





"""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 			=  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 				__lowercase			(			_UpperCamelCase							):







     '''simple docstring'''




     def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
         if self.framework == "tf":
             __a :						Any			       =  tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()
         elif self.framework == "pt":
             __a :						Optional[int]			       =  torch.nonzero(input_ids == self.tokenizer.mask_token_id	,			as_tuple=_UpperCAmelCase     )
         else:
             raise ValueError('''Unsupported framework'''     )
         return masked_index







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
         __a :						Union[str, Any]			       =  self.get_masked_index(_UpperCAmelCase     )
         __a :						List[str]			       =  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  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
         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  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
         if return_tensors is None:
             __a :						str			       =  self.framework
         __a :						List[str]			       =  self.tokenizer(_UpperCAmelCase	,			return_tensors=_UpperCAmelCase     )
         self.ensure_exactly_one_mask_token(_UpperCAmelCase     )
         return model_inputs







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
         __a :						Optional[int]			       =  self.model(**_UpperCAmelCase     )
         __a :						Optional[int]			       =  model_inputs['''input_ids''']
         return model_outputs







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=5	,			_UpperCAmelCase=None     ):
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
             __a :						int			       =  target_ids.shape[0]
         __a :						Optional[int]			       =  model_outputs['''input_ids'''][0]
         __a :						int			       =  model_outputs['''logits''']

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

             __a :						Tuple			       =  outputs.numpy()

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

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

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

             __a :						Optional[int]			       =  probs.topk(_UpperCAmelCase     )

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

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







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None     ):
         __a :						Union[str, Any]			       =  {}

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

         if top_k is not None:
             __a :						Tuple			       =  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     ):
         __a :						List[Any]			       =  super().__call__(_UpperCAmelCase	,			**_UpperCAmelCase     )
         if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) and len(_UpperCAmelCase     ) == 1:
             return outputs[0]
         return outputs






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[Any]			       =  max(self.edges.values()     ) + 1
													for edge, weight in self.edges.items():
																	if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																					if weight < min_weight:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






def     __A      (			a_						:str = "p107_network.txt")		->		int:
				__a :						str			       =  os.path.abspath(os.path.dirname(a_))
				__a :						str			       =  os.path.join(a_   ,       a_)
				__a :						dict[EdgeT, int]			       =  {}
				__a :						list[str]
				__a :						int
				__a :						int

				with open(a_) as f:
								__a :						Any			       =  f.read().strip().split('''\n''')

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

				for edgea in range(1   ,       len(a_)):
								for edgea in range(a_):
												if adjaceny_matrix[edgea][edgea] != "-":
																__a :						int			       =  int(adjaceny_matrix[edgea][edgea])

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

				__a :						int			       =  sum(graph.edges.values())
				__a :						int			       =  sum(subgraph.edges.values())

				return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')