"""simple docstring"""


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


UpperCAmelCase__  		=						logging.get_logger(__name__)


@dataclass
class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Optional[Any]     							=							[
        '''no_inference''',
        '''no_cuda''',
        '''no_tpu''',
        '''no_speed''',
        '''no_memory''',
        '''no_env_print''',
        '''no_multi_process''',
    ]


    def __init__(					self	:  str   ,				**__lowerCAmelCase	:  int					):
      for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
          _UpperCAmelCase						 =      deprecated_arg[3:]
          setattr(self   ,				A__   ,				not kwargs.pop(A__					)					)
          logger.warning(
              f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
              f''' {positive_arg}={kwargs[positive_arg]}'''					)

      _UpperCAmelCase						 =      kwargs.pop("""torchscript"""   ,				self.torchscript					)
      _UpperCAmelCase						 =      kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics					)
      _UpperCAmelCase						 =      kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level					)
      super().__init__(**A__					)

    _snake_case     :  bool     							=							field(default=lowerCAmelCase_	,	metadata={'help': 'Trace the models using torchscript'}   )
    _snake_case     :  bool     							=							field(default=lowerCAmelCase_	,	metadata={'help': 'Print Xla/PyTorch tpu metrics'}   )
    _snake_case     :  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      lowerCAmelCase_  (					self	:  List[str]					):
      requires_backends(self   ,				["""torch"""]					)
      logger.info("""PyTorch: setting up devices"""					)
      if not self.cuda:
        _UpperCAmelCase						 =      torch.device("""cpu"""					)
        _UpperCAmelCase						 =      0
      elif is_torch_tpu_available():
        _UpperCAmelCase						 =      xm.xla_device()
        _UpperCAmelCase						 =      0
      else:
        _UpperCAmelCase						 =      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
        _UpperCAmelCase						 =      torch.cuda.device_count()
      return device, n_gpu


    @property
    def      lowerCAmelCase_  (					self	:  int					):
      return is_torch_tpu_available() and self.tpu


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


    @property
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      requires_backends(self   ,				["""torch"""]					)
      return self._setup_devices[0]


    @property
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      requires_backends(self   ,				["""torch"""]					)
      return self._setup_devices[1]


    @property
    def      lowerCAmelCase_  (					self	:  List[str]					):
      return self.n_gpu > 0

"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  int   ,				*__lowerCAmelCase	:  List[str]   ,				**__lowerCAmelCase	:  List[str]					):
      warnings.warn(
          """The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ChineseCLIPImageProcessor instead."""   ,				__lowerCAmelCase   ,				)
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
