




'''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_						:      Union[str, Any]       =						logging.get_logger(__name__)

@add_end_docstrings(__lowerCAmelCase							)
class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,**_SCREAMING_SNAKE_CASE   )							->      Any:
          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 ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          return super().__call__(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          _snake_case							=				{}
          if "candidate_labels" in kwargs:
               _snake_case							=				kwargs["candidate_labels"]
          if "hypothesis_template" in kwargs:
               _snake_case							=				kwargs["hypothesis_template"]

          return preprocess_params, {}, {}






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="This is a photo of {}."   )							->      Union[str, Any]:
          _snake_case							=				load_image(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.image_processor(images=[image] ,return_tensors=self.framework   )
          _snake_case							=				candidate_labels
          _snake_case							=				[hypothesis_template.format(_SCREAMING_SNAKE_CASE   ) for x in candidate_labels]
          _snake_case							=				self.tokenizer(_SCREAMING_SNAKE_CASE ,return_tensors=self.framework ,padding=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				[text_inputs]
          return inputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				model_inputs.pop("candidate_labels"   )
          _snake_case							=				model_inputs.pop("text_inputs"   )
          if isinstance(text_inputs[0] ,_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				text_inputs[0]
          else:
               # Batching case.
               _snake_case							=				text_inputs[0][0]

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

          _snake_case							=				{
              "candidate_labels": candidate_labels,
              "logits": outputs.logits_per_image,
          }
          return model_outputs






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

          _snake_case							=				[
              {"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








'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None







UpperCamelCase_						:      Any       =						namedtuple('''CoinsDistribResult''', '''moves excess''')





def      __a     (       _UpperCamelCase:   TreeNode | None   )						->			int:

     """simple docstring"""




     if root is None:
          return 0

     # Validation
     def count_nodes(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

     def count_coins(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_coins(node.left   ) + count_coins(node.right   ) + node.data

     if count_nodes(_UpperCamelCase   ) != count_coins(_UpperCamelCase   ):
          raise ValueError("The nodes number should be same as the number of coins"   )

     # Main calculation
     def get_distrib(_UpperCamelCase:   TreeNode | None   ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0 ,						1   )

          _snake_case							, _snake_case							=				get_distrib(node.left   )
          _snake_case							, _snake_case							=				get_distrib(node.right   )

          _snake_case							=				1 - left_distrib_excess
          _snake_case							=				1 - right_distrib_excess

          _snake_case							=				(
              left_distrib_moves
              + right_distrib_moves
              + abs(_UpperCamelCase   )
              + abs(_UpperCamelCase   )
          )
          _snake_case							=				node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_UpperCamelCase ,						_UpperCamelCase   )

     return get_distrib(_UpperCamelCase   )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


