





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
     import tensorflow as tf

     from ..tf_utils import stable_softmax


if is_torch_available():
     import torch


_snake_case  :   str						   =  logging.get_logger(__name__)



@add_end_docstrings(
    _UpperCamelCase		,   R"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """		,   )
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		GenericTensor					)							->						np.ndarray:
     if self.framework == "tf":
        __lowerCAmelCase							     =     tf.where(input_ids == self.tokenizer.mask_token_id					).numpy()
     elif self.framework == "pt":
        __lowerCAmelCase							     =     torch.nonzero(input_ids == self.tokenizer.mask_token_id ,  as_tuple=lowerCAmelCase_					)
     else:
        raise ValueError('Unsupported framework'					)
     return masked_index






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		GenericTensor					)							->						np.ndarray:
     __lowerCAmelCase							     =     self.get_masked_index(lowerCAmelCase_					)
     __lowerCAmelCase							     =     np.prod(masked_index.shape					)
     if numel < 1:
        raise PipelineException(
            'fill-mask' ,  self.model.base_model_prefix ,  f"""No mask_token ({self.tokenizer.mask_token}) found on the input""" ,  )






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		GenericTensor					)							->						Optional[Any]:
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        for model_input in model_inputs:
           self._ensure_exactly_one_mask_token(model_input['input_ids'][0]					)
     else:
        for input_ids in model_inputs["input_ids"]:
           self._ensure_exactly_one_mask_token(lowerCAmelCase_					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		List[Any]=None ,  **lowerCAmelCase_			:		Dict					)							->						Dict[str, GenericTensor]:
     if return_tensors is None:
        __lowerCAmelCase							     =     self.framework
     __lowerCAmelCase							     =     self.tokenizer(lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_					)
     self.ensure_exactly_one_mask_token(lowerCAmelCase_					)
     return model_inputs






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Any					)							->						List[str]:
     __lowerCAmelCase							     =     self.model(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     model_inputs['input_ids']
     return model_outputs






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Tuple=5 ,  lowerCAmelCase_			:		Tuple=None					)							->						List[Any]:
     # Cap top_k if there are targets
     if target_ids is not None and target_ids.shape[0] < top_k:
        __lowerCAmelCase							     =     target_ids.shape[0]
     __lowerCAmelCase							     =     model_outputs['input_ids'][0]
     __lowerCAmelCase							     =     model_outputs['logits']

     if self.framework == "tf":
        __lowerCAmelCase							     =     tf.where(input_ids == self.tokenizer.mask_token_id					).numpy()[:, 0]

        __lowerCAmelCase							     =     outputs.numpy()

        __lowerCAmelCase							     =     outputs[0, masked_index, :]
        __lowerCAmelCase							     =     stable_softmax(lowerCAmelCase_ ,  axis=-1					)
        if target_ids is not None:
           __lowerCAmelCase							     =     tf.gather_nd(tf.squeeze(lowerCAmelCase_ ,  0					) ,  target_ids.reshape(-1 ,  1					)					)
           __lowerCAmelCase							     =     tf.expand_dims(lowerCAmelCase_ ,  0					)

        __lowerCAmelCase							     =     tf.math.top_k(lowerCAmelCase_ ,  k=lowerCAmelCase_					)
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     topk.values.numpy(), topk.indices.numpy()
     else:
        __lowerCAmelCase							     =     torch.nonzero(input_ids == self.tokenizer.mask_token_id ,  as_tuple=lowerCAmelCase_					).squeeze(-1					)
        # Fill mask pipeline supports only one ${mask_token} per sample

        __lowerCAmelCase							     =     outputs[0, masked_index, :]
        __lowerCAmelCase							     =     logits.softmax(dim=-1					)
        if target_ids is not None:
           __lowerCAmelCase							     =     probs[..., target_ids]

