
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						(							snake_case__:					Dict[str, torch.Tensor]					):


				'''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(snake_case__					)}
								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(
				    snake_case__						,   dtype=torch.intaa						,   device=protein['''aatype'''].device						,   )
				lowercase_       =					torch.tensor(
				    snake_case__						,   dtype=torch.intaa						,   device=protein['''aatype'''].device						,   )
				lowercase_       =					torch.tensor(
				    snake_case__						,   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						(							snake_case__:					Dict[str, torch.Tensor]					):


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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




