


"""simple docstring"""




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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				'focalnet'

		def __init__(  self							, _a=224							, _a=4							, _a=3							, _a=96							, _a=False							, _a=[192, 384, 768, 768]							, _a=[2, 2, 6, 2]							, _a=[2, 2, 2, 2]							, _a=[3, 3, 3, 3]							, _a="gelu"							, _a=4.0							, _a=0.0							, _a=0.1							, _a=False							, _a=1E-4							, _a=False							, _a=False							, _a=False							, _a=0.02							, _a=1E-5							, _a=32							, _a=None							, _a=None							, **_a							, ):
				super().__init__(**_a      )

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			embed_dim
				__a           =			use_conv_embed
				__a           =			hidden_sizes
				__a           =			depths
				__a           =			focal_levels
				__a           =			focal_windows
				__a           =			hidden_act
				__a           =			mlp_ratio
				__a           =			hidden_dropout_prob
				__a           =			drop_path_rate
				__a           =			use_layerscale
				__a           =			layerscale_value
				__a           =			use_post_layernorm
				__a           =			use_post_layernorm_in_modulation
				__a           =			normalize_modulator
				__a           =			initializer_range
				__a           =			layer_norm_eps
				__a           =			encoder_stride
				__a           =			['''stem'''] + [f'''stage{idx}''' for idx in range(1							, len(self.depths      ) + 1      )]
				__a      ,     __a           =			get_aligned_output_features_output_indices(
				    out_features=_a							, out_indices=_a							, stage_names=self.stage_names      )














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   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]
				lowercase__    =			[]
				lowercase__    =			[]

				for rt in rc.restypes:
								lowercase__    =			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]				)
								lowercase__    =			{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				)

				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.floataa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			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
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			restype_atomaa_mask[protein_aatype]

				lowercase__    =			residx_atomaa_mask
				lowercase__    =			residx_atomaa_to_atomaa.long()

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

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

				lowercase__    =			restype_atomaa_mask[protein_aatype]
				lowercase__    =			residx_atomaa_mask

				return protein






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			tree_map(lambda lowerCamelCase_				: torch.tensor(lowerCamelCase_		,   device=batch['''aatype'''].device				)		,   lowerCamelCase_		,   np.ndarray				)
				lowercase__    =			tensor_tree_map(lambda lowerCamelCase_				: np.array(lowerCamelCase_				)		,   make_atomaa_masks(lowerCamelCase_				)				)
				return out




