

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


_lowerCamelCase							       =logging.get_logger(__name__)



@dataclass
class 		a_       (							lowerCamelCase_     ):



      """simple docstring"""







      __UpperCAmelCase					=      [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]
      def __init__(      self     :							int			,**snake_case     :							Union[str, Any]				):



           for deprecated_arg in self.deprecated_args:
                if deprecated_arg in kwargs:
                     SCREAMING_SNAKE_CASE      						=deprecated_arg[3:]
                     setattr(self			,snake_case			,not kwargs.pop(snake_case				)				)
                     logger.warning(
                         f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
                         f' {positive_arg}={kwargs[positive_arg]}'				)

           SCREAMING_SNAKE_CASE      						=kwargs.pop('torchscript'			,self.torchscript				)
           SCREAMING_SNAKE_CASE      						=kwargs.pop('torch_xla_tpu_print_metrics'			,self.torch_xla_tpu_print_metrics				)
           SCREAMING_SNAKE_CASE      						=kwargs.pop('fp16_opt_level'			,self.fpaa_opt_level				)
           super().__init__(**snake_case				)

      __UpperCAmelCase					=      field(default=lowerCamelCase_ ,			metadata={'help': 'Trace the models using torchscript'}     )
      __UpperCAmelCase					=      field(default=lowerCamelCase_ ,			metadata={'help': 'Print Xla/PyTorch tpu metrics'}     )
      __UpperCAmelCase					=      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     :							str				):
           requires_backends(self			,['torch']				)
           logger.info('PyTorch: setting up devices'				)
           if not self.cuda:
                SCREAMING_SNAKE_CASE      						=torch.device('cpu'				)
                SCREAMING_SNAKE_CASE      						=0
           elif is_torch_tpu_available():
                SCREAMING_SNAKE_CASE      						=xm.xla_device()
                SCREAMING_SNAKE_CASE      						=0
           else:
                SCREAMING_SNAKE_CASE      						=torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
                SCREAMING_SNAKE_CASE      						=torch.cuda.device_count()
           return device, n_gpu
      @property
      def        _lowerCAmelCase     (      self     :							Tuple				):
           return is_torch_tpu_available() and self.tpu
      @property
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           requires_backends(self			,['torch']				)
           # TODO(PVP): currently only single GPU is supported
           return torch.cuda.current_device()
      @property
      def        _lowerCAmelCase     (      self     :							Dict				):
           requires_backends(self			,['torch']				)
           return self._setup_devices[0]
      @property
      def        _lowerCAmelCase     (      self     :							int				):
           requires_backends(self			,['torch']				)
           return self._setup_devices[1]


      @property
      def        _lowerCAmelCase     (      self     :							Any				):
           return self.n_gpu > 0






'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



