from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}


class 	lowerCamelCase_     (   A_ ):






    '''simple docstring'''






    a__	:						Any						=      "efficientnet"







    def __init__(	self , __lowercase = 3 , __lowercase = 600 , __lowercase = 2.0 , __lowercase = 3.1 , __lowercase = 8 , __lowercase = [3, 3, 5, 3, 5, 5, 3] , __lowercase = [32, 16, 24, 40, 80, 112, 192] , __lowercase = [16, 24, 40, 80, 112, 192, 320] , __lowercase = [] , __lowercase = [1, 2, 2, 2, 1, 2, 1] , __lowercase = [1, 2, 2, 3, 3, 4, 1] , __lowercase = [1, 6, 6, 6, 6, 6, 6] , __lowercase = 0.25 , __lowercase = "swish" , __lowercase = 2_560 , __lowercase = "mean" , __lowercase = 0.02 , __lowercase = 0.0_01 , __lowercase = 0.99 , __lowercase = 0.5 , __lowercase = 0.2 , **__lowercase , )       ->	Union[str, Any]:
        super().__init__(**snake_case__)

        __UpperCamelCase     :List[str]			  =		num_channels
        __UpperCamelCase     :Optional[Any]			  =		image_size
        __UpperCamelCase     :Any			  =		width_coefficient
        __UpperCamelCase     :Any			  =		depth_coefficient
        __UpperCamelCase     :Optional[int]			  =		depth_divisor
        __UpperCamelCase     :Tuple			  =		kernel_sizes
        __UpperCamelCase     :Dict			  =		in_channels
        __UpperCamelCase     :Tuple			  =		out_channels
        __UpperCamelCase     :Any			  =		depthwise_padding
        __UpperCamelCase     :Optional[Any]			  =		strides
        __UpperCamelCase     :Optional[int]			  =		num_block_repeats
        __UpperCamelCase     :Optional[Any]			  =		expand_ratios
        __UpperCamelCase     :List[Any]			  =		squeeze_expansion_ratio
        __UpperCamelCase     :Any			  =		hidden_act
        __UpperCamelCase     :Dict			  =		hidden_dim
        __UpperCamelCase     :Optional[Any]			  =		pooling_type
        __UpperCamelCase     :Optional[int]			  =		initializer_range
        __UpperCamelCase     :Union[str, Any]			  =		batch_norm_eps
        __UpperCamelCase     :Dict			  =		batch_norm_momentum
        __UpperCamelCase     :Optional[Any]			  =		dropout_rate
        __UpperCamelCase     :List[str]			  =		drop_connect_rate
        __UpperCamelCase     :int			  =		sum(snake_case__) * 4


class 	lowerCamelCase_     (   A_ ):






    '''simple docstring'''






    a__	:						List[str]						=      version.parse("""1.11""" )







    @property
    def 			UpperCamelCase__   (	self)       ->	Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
            ])







    @property
    def 			UpperCamelCase__   (	self)       ->	float:
        return 1E-5







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 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   List[str]							=      []
       snake_case       :   Optional[int]							=      []
       snake_case       :   Any							=      []

       for rt in rc.restypes:
              snake_case       :   List[Any]							=      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]    )
              snake_case       :   str							=      {name: i for i, name in enumerate(__lowerCamelCase    )}
              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    )

       snake_case       :   Optional[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   List[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.floataa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      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
       snake_case       :   List[Any]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   str							=      restype_atomaa_mask[protein_aatype]

       snake_case       :   str							=      residx_atomaa_mask
       snake_case       :   Any							=      residx_atomaa_to_atomaa.long()

       # create the gather indices for mapping back
       snake_case       :   List[str]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   List[Any]							=      residx_atomaa_to_atomaa.long()

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

       snake_case       :   List[Any]							=      restype_atomaa_mask[protein_aatype]
       snake_case       :   int							=      residx_atomaa_mask

       return protein




def 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   Dict							=      tree_map(lambda __lowerCamelCase    : torch.tensor(__lowerCamelCase				,					device=batch["aatype"].device    )				,					__lowerCamelCase				,					np.ndarray    )
       snake_case       :   List[str]							=      tensor_tree_map(lambda __lowerCamelCase    : np.array(__lowerCamelCase    )				,					make_atomaa_masks(__lowerCamelCase    )    )
       return out





