


'''simple docstring'''






# using dfs for finding eulerian path traversal





def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]=None							):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =True, True
         _UpperCAmelCase       :       Optional[Any]							   =dfs(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   return path





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Optional[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =0
   _UpperCAmelCase       :       Optional[int]							   =-1
   for i in range(__lowerCamelCase							):
      if i not in graph.keys():
         continue
      if len(graph[i]							) % 2 == 1:
         odd_degree_nodes += 1
         _UpperCAmelCase       :       Union[str, Any]							   =i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							Optional[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Union[str, Any]							   =[[False for _ in range(max_node + 1							)] for _ in range(max_node + 1							)]
   _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =check_circuit_or_path(__lowerCamelCase	,   __lowerCamelCase							)
   if check == 3:
      print('graph is not Eulerian'							)
      print('no path'							)
      return
   _UpperCAmelCase       :       Optional[Any]							   =1
   if check == 2:
      _UpperCAmelCase       :       int							   =odd_node
      print('graph has a Euler path'							)
   if check == 1:
      print('graph has a Euler cycle'							)
   _UpperCAmelCase       :       Optional[int]							   =dfs(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   print(__lowerCamelCase							)





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   ={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   _UpperCAmelCase       :       Tuple							   ={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   _UpperCAmelCase       :       List[str]							   ={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   _UpperCAmelCase       :       Any							   ={1: [2, 3], 2: [1, 3], 3: [1, 2]}
   _UpperCAmelCase       :       Tuple							   ={
       1: [],
       2: []
       # all degree is zero
   }
   _UpperCAmelCase       :       Optional[Any]							   =1_0
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 main()





'''simple docstring'''






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

lowercase							=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase    )
class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							**snake_case)						->  Optional[int]:




          '''simple docstring'''
          super().__init__(**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  ,							snake_case  ,							**snake_case)						->  str:




          '''simple docstring'''
          return super().__call__(snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   ={}
          if "candidate_labels" in kwargs:
             _UpperCAmelCase       :       Union[str, Any]							   =kwargs['candidate_labels']
          if "hypothesis_template" in kwargs:
             _UpperCAmelCase       :       List[Any]							   =kwargs['hypothesis_template']

          return preprocess_params, {}, {}






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=None  ,							snake_case="This is a photo of {}.")						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =load_image(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processor(images=[image]  ,							return_tensors=self.framework)
          _UpperCAmelCase       :       Union[str, Any]							   =candidate_labels
          _UpperCAmelCase       :       List[Any]							   =[hypothesis_template.format(snake_case) for x in candidate_labels]
          _UpperCAmelCase       :       str							   =self.tokenizer(snake_case  ,							return_tensors=self.framework  ,							padding=snake_case)
          _UpperCAmelCase       :       Any							   =[text_inputs]
          return inputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =model_inputs.pop('candidate_labels')
          _UpperCAmelCase       :       Tuple							   =model_inputs.pop('text_inputs')
          if isinstance(text_inputs[0]  ,							snake_case):
             _UpperCAmelCase       :       Any							   =text_inputs[0]
          else:
             # Batching case.
             _UpperCAmelCase       :       str							   =text_inputs[0][0]

          _UpperCAmelCase       :       Any							   =self.model(**snake_case  ,							**snake_case)

          _UpperCAmelCase       :       List[str]							   ={
              'candidate_labels': candidate_labels,
              'logits': outputs.logits_per_image,
          }
          return model_outputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =model_outputs.pop('candidate_labels')
          _UpperCAmelCase       :       Union[str, Any]							   =model_outputs['logits'][0]
          if self.framework == "pt":
             _UpperCAmelCase       :       Dict							   =logits.softmax(dim=-1).squeeze(-1)
             _UpperCAmelCase       :       Union[str, Any]							   =probs.tolist()
             if not isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase       :       Union[str, Any]							   =[scores]
          elif self.framework == "tf":
             _UpperCAmelCase       :       Dict							   =stable_softmax(snake_case  ,							axis=-1)
             _UpperCAmelCase       :       str							   =probs.numpy().tolist()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

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