        __lowerCAmelCase     ,     __lowerCAmelCase							     =     probs.topk(lowerCAmelCase_					)

     __lowerCAmelCase							     =     []
     __lowerCAmelCase							     =     values.shape[0] == 1
     for i, (_values, _predictions) in enumerate(zip(values.tolist() ,  predictions.tolist()					)					):
        __lowerCAmelCase							     =     []
        for v, p in zip(_values ,  _predictions					):
           # Copy is important since we're going to modify this array in place
           __lowerCAmelCase							     =     input_ids.numpy().copy()
           if target_ids is not None:
              __lowerCAmelCase							     =     target_ids[p].tolist()

           __lowerCAmelCase							     =     p
           # Filter padding out:
           __lowerCAmelCase							     =     tokens[np.where(tokens != self.tokenizer.pad_token_id					)]
           # Originally we skip special tokens to give readable output.
           # For multi masks though, the other [MASK] would be removed otherwise
           # making the output look odd, so we add them back
           __lowerCAmelCase							     =     self.tokenizer.decode(lowerCAmelCase_ ,  skip_special_tokens=lowerCAmelCase_					)
           __lowerCAmelCase							     =     {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]					), 'sequence': sequence}
           row.append(lowerCAmelCase_					)
        result.append(lowerCAmelCase_					)
     if single_mask:
        return result[0]
     return result






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Tuple=None					)							->						List[str]:
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [targets]
     try:
        __lowerCAmelCase							     =     self.tokenizer.get_vocab()
     except Exception:
        __lowerCAmelCase							     =     {}
     __lowerCAmelCase							     =     []
     for target in targets:
        __lowerCAmelCase							     =     vocab.get(lowerCAmelCase_ ,  lowerCAmelCase_					)
        if id_ is None:
           __lowerCAmelCase							     =     self.tokenizer(
               lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_ ,  max_length=1 ,  truncation=lowerCAmelCase_ ,  )['input_ids']
           if len(lowerCAmelCase_					) == 0:
              logger.warning(
                  f"""The specified target token `{target}` does not exist in the model vocabulary. """
                  'We cannot replace it with anything meaningful, ignoring it'					)
              continue
           __lowerCAmelCase							     =     input_ids[0]
           # XXX: If users encounter this pass
           # it becomes pretty slow, so let's make sure
           # The warning enables them to fix the input to
           # get faster performance.
           logger.warning(
               f"""The specified target token `{target}` does not exist in the model vocabulary. """
               f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_					)}`."""					)
        target_ids.append(id_					)
     __lowerCAmelCase							     =     list(set(lowerCAmelCase_					)					)
     if len(lowerCAmelCase_					) == 0:
        raise ValueError('At least one target must be provided when passed.'					)
     __lowerCAmelCase							     =     np.array(lowerCAmelCase_					)
     return target_ids






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Tuple=None ,  lowerCAmelCase_			:		Tuple=None					)							->						Dict:
     __lowerCAmelCase							     =     {}

     if targets is not None:
        __lowerCAmelCase							     =     self.get_target_ids(lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     target_ids

     if top_k is not None:
        __lowerCAmelCase							     =     top_k

     if self.tokenizer.mask_token_id is None:
        raise PipelineException(
            'fill-mask' ,  self.model.base_model_prefix ,  'The tokenizer does not define a `mask_token`.'					)
     return {}, {}, postprocess_params






  def __call__(    self			:		str ,  lowerCAmelCase_			:		int ,  *lowerCAmelCase_			:		Optional[int] ,  **lowerCAmelCase_			:		str					)							->						Any:
     __lowerCAmelCase							     =     super().__call__(lowerCAmelCase_ ,  **lowerCAmelCase_					)
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) and len(lowerCAmelCase_					) == 1:
        return outputs[0]
     return outputs








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_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []

   for rt in rc.restypes:
      __lowerCAmelCase							     =     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							     =     {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     )

   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.floataa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     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							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]

   __lowerCAmelCase							     =     residx_atomaa_mask
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

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

   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_mask

   return protein




def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     tree_map(lambda lowerCAmelCase_     : torch.tensor(lowerCAmelCase_,     device=batch['aatype'].device     ),     lowerCAmelCase_,     np.ndarray     )
   __lowerCAmelCase							     =     tensor_tree_map(lambda lowerCAmelCase_     : np.array(lowerCAmelCase_     ),     make_atomaa_masks(lowerCAmelCase_     )     )
   return out

