




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					List[str]:
     a     =				[]
     a     =				[]
     a     =				[]

     for rt in rc.restypes:
          a     =				rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
          restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
          a     =				{name: i for i, name in enumerate(_a)}
          restype_atomaa_to_atomaa_list.append(
              [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types])

          restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names])

     # Add dummy mapping for restype 'UNK'
     restype_atomaa_to_atomaa_list.append([0] * 14)
     restype_atomaa_to_atomaa_list.append([0] * 37)
     restype_atomaa_mask_list.append([0.0] * 14)

     a     =				torch.tensor(
         _a		, dtype=torch.intaa		, device=protein["aatype"].device		, )
     a     =				torch.tensor(
         _a		, dtype=torch.intaa		, device=protein["aatype"].device		, )
     a     =				torch.tensor(
         _a		, dtype=torch.floataa		, device=protein["aatype"].device		, )
     a     =				protein["""aatype"""].to(torch.long)

     # create the mapping for (residx, atom14) --> atom37, i.e. an array
     # with shape (num_res, 14) containing the atom37 indices for this protein
     a     =				restype_atomaa_to_atomaa[protein_aatype]
     a     =				restype_atomaa_mask[protein_aatype]

     a     =				residx_atomaa_mask
     a     =				residx_atomaa_to_atomaa.long()

     # create the gather indices for mapping back
     a     =				restype_atomaa_to_atomaa[protein_aatype]
     a     =				residx_atomaa_to_atomaa.long()

     # create the corresponding mask
     a     =				torch.zeros([21, 37]		, dtype=torch.floataa		, device=protein["aatype"].device)
     for restype, restype_letter in enumerate(rc.restypes):
          a     =				rc.restype_atoa[restype_letter]
          a     =				rc.residue_atoms[restype_name]
          for atom_name in atom_names:
               a     =				rc.atom_order[atom_name]
               a     =				1

     a     =				restype_atomaa_mask[protein_aatype]
     a     =				residx_atomaa_mask

     return protein





def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Optional[Any]:
     a     =				tree_map(lambda __UpperCamelCase: torch.tensor(_a		, device=batch["aatype"].device)		, _a		, np.ndarray)
     a     =				tensor_tree_map(lambda __UpperCamelCase: np.array(_a)		, make_atomaa_masks(_a))
     return out







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__				:      Tuple	   = logging.get_logger(__name__)

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




class       a__	( UpperCamelCase__       , UpperCamelCase__				):
     a		:      Any		 =     """resnet"""
     a		:      Tuple		 =     ["""basic""", """bottleneck"""]







     def __init__(     self  ,		A=3  ,		A=64  ,		A=[256, 512, 1024, 2048]  ,		A=[3, 4, 6, 3]  ,		A="bottleneck"  ,		A="relu"  ,		A=False  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Any:



          '''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__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     version.parse("""1.11"""				)







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



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-3

