
def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    list:

       # bit count represents no. of bits in the gray code
       if bit_count < 0:
              raise ValueError("""The given input must be positive"""				)

       # get the generated string sequence
       lowercase							:     List[str]       	=      gray_code_sequence_string(SCREAMING_SNAKE_CASE__				)
       #
       # convert them to integers
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     List[Any]       	=      int(sequence[i]					,	2				)

       return sequence







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    list:

       # The approach is a recursive one
       # Base case achieved when either n = 0 or n=1
       if bit_count == 0:
              return ["0"]

       if bit_count == 1:
              return ["0", "1"]

       lowercase							:     Union[str, Any]       	=      1 << bit_count  # defines the length of the sequence
       # 1<< n is equivalent to 2^n

       # recursive answer will generate answer for n-1 bits
       lowercase							:     Optional[int]       	=      gray_code_sequence_string(bit_count - 1				)

       lowercase							:     Tuple       	=      []

       # append 0 to first half of the smaller sequence generated
       for i in range(seq_len // 2				):
              lowercase							:     Any       	=      """0""" + smaller_sequence[i]
              sequence.append(SCREAMING_SNAKE_CASE__				)

       # append 1 to second half ... start from the end of the list
       for i in reversed(range(seq_len // 2				)				):
              lowercase							:     Dict       	=      """1""" + smaller_sequence[i]
              sequence.append(SCREAMING_SNAKE_CASE__				)

       return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()


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_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           requires_backends(self		,"""vision"""      )
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

           lowercase							:     Tuple       	=      scores.tolist()
           lowercase							:     Dict       	=      ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,snake_case      )]
