



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    ):
			A							:					List[str]              =						[]
			A							:					int              =						[]
			A							:					Dict              =						[]

			for rt in rc.restypes:
						A							:					int              =						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							:					Optional[Any]              =						{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    )

			A							:					int              =						torch.tensor(
			    _lowerCamelCase ,    dtype=torch.intaa ,    device=protein["aatype"].device ,    )
			A							:					Optional[Any]              =						torch.tensor(
			    _lowerCamelCase ,    dtype=torch.intaa ,    device=protein["aatype"].device ,    )
			A							:					str              =						torch.tensor(
			    _lowerCamelCase ,    dtype=torch.floataa ,    device=protein["aatype"].device ,    )
			A							:					Tuple              =						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							:					Optional[int]              =						restype_atomaa_to_atomaa[protein_aatype]
			A							:					Union[str, Any]              =						restype_atomaa_mask[protein_aatype]

			A							:					str              =						residx_atomaa_mask
			A							:					Union[str, Any]              =						residx_atomaa_to_atomaa.long()

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

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

			A							:					Dict              =						restype_atomaa_mask[protein_aatype]
			A							:					List[str]              =						residx_atomaa_mask

			return protein






def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					int              =						tree_map(lambda _lowerCamelCase    : torch.tensor(_lowerCamelCase ,    device=batch["aatype"].device    ) ,    _lowerCamelCase ,    np.ndarray    )
			A							:					List[Any]              =						tensor_tree_map(lambda _lowerCamelCase    : np.array(_lowerCamelCase    ) ,    make_atomaa_masks(_lowerCamelCase    )    )
			return out




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AltCLIPPreTrainedModel""",
							    """AltCLIPModel""",
							    """AltCLIPTextModel""",
							    """AltCLIPVisionModel""",
							]


if TYPE_CHECKING:
							from .configuration_altclip import (
							    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AltCLIPConfig,
							    AltCLIPTextConfig,
							    AltCLIPVisionConfig,
							)
							from .processing_altclip import AltCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_altclip import (
														    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AltCLIPModel,
														    AltCLIPPreTrainedModel,
														    AltCLIPTextModel,
														    AltCLIPVisionModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE           =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)