


'''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       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				str       =							[]
       _lowerCAmelCase     :				Optional[Any]       =							[]
       _lowerCAmelCase     :				Union[str, Any]       =							[]

       for rt in rc.restypes:
              _lowerCAmelCase     :				Tuple       =							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]       )
              _lowerCAmelCase     :				Union[str, Any]       =							{name: i for i, name in enumerate(_A       )}
              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] * 1_4       )
       restype_atomaa_to_atomaa_list.append([0] * 3_7       )
       restype_atomaa_mask_list.append([0.0] * 1_4       )

       _lowerCAmelCase     :				Dict       =							torch.tensor(
           _A ,    dtype=torch.intaa ,    device=protein['aatype'].device ,    )
       _lowerCAmelCase     :				Union[str, Any]       =							torch.tensor(
           _A ,    dtype=torch.intaa ,    device=protein['aatype'].device ,    )
       _lowerCAmelCase     :				Any       =							torch.tensor(
           _A ,    dtype=torch.floataa ,    device=protein['aatype'].device ,    )
       _lowerCAmelCase     :				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
       _lowerCAmelCase     :				Union[str, Any]       =							restype_atomaa_to_atomaa[protein_aatype]
       _lowerCAmelCase     :				Dict       =							restype_atomaa_mask[protein_aatype]

       _lowerCAmelCase     :				Any       =							residx_atomaa_mask
       _lowerCAmelCase     :				Optional[int]       =							residx_atomaa_to_atomaa.long()

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

       # create the corresponding mask
       _lowerCAmelCase     :				Any       =							torch.zeros([2_1, 3_7] ,    dtype=torch.floataa ,    device=protein['aatype'].device       )
       for restype, restype_letter in enumerate(rc.restypes       ):
              _lowerCAmelCase     :				Optional[int]       =							rc.restype_atoa[restype_letter]
              _lowerCAmelCase     :				List[str]       =							rc.residue_atoms[restype_name]
              for atom_name in atom_names:
                     _lowerCAmelCase     :				Any       =							rc.atom_order[atom_name]
                     _lowerCAmelCase     :				Dict       =							1

       _lowerCAmelCase     :				Any       =							restype_atomaa_mask[protein_aatype]
       _lowerCAmelCase     :				List[str]       =							residx_atomaa_mask

       return protein






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							tree_map(lambda _A       : torch.tensor(_A ,    device=batch['aatype'].device       ) ,    _A ,    np.ndarray       )
       _lowerCAmelCase     :				Optional[Any]       =							tensor_tree_map(lambda _A       : np.array(_A       ) ,    make_atomaa_masks(_A       )       )
       return out





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase     :  List[str]											=    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    ["""ElectraTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  str											=    [
           """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ElectraForCausalLM""",
           """ElectraForMaskedLM""",
           """ElectraForMultipleChoice""",
           """ElectraForPreTraining""",
           """ElectraForQuestionAnswering""",
           """ElectraForSequenceClassification""",
           """ElectraForTokenClassification""",
           """ElectraModel""",
           """ElectraPreTrainedModel""",
           """load_tf_weights_in_electra""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFElectraForMaskedLM""",
           """TFElectraForMultipleChoice""",
           """TFElectraForPreTraining""",
           """TFElectraForQuestionAnswering""",
           """TFElectraForSequenceClassification""",
           """TFElectraForTokenClassification""",
           """TFElectraModel""",
           """TFElectraPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Union[str, Any]											=    [
           """FlaxElectraForCausalLM""",
           """FlaxElectraForMaskedLM""",
           """FlaxElectraForMultipleChoice""",
           """FlaxElectraForPreTraining""",
           """FlaxElectraForQuestionAnswering""",
           """FlaxElectraForSequenceClassification""",
           """FlaxElectraForTokenClassification""",
           """FlaxElectraModel""",
           """FlaxElectraPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

       lowerCAmelCase     :  int											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

