





"""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

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						**lowercase )		->     Optional[int]:
            super().__init__(**lowercase )

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

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





      def __call__(       self      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     List[str]:
            if "text_queries" in kwargs:
                  lowerCAmelCase  						=       kwargs.pop("""text_queries""" )

            if isinstance(lowercase      ,						(str, Image.Image) ):
                  lowerCAmelCase  						=       {"""image""": image, """candidate_labels""": candidate_labels}
            else:
                  lowerCAmelCase  						=       image
            lowerCAmelCase  						=       super().__call__(lowercase      ,						**lowercase )
            return results





      def     _snake_case			(       self      ,						**lowercase )		->     List[str]:
            lowerCAmelCase  						=       {}
            if "threshold" in kwargs:
                  lowerCAmelCase  						=       kwargs["""threshold"""]
            if "top_k" in kwargs:
                  lowerCAmelCase  						=       kwargs["""top_k"""]
            return {}, {}, postprocess_params





      def     _snake_case			(       self      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       load_image(inputs["""image"""] )
            lowerCAmelCase  						=       inputs["""candidate_labels"""]
            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       candidate_labels.split(""",""" )

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





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       model_inputs.pop("""target_size""" )
            lowerCAmelCase  						=       model_inputs.pop("""candidate_label""" )
            lowerCAmelCase  						=       model_inputs.pop("""is_last""" )

            lowerCAmelCase  						=       self.model(**lowercase )

            lowerCAmelCase  						=       {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
            return model_outputs





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=0.1      ,						lowercase=None )		->     List[str]:
            lowerCAmelCase  						=       []
            for model_output in model_outputs:
                  lowerCAmelCase  						=       model_output["""candidate_label"""]
                  lowerCAmelCase  						=       BaseModelOutput(lowercase )
                  lowerCAmelCase  						=       self.image_processor.post_process_object_detection(
                      outputs=lowercase      ,						threshold=lowercase      ,						target_sizes=model_output["""target_size"""] )[0]

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

                        lowerCAmelCase  						=       {"""score""": score, """label""": label, """box""": box}
                        results.append(lowercase )

            lowerCAmelCase  						=       sorted(lowercase      ,						key=lambda lowercase : x["score"]      ,						reverse=lowercase )
            if top_k:
                  lowerCAmelCase  						=       results[:top_k]

            return results





      def     _snake_case			(       self      ,						lowercase )		->     Dict[str, int]:
            if self.framework != "pt":
                  raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""" )
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       box.int().tolist()
            lowerCAmelCase  						=       {
                """xmin""": xmin,
                """ymin""": ymin,
                """xmax""": xmax,
                """ymax""": ymax,
            }
            return bbox








from __future__ import annotations




def 				a						(							snake_case__:					list[int]						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if (direction == 1 and array[indexa] > array[indexa]) or (
				    direction == 0 and array[indexa] < array[indexa]
				):
								lowercase_  ,	lowercase_       =					array[indexa], array[indexa]




def 				a						(							snake_case__:					list[int]						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if length > 1:
								lowercase_       =					int(length / 2					)
								for i in range(snake_case__						,   low + middle					):
												comp_and_swap(snake_case__						,   snake_case__						,   i + middle						,   snake_case__					)
								bitonic_merge(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
								bitonic_merge(snake_case__						,   low + middle						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					list[int]						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if length > 1:
								lowercase_       =					int(length / 2					)
								bitonic_sort(snake_case__						,   snake_case__						,   snake_case__						,   1					)
								bitonic_sort(snake_case__						,   low + middle						,   snake_case__						,   0					)
								bitonic_merge(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)


if __name__ == "__main__":
						__a  			=					input('Enter numbers separated by a comma:\n').strip()
						__a  			=					[int(item.strip()) for item in user_input.split(',')]

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print('\nSorted array in ascending order is: ', end='')
						print(*unsorted, sep=', ')

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print('Sorted array in descending order is: ', end='')
						print(*unsorted, sep=', ')




