

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						:str						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					int					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           super().__init__(**_lowerCAmelCase)

           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							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str], "Image", List["Image"]]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

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



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


           '''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							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					str=None					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="This is a photo of {}."):


           '''simple docstring'''

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



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


           '''simple docstring'''

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

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

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





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


           '''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(_lowerCAmelCase					,		_lowerCAmelCase):
                   __a							=    [scores]
           elif self.framework == "tf":
               __a							=    stable_softmax(_lowerCAmelCase					,		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(_lowerCAmelCase					,		_lowerCAmelCase)					,		key=lambda __SCREAMING_SNAKE_CASE: -x[0])
           ]
           return result










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

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

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

    def count_coins(_UpperCAmelCase    ) -> 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    ) -> CoinsDistribResult:

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

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

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





