





"""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

__UpperCamelCase     :						Dict						       =  logging.get_logger(__name__)







@add_end_docstrings(a__  )
class       a							(   a__  ):



 def __init__(     self				,       **_snake_case     ):







  """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     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






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

  return preprocess_params, {}, {}



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None				,       _snake_case="This is a photo of {}."     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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

  lowerCAmelCase    				= self.model(**_snake_case				,       **_snake_case     )

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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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

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









def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[False] * len(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						[]
		queue.append(a )
		SCREAMING_SNAKE_CASE_				:int				     =						True

		while queue:
				SCREAMING_SNAKE_CASE_				:int				     =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(a )
								SCREAMING_SNAKE_CASE_				:Tuple				     =						True
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						u

		return visited[t]


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						[-1] * (len(a ))
		SCREAMING_SNAKE_CASE_				:Tuple				     =						0
		while bfs(a     , a     , a     , a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float("Inf" )
				SCREAMING_SNAKE_CASE_				:str				     =						sink

				while s != source:
						# Find the minimum value in select path
						SCREAMING_SNAKE_CASE_				:str				     =						min(a     , graph[parent[s]][s] )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						parent[s]

				max_flow += path_flow
				SCREAMING_SNAKE_CASE_				:Dict				     =						sink

				while v != source:
						SCREAMING_SNAKE_CASE_				:int				     =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						SCREAMING_SNAKE_CASE_				:Any				     =						parent[v]
		return max_flow


SCREAMING_SNAKE_CASE__     					=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE__					,    SCREAMING_SNAKE_CASE__     					=			0, 5
print(ford_fulkerson(graph, source, sink))






