





'''simple docstring'''

# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_					= {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """CpmAntForCausalLM""",
          """CpmAntModel""",
          """CpmAntPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
      from .tokenization_cpmant import CpmAntTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_cpmant import (
                CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
                CpmAntForCausalLM,
                CpmAntModel,
                CpmAntPreTrainedModel,
            )


else:
      import sys

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







'''simple docstring'''


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 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				_SCREAMING_SNAKE_CASE :       Dict     		=      []
				_SCREAMING_SNAKE_CASE :       str     		=      []

				for rt in rc.restypes:
								_SCREAMING_SNAKE_CASE :       Optional[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]     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {name: i for i, name in enumerate(SCREAMING_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     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.floataa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_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
				_SCREAMING_SNAKE_CASE :       List[str]     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       str     		=      restype_atomaa_mask[protein_aatype]

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      residx_atomaa_mask
				_SCREAMING_SNAKE_CASE :       List[str]     		=      residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       Dict     		=      residx_atomaa_to_atomaa.long()

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

				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_mask[protein_aatype]
				_SCREAMING_SNAKE_CASE :       int     		=      residx_atomaa_mask

				return protein







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tree_map(lambda SCREAMING_SNAKE_CASE__     : torch.tensor(SCREAMING_SNAKE_CASE__							,      device=batch["""aatype"""].device     )							,      SCREAMING_SNAKE_CASE__							,      np.ndarray     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tensor_tree_map(lambda SCREAMING_SNAKE_CASE__     : np.array(SCREAMING_SNAKE_CASE__     )							,      make_atomaa_masks(SCREAMING_SNAKE_CASE__     )     )
				return out




