






'''simple docstring'''



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


__UpperCamelCase      :       Tuple      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Optional[int]      			=      {
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase     ):
      __a  					="bit"
      __a  					=["preactivation", "bottleneck"]
      __a  					=["SAME", "VALID"]




      def __init__(  self ,   lowerCamelCase=3 ,   lowerCamelCase=64 ,   lowerCamelCase=[256, 512, 1024, 2048] ,   lowerCamelCase=[3, 4, 6, 3] ,   lowerCamelCase="preactivation" ,   lowerCamelCase="relu" ,   lowerCamelCase=None ,   lowerCamelCase=32 ,   lowerCamelCase=0.0 ,   lowerCamelCase=False ,   lowerCamelCase=32 ,   lowerCamelCase=1 ,   lowerCamelCase=None ,   lowerCamelCase=None ,   **lowerCamelCase ,   )							->List[str]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         if layer_type not in self.layer_types:
            raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types      )}"""      )
         if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
               __a				     =    global_padding.upper()
            else:
               raise ValueError(F"""Padding strategy {global_padding} not supported"""      )
         __a				     =    num_channels
         __a				     =    embedding_size
         __a				     =    hidden_sizes
         __a				     =    depths
         __a				     =    layer_type
         __a				     =    hidden_act
         __a				     =    global_padding
         __a				     =    num_groups
         __a				     =    drop_path_rate
         __a				     =    embedding_dynamic_padding
         __a				     =    output_stride
         __a				     =    width_factor

         __a				     =    ['stem'] + [F"""stage{idx}""" for idx in range(1 ,   len(lowerCamelCase      ) + 1      )]
         __a     ,			__a				     =    get_aligned_output_features_output_indices(
             out_features=lowerCamelCase ,   out_indices=lowerCamelCase ,   stage_names=self.stage_names      )







'''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      :       Optional[Any]      			=      logging.get_logger(__name__)
@dataclass
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]




      def __init__(  self ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               __a				     =    deprecated_arg[3:]
               setattr(self ,   lowerCamelCase ,   not kwargs.pop(lowerCamelCase      )      )
               logger.warning(
                   F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   F""" {positive_arg}={kwargs[positive_arg]}"""      )

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

      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Trace the models using torchscript"}     )
      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Print Xla/PyTorch tpu metrics"}     )
      __a  					=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  __UpperCamelCase							(  self      )							->Tuple["torch.device", int]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         logger.info('PyTorch: setting up devices'      )
         if not self.cuda:
            __a				     =    torch.device('cpu'      )
            __a				     =    0
         elif is_torch_tpu_available():
            __a				     =    xm.xla_device()
            __a				     =    0
         else:
            __a				     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
            __a				     =    torch.cuda.device_count()
         return device, n_gpu




      @property
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         return is_torch_tpu_available() and self.tpu




      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()




      @property
      def  __UpperCamelCase							(  self      )							->"torch.device":



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[0]




      @property
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[1]






      @property
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         return self.n_gpu > 0