
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

_lowerCamelCase	:    int          =      logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase )
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   List[str]      ,  **lowercase     :   Optional[int]     ):


				'''simple docstring'''


				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     :   Any      ,  lowercase     :   Union[str, "Image.Image", List[Dict[str, Any]]]      ,  lowercase     :   Union[str, List[str]] = None      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				if "text_queries" in kwargs:
							_snake_case          =      kwargs.pop('text_queries'     )

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



	def 			A							(  self     :   Tuple      ,  **lowercase     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      {}
				if "threshold" in kwargs:
							_snake_case          =      kwargs['threshold']
				if "top_k" in kwargs:
							_snake_case          =      kwargs['top_k']
				return {}, {}, postprocess_params



	def 			A							(  self     :   int      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      load_image(inputs['image']     )
				_snake_case          =      inputs['candidate_labels']
				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      candidate_labels.split(','     )

				_snake_case          =      torch.tensor([[image.height, image.width]]      ,  dtype=torch.intaa     )
				for i, candidate_label in enumerate(lowercase     ):
							_snake_case          =      self.tokenizer(lowercase      ,  return_tensors=self.framework     )
							_snake_case          =      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 			A							(  self     :   Dict      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      model_inputs.pop('target_size'     )
				_snake_case          =      model_inputs.pop('candidate_label'     )
				_snake_case          =      model_inputs.pop('is_last'     )

				_snake_case          =      self.model(**lowercase     )

				_snake_case          =      {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
				return model_outputs



	def 			A							(  self     :   List[str]      ,  lowercase     :   Tuple      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   List[str]=None     ):


				'''simple docstring'''


				_snake_case          =      []
				for model_output in model_outputs:
							_snake_case          =      model_output['candidate_label']
							_snake_case          =      BaseModelOutput(lowercase     )
							_snake_case          =      self.image_processor.post_process_object_detection(
							    outputs=lowercase      ,  threshold=lowercase      ,  target_sizes=model_output['target_size']     )[0]

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

										_snake_case          =      {'score': score, 'label': label, 'box': box}
										results.append(lowercase     )

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

				return results





	def 			A							(  self     :   Dict      ,  lowercase     :   "torch.Tensor"     ):


				'''simple docstring'''


				if self.framework != "pt":
							raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'     )
				_snake_case     , _snake_case     , _snake_case     , _snake_case          =      box.int().tolist()
				_snake_case          =      {
				    'xmin': xmin,
				    'ymin': ymin,
				    'xmax': xmax,
				    'ymax': ymax,
				}
				return bbox

import baseaa






def 	a_		(	__lowercase       :     str			)			->  bytes:
			return baseaa.aaaencode(string.encode('utf-8'			)			)






def 	a_		(	__lowercase       :     bytes			)			->  str:
			return baseaa.aaadecode(__lowercase			).decode('utf-8'			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()