






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_						     =					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	,      SCREAMING_SNAKE_CASE_						):
       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=SCREAMING_SNAKE_CASE_						)
       else:
            raise ValueError("""Unsupported framework"""						)
       return masked_index







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.get_masked_index(SCREAMING_SNAKE_CASE_						)
       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	,      SCREAMING_SNAKE_CASE_						):
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            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(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       if return_tensors is None:
            UpperCamelCase__				=    self.framework
       UpperCamelCase__				=    self.tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors=SCREAMING_SNAKE_CASE_						)
       self.ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE_						)
       return model_inputs







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.model(**SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model_inputs["""input_ids"""]
       return model_outputs







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=None						):
       # 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(SCREAMING_SNAKE_CASE_	,      axis=-1						)
            if target_ids is not None:
                 UpperCamelCase__				=    tf.gather_nd(tf.squeeze(SCREAMING_SNAKE_CASE_	,      0						)	,      target_ids.reshape(-1	,      1						)						)
                 UpperCamelCase__				=    tf.expand_dims(SCREAMING_SNAKE_CASE_	,      0						)

            UpperCamelCase__				=    tf.math.top_k(SCREAMING_SNAKE_CASE_	,      k=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__		,	UpperCamelCase__				=    topk.values.numpy(), topk.indices.numpy()
       else:
            UpperCamelCase__				=    torch.nonzero(input_ids == self.tokenizer.mask_token_id	,      as_tuple=SCREAMING_SNAKE_CASE_						).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(SCREAMING_SNAKE_CASE_						)

       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(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]						), """sequence""": sequence}
                 row.append(SCREAMING_SNAKE_CASE_						)
            result.append(SCREAMING_SNAKE_CASE_						)
       if single_mask:
            return result[0]
       return result







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None						):
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    [targets]
       try:
            UpperCamelCase__				=    self.tokenizer.get_vocab()
       except Exception:
            UpperCamelCase__				=    {}
       UpperCamelCase__				=    []
       for target in targets:
            UpperCamelCase__				=    vocab.get(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            if id_ is None:
                 UpperCamelCase__				=    self.tokenizer(
                     SCREAMING_SNAKE_CASE_	,      add_special_tokens=SCREAMING_SNAKE_CASE_	,      return_attention_mask=SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_SNAKE_CASE_	,      max_length=1	,      truncation=SCREAMING_SNAKE_CASE_	,      )["""input_ids"""]
                 if len(SCREAMING_SNAKE_CASE_						) == 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(SCREAMING_SNAKE_CASE_						)						)
       if len(SCREAMING_SNAKE_CASE_						) == 0:
            raise ValueError("""At least one target must be provided when passed."""						)
       UpperCamelCase__				=    np.array(SCREAMING_SNAKE_CASE_						)
       return target_ids







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None						):
       UpperCamelCase__				=    {}

       if targets is not None:
            UpperCamelCase__				=    self.get_target_ids(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            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	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    super().__call__(SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) and len(SCREAMING_SNAKE_CASE_						) == 1:
            return outputs[0]
       return outputs







from __future__ import annotations

from collections import Counter
from random import random




class      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    probability







  def 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    random()

       for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                 return dest
       return ""






def 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(__a	,		__a	,		__a						)

     UpperCamelCase__				=    Counter(graph.get_nodes()						)
     UpperCamelCase__				=    start

     for _ in range(__a						):
          UpperCamelCase__				=    graph.transition(__a						)
          visited[node] += 1

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





