






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__      :	List[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 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> np.ndarray:
     UpperCamelCase_									=     self.get_masked_index(_UpperCAmelCase )
     UpperCamelCase_									=     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		,		_UpperCAmelCase )			-> List[str]:
     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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		**_UpperCAmelCase )			-> Dict[str, GenericTensor]:
     if return_tensors is None:
        UpperCamelCase_									=     self.framework
     UpperCamelCase_									=     self.tokenizer(_UpperCAmelCase		,		return_tensors=_UpperCAmelCase )
     self.ensure_exactly_one_mask_token(_UpperCAmelCase )
     return model_inputs




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     self.model(**_UpperCAmelCase )
     UpperCamelCase_									=     model_inputs['input_ids']
     return model_outputs




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=5		,		_UpperCAmelCase=None )			-> str:
     # Cap top_k if there are targets
     if target_ids is not None and target_ids.shape[0] < top_k:
        UpperCamelCase_									=     target_ids.shape[0]
     UpperCamelCase_									=     model_outputs['input_ids'][0]
     UpperCamelCase_									=     model_outputs['logits']

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

        UpperCamelCase_									=     outputs.numpy()

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

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

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

        UpperCamelCase_		,      UpperCamelCase_									=     probs.topk(_UpperCAmelCase )

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

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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None )			-> Optional[int]:
     UpperCamelCase_									=     {}

     if targets is not None:
        UpperCamelCase_									=     self.get_target_ids(_UpperCAmelCase		,		_UpperCAmelCase )
        UpperCamelCase_									=     target_ids

     if top_k is not None:
        UpperCamelCase_									=     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		,		_UpperCAmelCase		,		*_UpperCAmelCase		,		**_UpperCAmelCase )			-> Optional[int]:
     UpperCamelCase_									=     super().__call__(_UpperCAmelCase		,		**_UpperCAmelCase )
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) and len(_UpperCAmelCase ) == 1:
        return outputs[0]
     return outputs













def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   return not any(
       neighbour == 1 and colored_vertices[i] == color
       for i, neighbour in enumerate(__lowercase))






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):

   # Base Case
   if index == len(__lowercase):
      return True

   # Recursive Step
   for i in range(__lowercase):
      if valid_coloring(graph[index]    ,      __lowercase    ,      __lowercase):
         # Color current vertex
         UpperCamelCase_									=     i
         # Validate coloring
         if util_color(__lowercase    ,      __lowercase    ,      __lowercase    ,      index + 1):
            return True
         # Backtrack
         UpperCamelCase_									=     -1
   return False






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     [-1] * len(__lowercase)

   if util_color(__lowercase    ,      __lowercase    ,      __lowercase    ,      0):
      return colored_vertices

   return []





