





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     =    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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       GenericTensor)	->np.ndarray:



   '''simple docstring'''



   if self.framework == "tf":
     lowerCamelCase__:				Tuple        =tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
   elif self.framework == "pt":
     lowerCamelCase__:				Tuple        =torch.nonzero(input_ids == self.tokenizer.mask_token_id	,			as_tuple=UpperCAmelCase_)
   else:
     raise ValueError("Unsupported framework")
   return masked_index

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       GenericTensor)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.get_masked_index(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =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 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       GenericTensor)	->List[str]:



   '''simple docstring'''



   if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     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(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Union[str, Any]=None	,			**UpperCAmelCase_    :       Union[str, Any])	->Dict[str, GenericTensor]:



   '''simple docstring'''



   if return_tensors is None:
     lowerCamelCase__:				Any        =self.framework
   lowerCamelCase__:				str        =self.tokenizer(UpperCAmelCase_	,			return_tensors=UpperCAmelCase_)
   self.ensure_exactly_one_mask_token(UpperCAmelCase_)
   return model_inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       List[str])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.model(**UpperCAmelCase_)
   lowerCamelCase__:				str        =model_inputs["input_ids"]
   return model_outputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       str=5	,			UpperCAmelCase_    :       Tuple=None)	->Dict:



   '''simple docstring'''



   if target_ids is not None and target_ids.shape[0] < top_k:
     lowerCamelCase__:				Tuple        =target_ids.shape[0]
   lowerCamelCase__:				Any        =model_outputs["input_ids"][0]
   lowerCamelCase__:				List[Any]        =model_outputs["logits"]

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

     lowerCamelCase__:				Optional[int]        =outputs.numpy()

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

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

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

     lowerCamelCase__:				Any        =probs.topk(UpperCAmelCase_)

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

       lowerCamelCase__:				Dict        =p
       # Filter padding out:
       lowerCamelCase__:				Tuple        =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__:				int        =self.tokenizer.decode(UpperCAmelCase_	,			skip_special_tokens=UpperCAmelCase_)
       lowerCamelCase__:				Any        ={"score": v, "token": p, "token_str": self.tokenizer.decode([p]), "sequence": sequence}
       row.append(UpperCAmelCase_)
     result.append(UpperCAmelCase_)
   if single_mask:
     return result[0]
   return result

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Any=None)	->Dict:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       List[Any]=None	,			UpperCAmelCase_    :       List[Any]=None)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        ={}

   if targets is not None:
     lowerCamelCase__:				int        =self.get_target_ids(UpperCAmelCase_	,			UpperCAmelCase_)
     lowerCamelCase__:				Union[str, Any]        =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    :       List[str]	,			UpperCAmelCase_    :       Union[str, Any]	,			*UpperCAmelCase_    :       int	,			**UpperCAmelCase_    :       List[Any])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =super().__call__(UpperCAmelCase_	,			**UpperCAmelCase_)
   if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and len(UpperCAmelCase_) == 1:
     return outputs[0]
   return outputs







def 					lowerCAmelCase_			(	__a		)					->		str:







  """simple docstring"""





  if isinstance(__a     ,   __a		):
    raise TypeError("'float' object cannot be interpreted as an integer"		)
  if isinstance(__a     ,   __a		):
    raise TypeError("'str' object cannot be interpreted as an integer"		)

  if num == 0:
    return "0b0"

  lowerCamelCase__:				Optional[int]        =False

  if num < 0:
    lowerCamelCase__:				Optional[Any]        =True
    lowerCamelCase__:				List[Any]        =-num

  lowerCamelCase__:				list[int]        =[]
  while num > 0:
    binary.insert(0     ,   num % 2		)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(__a		) for e in binary		)

  return "0b" + "".join(str(__a		) for e in binary		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

