


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any] = 100   ):
					__a			:       Optional[Any]					    =  n * (n + 1) * (2 * n + 1) / 6
					__a			:       List[Any]					    =  (n * (n + 1) / 2) ** 2
					return int(square_of_sum - sum_of_squares   )


if __name__ == "__main__":
	print(f'''{solution() = }''')








"""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

lowerCAmelCase_   						=   logging.get_logger(__name__)
@add_end_docstrings(A_    )
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  List[str]			,**_snake_case						:  Dict						)					->		List[Any]:






				"""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						:  Optional[int]			,_snake_case						:  Union[str, List[str], "Image", List["Image"]]			,**_snake_case						:  int						)					->		Optional[Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Dict			,**_snake_case						:  Optional[int]						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			{}
				if "candidate_labels" in kwargs:
						lowercase__   :    Any				=			kwargs['''candidate_labels''']
				if "hypothesis_template" in kwargs:
						lowercase__   :    Optional[Any]				=			kwargs['''hypothesis_template''']

				return preprocess_params, {}, {}







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Optional[int]			,_snake_case						:  Dict=None			,_snake_case						:  Union[str, Any]="This is a photo of {}."						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			load_image(_snake_case						)
				lowercase__   :    int				=			self.image_processor(images=[image]			,return_tensors=self.framework						)
				lowercase__   :    str				=			candidate_labels
				lowercase__   :    Dict				=			[hypothesis_template.format(_snake_case						) for x in candidate_labels]
				lowercase__   :    Any				=			self.tokenizer(_snake_case			,return_tensors=self.framework			,padding=_snake_case						)
				lowercase__   :    Optional[int]				=			[text_inputs]
				return inputs







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Optional[int]						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			model_inputs.pop('''candidate_labels'''						)
				lowercase__   :    Union[str, Any]				=			model_inputs.pop('''text_inputs'''						)
				if isinstance(text_inputs[0]			,_snake_case						):
						lowercase__   :    List[str]				=			text_inputs[0]
				else:
						# Batching case.
						lowercase__   :    int				=			text_inputs[0][0]

				lowercase__   :    Tuple				=			self.model(**_snake_case			,**_snake_case						)

				lowercase__   :    Union[str, Any]				=			{
				    '''candidate_labels''': candidate_labels,
				    '''logits''': outputs.logits_per_image,
				}
				return model_outputs







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  Tuple						)					->		Any:






				"""simple docstring"""



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

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