




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


lowerCamelCase	:	Any				=   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 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def      UpperCAmelCase				(				self    ,				A     )		->    np.ndarray:
         if self.framework == "tf":
            snake_case				:    str           =	tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()
         elif self.framework == "pt":
            snake_case				:    Dict           =	torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,				as_tuple=A     )
         else:
            raise ValueError("""Unsupported framework"""     )
         return masked_index

      def      UpperCAmelCase				(				self    ,				A     )		->    np.ndarray:
         snake_case				:    Dict           =	self.get_masked_index(A     )
         snake_case				:    Tuple           =	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      UpperCAmelCase				(				self    ,				A     )		->    Dict:
         if isinstance(A    ,				A     ):
            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(A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A=None    ,				**A     )		->    Dict[str, GenericTensor]:
         if return_tensors is None:
            snake_case				:    Dict           =	self.framework
         snake_case				:    List[Any]           =	self.tokenizer(A    ,				return_tensors=A     )
         self.ensure_exactly_one_mask_token(A     )
         return model_inputs

      def      UpperCAmelCase				(				self    ,				A     )		->    Tuple:
         snake_case				:    Tuple           =	self.model(**A     )
         snake_case				:    str           =	model_inputs["""input_ids"""]
         return model_outputs

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

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

            snake_case				:    Any           =	outputs.numpy()

            snake_case				:    Optional[Any]           =	outputs[0, masked_index, :]
            snake_case				:    str           =	stable_softmax(A    ,				axis=-1     )
            if target_ids is not None:
               snake_case				:    int           =	tf.gather_nd(tf.squeeze(A    ,				0     )    ,				target_ids.reshape(-1    ,				1     )     )
               snake_case				:    str           =	tf.expand_dims(A    ,				0     )

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

            snake_case				:    Optional[int]           =	outputs[0, masked_index, :]
            snake_case				:    str           =	logits.softmax(dim=-1     )
            if target_ids is not None:
               snake_case				:    Tuple           =	probs[..., target_ids]

            snake_case				:    str           =	probs.topk(A     )

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

               snake_case				:    Dict           =	p
               # Filter padding out:
               snake_case				:    Optional[int]           =	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
               snake_case				:    Union[str, Any]           =	self.tokenizer.decode(A    ,				skip_special_tokens=A     )
               snake_case				:    Union[str, Any]           =	{"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]     ), """sequence""": sequence}
               row.append(A     )
            result.append(A     )
         if single_mask:
            return result[0]
         return result

      def      UpperCAmelCase				(				self    ,				A    ,				A=None     )		->    Dict:
         if isinstance(A    ,				A     ):
            snake_case				:    List[str]           =	[targets]
         try:
            snake_case				:    Any           =	self.tokenizer.get_vocab()
         except Exception:
            snake_case				:    str           =	{}
         snake_case				:    List[Any]           =	[]
         for target in targets:
            snake_case				:    List[str]           =	vocab.get(A    ,				A     )
            if id_ is None:
               snake_case				:    List[Any]           =	self.tokenizer(
                   A    ,				add_special_tokens=A    ,				return_attention_mask=A    ,				return_token_type_ids=A    ,				max_length=1    ,				truncation=A    ,				)["""input_ids"""]
               if len(A     ) == 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
               snake_case				:    List[str]           =	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_     )
         snake_case				:    List[Any]           =	list(set(A     )     )
         if len(A     ) == 0:
            raise ValueError("""At least one target must be provided when passed."""     )
         snake_case				:    Optional[Any]           =	np.array(A     )
         return target_ids

      def      UpperCAmelCase				(				self    ,				A=None    ,				A=None     )		->    int:
         snake_case				:    List[Any]           =	{}

         if targets is not None:
            snake_case				:    Tuple           =	self.get_target_ids(A    ,				A     )
            snake_case				:    Union[str, Any]           =	target_ids

         if top_k is not None:
            snake_case				:    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    ,				A    ,				*A    ,				**A     )		->    Optional[Any]:
         snake_case				:    List[str]           =	super().__call__(A    ,				**A     )
         if isinstance(A    ,				A     ) and len(A     ) == 1:
            return outputs[0]
         return outputs











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





