





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			:	List[str]					    =	logging.get_logger(__name__)



@add_end_docstrings(
    __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    A__  (			__snake_case     ):





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       if self.framework == "tf":
        UpperCamelCase      :   Optional[int]			      =  tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
       elif self.framework == "pt":
        UpperCamelCase      :   Union[str, Any]			      =  torch.nonzero(input_ids == self.tokenizer.mask_token_id			,				as_tuple=A_  )
       else:
        raise ValueError("Unsupported framework"  )
       return masked_index





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.get_masked_index(A_  )
       UpperCamelCase      :   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_  ):
       '''simple docstring'''




       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_  ):
       '''simple docstring'''




       if return_tensors is None:
        UpperCamelCase      :   int			      =  self.framework
       UpperCamelCase      :   int			      =  self.tokenizer(A_			,				return_tensors=A_  )
       self.ensure_exactly_one_mask_token(A_  )
       return model_inputs





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  self.model(**A_  )
       UpperCamelCase      :   List[str]			      =  model_inputs["input_ids"]
       return model_outputs





      def 			__UpperCamelCase(					self			,				A_			,				A_=5			,				A_=None  ):
       '''simple docstring'''




       if target_ids is not None and target_ids.shape[0] < top_k:
        UpperCamelCase      :   Tuple			      =  target_ids.shape[0]
       UpperCamelCase      :   Optional[int]			      =  model_outputs["input_ids"][0]
       UpperCamelCase      :   Tuple			      =  model_outputs["logits"]

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

        UpperCamelCase      :   Any			      =  outputs.numpy()

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

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

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

        UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  probs.topk(A_  )

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

         UpperCamelCase      :   List[str]			      =  p
         # Filter padding out:
         UpperCamelCase      :   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
         UpperCamelCase      :   List[Any]			      =  self.tokenizer.decode(A_			,				skip_special_tokens=A_  )
         UpperCamelCase      :   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  ):
       '''simple docstring'''




       if isinstance(A_			,				A_  ):
        UpperCamelCase      :   Dict			      =  [targets]
       try:
        UpperCamelCase      :   Union[str, Any]			      =  self.tokenizer.get_vocab()
       except Exception:
        UpperCamelCase      :   Tuple			      =  {}
       UpperCamelCase      :   int			      =  []
       for target in targets:
        UpperCamelCase      :   int			      =  vocab.get(A_			,				A_  )
        if id_ is None:
         UpperCamelCase      :   Tuple			      =  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
         UpperCamelCase      :   Optional[int]			      =  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      :   Tuple			      =  list(set(A_  )  )
       if len(A_  ) == 0:
        raise ValueError("At least one target must be provided when passed."  )
       UpperCamelCase      :   Optional[Any]			      =  np.array(A_  )
       return target_ids





      def 			__UpperCamelCase(					self			,				A_=None			,				A_=None  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  {}

       if targets is not None:
        UpperCamelCase      :   Tuple			      =  self.get_target_ids(A_			,				A_  )
        UpperCamelCase      :   Optional[int]			      =  target_ids

       if top_k is not None:
        UpperCamelCase      :   List[Any]			      =  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_  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  super().__call__(A_			,				**A_  )
       if isinstance(A_			,				A_  ) and len(A_  ) == 1:
        return outputs[0]
       return outputs







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         self.adj_list[destination_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
         self.adj_list[destination_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [source_vertex]
       else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   List[str]			      =  []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





      def __repr__(					self  ):
       '''simple docstring'''




       return pformat(self.adj_list  )
