





"""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 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :str											=				[]
  lowerCAmelCase__     :str											=				[]
  lowerCAmelCase__     :str											=				[]

  for rt in rc.restypes:
    lowerCAmelCase__     :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]     )
    lowerCAmelCase__     :List[str]											=				{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     )

  lowerCAmelCase__     :int											=				torch.tensor(
      _SCREAMING_SNAKE_CASE	,  dtype=torch.intaa	,  device=protein['aatype'].device	,  )
  lowerCAmelCase__     :List[Any]											=				torch.tensor(
      _SCREAMING_SNAKE_CASE	,  dtype=torch.intaa	,  device=protein['aatype'].device	,  )
  lowerCAmelCase__     :List[str]											=				torch.tensor(
      _SCREAMING_SNAKE_CASE	,  dtype=torch.floataa	,  device=protein['aatype'].device	,  )
  lowerCAmelCase__     :Dict											=				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__     :Tuple											=				restype_atomaa_to_atomaa[protein_aatype]
  lowerCAmelCase__     :Optional[Any]											=				restype_atomaa_mask[protein_aatype]

  lowerCAmelCase__     :List[str]											=				residx_atomaa_mask
  lowerCAmelCase__     :Tuple											=				residx_atomaa_to_atomaa.long()

  # create the gather indices for mapping back
  lowerCAmelCase__     :int											=				restype_atomaa_to_atomaa[protein_aatype]
  lowerCAmelCase__     :Union[str, Any]											=				residx_atomaa_to_atomaa.long()

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

  lowerCAmelCase__     :Optional[int]											=				restype_atomaa_mask[protein_aatype]
  lowerCAmelCase__     :Optional[Any]											=				residx_atomaa_mask

  return protein




def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Dict:





  """simple docstring"""


  lowerCAmelCase__     :Tuple											=				tree_map(lambda _SCREAMING_SNAKE_CASE     : torch.tensor(_SCREAMING_SNAKE_CASE	,  device=batch['aatype'].device     )	,  _SCREAMING_SNAKE_CASE	,  np.ndarray     )
  lowerCAmelCase__     :List[Any]											=				tensor_tree_map(lambda _SCREAMING_SNAKE_CASE     : np.array(_SCREAMING_SNAKE_CASE     )	,  make_atomaa_masks(_SCREAMING_SNAKE_CASE     )     )
  return out






'''simple docstring'''


from typing import TYPE_CHECKING

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


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

				_snake_case : Any  				=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

