



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

__snake_case		:Tuple   	=  logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase					)
class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Tuple  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

           requires_backends(self  ,	'''vision''')
           self.check_model_type(
               TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == '''tf'''
               else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)



       def __call__(	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, List[str], "Image", List["Image"]]  ,	**__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           __a		 =       {}
           if "candidate_labels" in kwargs:
               __a		 =       kwargs['''candidate_labels''']
           if "hypothesis_template" in kwargs:
               __a		 =       kwargs['''hypothesis_template''']

           return preprocess_params, {}, {}



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  str=None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]="This is a photo of {}."):


           '''simple docstring'''

           __a		 =       load_image(__SCREAMING_SNAKE_CASE)
           __a		 =       self.image_processor(images=[image]  ,	return_tensors=self.framework)
           __a		 =       candidate_labels
           __a		 =       [hypothesis_template.format(__SCREAMING_SNAKE_CASE) for x in candidate_labels]
           __a		 =       self.tokenizer(__SCREAMING_SNAKE_CASE  ,	return_tensors=self.framework  ,	padding=__SCREAMING_SNAKE_CASE)
           __a		 =       [text_inputs]
           return inputs



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       model_inputs.pop('''candidate_labels''')
           __a		 =       model_inputs.pop('''text_inputs''')
           if isinstance(text_inputs[0]  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       text_inputs[0]
           else:
               # Batching case.
               __a		 =       text_inputs[0][0]

           __a		 =       self.model(**__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)

           __a		 =       {
               '''candidate_labels''': candidate_labels,
               '''logits''': outputs.logits_per_image,
           }
           return model_outputs





       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       model_outputs.pop('''candidate_labels''')
           __a		 =       model_outputs['''logits'''][0]
           if self.framework == "pt":
               __a		 =       logits.softmax(dim=-1).squeeze(-1)
               __a		 =       probs.tolist()
               if not isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
                   __a		 =       [scores]
           elif self.framework == "tf":
               __a		 =       stable_softmax(__SCREAMING_SNAKE_CASE  ,	axis=-1)
               __a		 =       probs.numpy().tolist()
           else:
               raise ValueError(F'Unsupported framework: {self.framework}')

           __a		 =       [
               {'''score''': score, '''label''': candidate_label}
               for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)  ,	key=lambda __SCREAMING_SNAKE_CASE: -x[0])
           ]
           return result










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)





