






'''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


UpperCamelCase							=logging.get_logger(__name__)




@add_end_docstrings(
    SCREAMING_SNAKE_CASE__,   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     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.framework == "tf":
             UpperCamelCase_				:   int					    =							tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
       elif self.framework == "pt":
             UpperCamelCase_				:   Tuple					    =							torch.nonzero(input_ids == self.tokenizer.mask_token_id			,     as_tuple=__lowerCAmelCase )
       else:
             raise ValueError("""Unsupported framework""" )
       return masked_index
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							self.get_masked_index(__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							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			,     __lowerCAmelCase ):
       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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       if return_tensors is None:
             UpperCamelCase_				:   List[Any]					    =							self.framework
       UpperCamelCase_				:   Dict					    =							self.tokenizer(__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.ensure_exactly_one_mask_token(__lowerCAmelCase )
       return model_inputs
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							self.model(**__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							model_inputs["""input_ids"""]
       return model_outputs
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=5			,     __lowerCAmelCase=None ):
       # Cap top_k if there are targets
       if target_ids is not None and target_ids.shape[0] < top_k:
             UpperCamelCase_				:   Dict					    =							target_ids.shape[0]
       UpperCamelCase_				:   Optional[int]					    =							model_outputs["""input_ids"""][0]
       UpperCamelCase_				:   Optional[Any]					    =							model_outputs["""logits"""]

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

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

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

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

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

             UpperCamelCase_    ,			UpperCamelCase_				:   Any					    =							probs.topk(__lowerCAmelCase )

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

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

       if targets is not None:
             UpperCamelCase_				:   Any					    =							self.get_target_ids(__lowerCAmelCase			,     __lowerCAmelCase )
             UpperCamelCase_				:   int					    =							target_ids

       if top_k is not None:
             UpperCamelCase_				:   str					    =							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			,     __lowerCAmelCase			,     *__lowerCAmelCase			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							super().__call__(__lowerCAmelCase			,     **__lowerCAmelCase )
       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) and len(__lowerCAmelCase ) == 1:
             return outputs[0]
       return outputs














'''simple docstring'''

# flake8: noqa
# Lint as: python3
UpperCamelCase							=[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental






