


'''simple docstring'''






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


__A					:  Tuple											=      logging.get_logger(__name__)






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







    """simple docstring"""



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

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  GenericTensor		)   ->  np.ndarray:
      lowerCAmelCase_			:      Union[str, Any]			=   self.get_masked_index(lowerCamelCase		)
      lowerCAmelCase_			:      Union[str, Any]			=   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		)   ->  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    :  Optional[int]    ,						lowerCamelCase    :  Any=None    ,						**lowerCamelCase    :  int		)   ->  Dict[str, GenericTensor]:
      if return_tensors is None:
        lowerCAmelCase_			:      Any			=   self.framework
      lowerCAmelCase_			:      Union[str, Any]			=   self.tokenizer(lowerCamelCase    ,						return_tensors=lowerCamelCase		)
      self.ensure_exactly_one_mask_token(lowerCamelCase		)
      return model_inputs

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  List[str]		)   ->  Tuple:
      lowerCAmelCase_			:      Union[str, Any]			=   self.model(**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   model_inputs["""input_ids"""]
      return model_outputs

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

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

        lowerCAmelCase_			:      Union[str, Any]			=   outputs.numpy()

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

        lowerCAmelCase_			:      Optional[Any]			=   tf.math.top_k(lowerCamelCase    ,						k=lowerCamelCase		)
        lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   topk.values.numpy(), topk.indices.numpy()
      else:
        lowerCAmelCase_			:      Optional[Any]			=   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_			:      List[str]			=   outputs[0, masked_index, :]
        lowerCAmelCase_			:      str			=   logits.softmax(dim=-1		)
        if target_ids is not None:
          lowerCAmelCase_			:      Optional[int]			=   probs[..., target_ids]

        lowerCAmelCase_,							lowerCAmelCase_			:      int			=   probs.topk(lowerCamelCase		)

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

          lowerCAmelCase_			:      List[Any]			=   p
          # Filter padding out:
          lowerCAmelCase_			:      List[Any]			=   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_			:      Union[str, Any]			=   self.tokenizer.decode(lowerCamelCase    ,						skip_special_tokens=lowerCamelCase		)
          lowerCAmelCase_			:      int			=   {"""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    :  Any    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  Optional[Any]=None		)   ->  int:
      if isinstance(lowerCamelCase    ,						lowerCamelCase		):
        lowerCAmelCase_			:      Optional[int]			=   [targets]
      try:
        lowerCAmelCase_			:      Any			=   self.tokenizer.get_vocab()
      except Exception:
        lowerCAmelCase_			:      int			=   {}
      lowerCAmelCase_			:      Union[str, Any]			=   []
      for target in targets:
        lowerCAmelCase_			:      Tuple			=   vocab.get(lowerCamelCase    ,						lowerCamelCase		)
        if id_ is None:
          lowerCAmelCase_			:      Optional[Any]			=   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_			:      Any			=   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_			:      Optional[Any]			=   list(set(lowerCamelCase		)		)
      if len(lowerCamelCase		) == 0:
        raise ValueError("""At least one target must be provided when passed."""		)
      lowerCAmelCase_			:      List[str]			=   np.array(lowerCamelCase		)
      return target_ids

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  int=None    ,						lowerCamelCase    :  str=None		)   ->  List[str]:
      lowerCAmelCase_			:      str			=   {}

      if targets is not None:
        lowerCAmelCase_			:      str			=   self.get_target_ids(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      int			=   target_ids

      if top_k is not None:
        lowerCAmelCase_			:      Optional[int]			=   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    :  Any    ,						lowerCamelCase    :  Dict    ,						*lowerCamelCase    :  Dict    ,						**lowerCamelCase    :  Optional[int]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Optional[int]			=   super().__call__(lowerCamelCase    ,						**lowerCamelCase		)
      if isinstance(lowerCamelCase    ,						lowerCamelCase		) and len(lowerCamelCase		) == 1:
        return outputs[0]
      return outputs







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   str							,    A__					:   list[str]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   """"""
  for word_or_phrase in separated:
    if not isinstance(A__							,    A__					):
      raise Exception("""join() accepts only strings to be joined"""					)
    joined += word_or_phrase + separator
  return joined.strip(A__					)


if __name__ == "__main__":
  from doctest import testmod

  testmod()



