


'''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_   :      Dict				    =      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    ):
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      if self.framework == "tf":
           _a      =							tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
      elif self.framework == "pt":
           _a      =							torch.nonzero(input_ids == self.tokenizer.mask_token_id ,			as_tuple=__magic_name__  )
      else:
           raise ValueError('Unsupported framework'  )
      return masked_index
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      _a      =							self.get_masked_index(__magic_name__  )
      _a      =							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 ,			__magic_name__  )   ->							str:
      if isinstance(__magic_name__ ,			__magic_name__  ):
           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(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=None ,			**__magic_name__  )   ->							Dict[str, GenericTensor]:
      if return_tensors is None:
           _a      =							self.framework
      _a      =							self.tokenizer(__magic_name__ ,			return_tensors=__magic_name__  )
      self.ensure_exactly_one_mask_token(__magic_name__  )
      return model_inputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[int]:
      _a      =							self.model(**__magic_name__  )
      _a      =							model_inputs['input_ids']
      return model_outputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=5 ,			__magic_name__=None  )   ->							Dict:
      # Cap top_k if there are targets
      if target_ids is not None and target_ids.shape[0] < top_k:
           _a      =							target_ids.shape[0]
      _a      =							model_outputs['input_ids'][0]
      _a      =							model_outputs['logits']

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

           _a      =							outputs.numpy()

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

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

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

           _a ,				_a      =							probs.topk(__magic_name__  )

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

                _a      =							p
                # Filter padding out:
                _a      =							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
                _a      =							self.tokenizer.decode(__magic_name__ ,			skip_special_tokens=__magic_name__  )
                _a      =							{'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]  ), 'sequence': sequence}
                row.append(__magic_name__  )
           result.append(__magic_name__  )
      if single_mask:
           return result[0]
      return result
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=None  )   ->							int:
      if isinstance(__magic_name__ ,			__magic_name__  ):
           _a      =							[targets]
      try:
           _a      =							self.tokenizer.get_vocab()
      except Exception:
           _a      =							{}
      _a      =							[]
      for target in targets:
           _a      =							vocab.get(__magic_name__ ,			__magic_name__  )
           if id_ is None:
                _a      =							self.tokenizer(
                    __magic_name__ ,			add_special_tokens=__magic_name__ ,			return_attention_mask=__magic_name__ ,			return_token_type_ids=__magic_name__ ,			max_length=1 ,			truncation=__magic_name__ ,			)['input_ids']
                if len(__magic_name__  ) == 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
                _a      =							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_  )
      _a      =							list(set(__magic_name__  )  )
      if len(__magic_name__  ) == 0:
           raise ValueError('At least one target must be provided when passed.'  )
      _a      =							np.array(__magic_name__  )
      return target_ids
 def 							__UpperCAmelCase						(	self ,			__magic_name__=None ,			__magic_name__=None  )   ->							Optional[Any]:
      _a      =							{}

      if targets is not None:
           _a      =							self.get_target_ids(__magic_name__ ,			__magic_name__  )
           _a      =							target_ids

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





'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_   :      Optional[Any]				    =      TypeVar("T")






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (position - 1) // 2






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 1






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 2




class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							[]
      _a      =							{}
      _a      =							0
 def __len__(	self  )   ->							int:
      return self.elements
 def __repr__(	self  )   ->							str:
      return str(self.heap  )
 def 							__UpperCAmelCase						(	self  )   ->							bool:
      # Check if the priority queue is empty
      return self.elements == 0
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)  )
      _a      =							self.elements
      self.elements += 1
      self._bubble_up(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
           self._swap_nodes(0 ,			self.elements - 1  )
      _a ,				_a      =							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
           _a ,				_a      =							self.heap[0]
           self._bubble_down(__magic_name__  )
      return elem
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Update the weight of the given key
      _a      =							self.position_map[elem]
      _a      =							(elem, weight)
      if position > 0:
           _a      =							get_parent_position(__magic_name__  )
           _a ,				_a      =							self.heap[parent_position]
           if parent_weight > weight:
                self._bubble_up(__magic_name__  )
           else:
                self._bubble_down(__magic_name__  )
      else:
           self._bubble_down(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      _a      =							self.position_map[elem]
      if curr_pos == 0:
           return None
      _a      =							get_parent_position(__magic_name__  )
      _a ,				_a      =							self.heap[curr_pos]
      _a ,				_a      =							self.heap[parent_position]
      if parent_weight > weight:
           self._swap_nodes(__magic_name__ ,			__magic_name__  )
           return self._bubble_up(__magic_name__  )
      return None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      _a      =							self.position_map[elem]
      _a ,				_a      =							self.heap[curr_pos]
      _a      =							get_child_left_position(__magic_name__  )
      _a      =							get_child_right_position(__magic_name__  )
      if child_left_position < self.elements and child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      if child_left_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           if child_left_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      else:
           return None
      if child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      return None




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Swap the nodes at the given positions
      _a      =							self.heap[nodea_pos][0]
      _a      =							self.heap[nodea_pos][0]
      _a ,				_a      =							(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      _a      =							nodea_pos
      _a      =							nodea_pos





class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							{}
      _a      =							0
 def __repr__(	self  )   ->							str:
      return str(self.connections  )
 def __len__(	self  )   ->							int:
      return self.nodes
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
           _a      =							{}
           self.nodes += 1




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__magic_name__  )
      self.add_node(__magic_name__  )
      _a      =							weight
      _a      =							weight






def 		_A			(lowerCAmelCase__					:GraphUndirectedWeighted[T]			,  )				->					tuple[dict[T, int], dict[T, T | None]]:




     '''simple docstring'''



     _a      =							{node: maxsize for node in graph.connections}
     _a      =							{node: None for node in graph.connections}

     _a      =							MinPriorityQueue()
     for node, weight in dist.items():
          priority_queue.push(lowerCAmelCase__			,  lowerCAmelCase__						)

     if priority_queue.is_empty():
          return dist, parent

     # initialization
     _a      =							priority_queue.extract_min()
     _a      =							0
     for neighbour in graph.connections[node]:
          if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
               _a      =							dist[node] + graph.connections[node][neighbour]
               priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
               _a      =							node

    # running prim's algorithm
     while not priority_queue.is_empty():
          _a      =							priority_queue.extract_min()
          for neighbour in graph.connections[node]:
               if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                    _a      =							dist[node] + graph.connections[node][neighbour]
                    priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
                    _a      =							node
     return dist, parent

