union[str, Any]         =()
       if not isinstance(args[0],		lowercase					):
              A						:     List[str]         =(args,)

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

              A						:     Tuple         =None
              if isinstance(lowercase,		lowercase					) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowercase					),)
                     A						:     Union[str, Any]         =F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(lowercase,		lowercase					):
                     values += (getattr(lowercase,		lowercase					),)
                     A						:     Optional[Any]         =F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     A						:     List[Any]         =F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     A						:     List[Any]         =warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message,		lowercase,		stacklevel=lowercase					)

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

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






