



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		:int   	=  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					):



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  GenericTensor):


           '''simple docstring'''

           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=__SCREAMING_SNAKE_CASE)
           else:
               raise ValueError('''Unsupported framework''')
           return masked_index



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  GenericTensor):


           '''simple docstring'''

           __a		 =       self.get_masked_index(__SCREAMING_SNAKE_CASE)
           __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 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  GenericTensor):


           '''simple docstring'''

           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 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=None  ,	**__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           if return_tensors is None:
               __a		 =       self.framework
           __a		 =       self.tokenizer(__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE)
           self.ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE)
           return model_inputs



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.model(**__SCREAMING_SNAKE_CASE)
           __a		 =       model_inputs['''input_ids''']
           return model_outputs



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=5  ,	__SCREAMING_SNAKE_CASE							:  str=None):


           '''simple docstring'''

           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(__SCREAMING_SNAKE_CASE  ,	axis=-1)
               if target_ids is not None:
                   __a		 =       tf.gather_nd(tf.squeeze(__SCREAMING_SNAKE_CASE  ,	0)  ,	target_ids.reshape(-1  ,	1))
                   __a		 =       tf.expand_dims(__SCREAMING_SNAKE_CASE  ,	0)

               __a		 =       tf.math.top_k(__SCREAMING_SNAKE_CASE  ,	k=__SCREAMING_SNAKE_CASE)
               __a ,     __a		 =       topk.values.numpy(), topk.indices.numpy()
           else:
               __a		 =       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

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

               __a ,     __a		 =       probs.topk(__SCREAMING_SNAKE_CASE)

           __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(__SCREAMING_SNAKE_CASE  ,	skip_special_tokens=__SCREAMING_SNAKE_CASE)
                   __a		 =       {'''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 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple=None):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       [targets]
           try:
               __a		 =       self.tokenizer.get_vocab()
           except Exception:
               __a		 =       {}
           __a		 =       []
           for target in targets:
               __a		 =       vocab.get(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               if id_ is None:
                   __a		 =       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
                   __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(__SCREAMING_SNAKE_CASE))
           if len(__SCREAMING_SNAKE_CASE) == 0:
               raise ValueError('''At least one target must be provided when passed.''')
           __a		 =       np.array(__SCREAMING_SNAKE_CASE)
           return target_ids



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=None):


           '''simple docstring'''

           __a		 =       {}

           if targets is not None:
               __a		 =       self.get_target_ids(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               __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							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  int  ,	*__SCREAMING_SNAKE_CASE							:  str  ,	**__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    print(f'Vertex\tShortest Distance from vertex {src}'       )
    for i, d in enumerate(_UpperCAmelCase       ):
        print(f'{i}\t\t{d}'       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for j in range(_UpperCAmelCase       ):
        __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
            return True
    return False





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [float('''inf'''       )] * vertex_count
    __a		 =       0.0

    for _ in range(vertex_count - 1       ):
        for j in range(_UpperCAmelCase       ):
            __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                __a		 =       distance[u] + w

    __a		 =       check_negative_cycle(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''       )

    return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __snake_case		:Dict   	=  int(input('''Enter number of vertices: ''').strip())
 __snake_case		:Any   	=  int(input('''Enter number of edges: ''').strip())

 __snake_case		:list[dict[str, int]]   	=  [{} for _ in range(E)]

 for i in range(E):
  print('''Edge ''', i + 1)
  __snake_case	,__snake_case	,__snake_case		:int   	=  (
      int(x)
      for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
  )
  __snake_case		:Any   	=  {'''src''': src, '''dst''': dest, '''weight''': weight}

 __snake_case		:List[str]   	=  int(input('''\nEnter shortest path source:''').strip())
 __snake_case		:Optional[Any]   	=  bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)





