






def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase = 1 ,   __lowerCAmelCase = 1000      )			->     int:
     UpperCamelCase__      :       Any	    =  1
     UpperCamelCase__      :       List[str]	    =  0

     for divide_by_number in range(__lowerCAmelCase ,   digit + 1      ):
          UpperCamelCase__      :       int	    =  []
          UpperCamelCase__      :       Dict	    =  numerator
          for _ in range(1 ,   digit + 1      ):
               if now_divide in has_been_divided:
                    if longest_list_length < len(__lowerCAmelCase      ):
                         UpperCamelCase__      :       Union[str, Any]	    =  len(__lowerCAmelCase      )
                         UpperCamelCase__      :       List[str]	    =  divide_by_number
               else:
                    has_been_divided.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       int	    =  now_divide * 10 % divide_by_number

     return the_digit


# Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()








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     :							str    =logging.get_logger(__name__)





@add_end_docstrings(A__	)
class       __a  (   A__	):







 def __init__(		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      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 :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, List[str], "Image", List["Image"]]							,       **SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  {}
      if "candidate_labels" in kwargs:
           UpperCamelCase__      :       Optional[Any]	    =  kwargs["candidate_labels"]
      if "hypothesis_template" in kwargs:
           UpperCamelCase__      :       int	    =  kwargs["hypothesis_template"]

      return preprocess_params, {}, {}







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[Any]							,       SCREAMING_SNAKE_CASE :       Tuple=None							,       SCREAMING_SNAKE_CASE :       Optional[int]="This is a photo of {}."						):


      '''simple docstring'''






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







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  model_inputs.pop("candidate_labels"						)
      UpperCamelCase__      :       List[str]	    =  model_inputs.pop("text_inputs"						)
      if isinstance(text_inputs[0]							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Dict	    =  text_inputs[0]
      else:
           # Batching case.
           UpperCamelCase__      :       Union[str, Any]	    =  text_inputs[0][0]

      UpperCamelCase__      :       Any	    =  self.model(**SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

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







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






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

      UpperCamelCase__      :       Optional[int]	    =  [
          {"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