


"""simple docstring"""







def 					lowercase      (    A_	)->				list[int]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      [0 for i in range(len(A_	)	)]

  # initialize interval's left pointer and right pointer
  a							,		a						:		Optional[int]											=      0, 0

  for i in range(1  ,       len(A_	)	):
    # case when current index is inside the interval
    if i <= right_pointer:
      a						:		Optional[int]											=      min(right_pointer - i + 1  ,       z_result[i - left_pointer]	)
      a						:		int											=      min_edge

    while go_next(A_  ,       A_  ,       A_	):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      a							,		a						:		Dict											=      i, i + z_result[i] - 1

  return z_result


def 					lowercase      (    A_  ,       A_  ,       A_	)->				bool:
  '''simple docstring'''







  return i + z_result[i] < len(A_	) and s[z_result[i]] == s[i + z_result[i]]


def 					lowercase      (    A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		Tuple											=      0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  a						:		Optional[Any]											=      z_function(pattern + input_str	)

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(A_	):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()










"""simple docstring"""







from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowercase    =				logging.get_logger(__name__)






@add_end_docstrings(_a  )
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			**__UpperCAmelCase    :   List[Any]):
      super().__init__(**__UpperCAmelCase)

      if self.framework == "tf":
        raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

      requires_backends(self    ,			"vision")
      self.check_model_type(__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Union[str, "Image.Image", List[Dict[str, Any]]]    ,			__UpperCAmelCase    :   Union[str, List[str]] = None    ,			**__UpperCAmelCase    :   List[Any]    ,			):
      if "text_queries" in kwargs:
        a						:		List[Any]											=      kwargs.pop("text_queries")

      if isinstance(__UpperCAmelCase    ,			(str, Image.Image)):
        a						:		Any											=      {"image": image, "candidate_labels": candidate_labels}
      else:
        a						:		Optional[int]											=      image
      a						:		Optional[int]											=      super().__call__(__UpperCAmelCase    ,			**__UpperCAmelCase)
      return results






    def      __snake_case							( self    :   Optional[int]    ,			**__UpperCAmelCase    :   List[Any]):
      a						:		str											=      {}
      if "threshold" in kwargs:
        a						:		Dict											=      kwargs["threshold"]
      if "top_k" in kwargs:
        a						:		str											=      kwargs["top_k"]
      return {}, {}, postprocess_params






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      a						:		Union[str, Any]											=      load_image(inputs["image"])
      a						:		Any											=      inputs["candidate_labels"]
      if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase):
        a						:		Optional[Any]											=      candidate_labels.split(",")

      a						:		Union[str, Any]											=      torch.tensor([[image.height, image.width]]    ,			dtype=torch.intaa)
      for i, candidate_label in enumerate(__UpperCAmelCase):
        a						:		int											=      self.tokenizer(__UpperCAmelCase    ,			return_tensors=self.framework)
        a						:		int											=      self.image_processor(__UpperCAmelCase    ,			return_tensors=self.framework)
        yield {
            "is_last": i == len(__UpperCAmelCase) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		List[Any]											=      model_inputs.pop("target_size")
      a						:		Optional[int]											=      model_inputs.pop("candidate_label")
      a						:		List[Any]											=      model_inputs.pop("is_last")

      a						:		List[Any]											=      self.model(**__UpperCAmelCase)

      a						:		Union[str, Any]											=      {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
      return model_outputs






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[str]=None):
      a						:		Dict											=      []
      for model_output in model_outputs:
        a						:		int											=      model_output["candidate_label"]
        a						:		Any											=      BaseModelOutput(__UpperCAmelCase)
        a						:		Optional[Any]											=      self.image_processor.post_process_object_detection(
            outputs=__UpperCAmelCase    ,			threshold=__UpperCAmelCase    ,			target_sizes=model_output["target_size"])[0]

        for index in outputs["scores"].nonzero():
          a						:		Any											=      outputs["scores"][index].item()
          a						:		str											=      self._get_bounding_box(outputs["boxes"][index][0])

          a						:		Optional[Any]											=      {"score": score, "label": label, "box": box}
          results.append(__UpperCAmelCase)

      a						:		str											=      sorted(__UpperCAmelCase    ,			key=lambda __UpperCAmelCase: x["score"]    ,			reverse=__UpperCAmelCase)
      if top_k:
        a						:		Union[str, Any]											=      results[:top_k]

      return results






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   "torch.Tensor"):
      if self.framework != "pt":
        raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
      a							,		a							,		a							,		a						:		List[Any]											=      box.int().tolist()
      a						:		str											=      {
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox






