






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

__UpperCAmelCase     							=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case      )
class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		**_UpperCamelCase       )     ->							List[str]:
							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    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							Optional[int]:
							if "text_queries" in kwargs:
												UpperCAmelCase_   :				str						=						kwargs.pop('text_queries'       )

							if isinstance(_UpperCamelCase    ,		(str, Image.Image)       ):
												UpperCAmelCase_   :				Optional[int]						=						{'image': image, 'candidate_labels': candidate_labels}
							else:
												UpperCAmelCase_   :				Optional[int]						=						image
							UpperCAmelCase_   :				Optional[int]						=						super().__call__(_UpperCamelCase    ,		**_UpperCamelCase       )
							return results



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Dict						=						{}
							if "threshold" in kwargs:
												UpperCAmelCase_   :				int						=						kwargs['threshold']
							if "top_k" in kwargs:
												UpperCAmelCase_   :				Any						=						kwargs['top_k']
							return {}, {}, postprocess_params



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				List[str]						=						load_image(inputs['image']       )
							UpperCAmelCase_   :				List[str]						=						inputs['candidate_labels']
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Dict						=						candidate_labels.split(','       )

							UpperCAmelCase_   :				str						=						torch.tensor([[image.height, image.width]]    ,		dtype=torch.intaa       )
							for i, candidate_label in enumerate(_UpperCamelCase       ):
												UpperCAmelCase_   :				Union[str, Any]						=						self.tokenizer(_UpperCamelCase    ,		return_tensors=self.framework       )
												UpperCAmelCase_   :				str						=						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  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				int						=						model_inputs.pop('target_size'       )
							UpperCAmelCase_   :				Optional[int]						=						model_inputs.pop('candidate_label'       )
							UpperCAmelCase_   :				List[str]						=						model_inputs.pop('is_last'       )

							UpperCAmelCase_   :				Dict						=						self.model(**_UpperCamelCase       )

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=None       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						[]
							for model_output in model_outputs:
												UpperCAmelCase_   :				Dict						=						model_output['candidate_label']
												UpperCAmelCase_   :				int						=						BaseModelOutput(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[int]						=						self.image_processor.post_process_object_detection(
												    outputs=_UpperCamelCase    ,		threshold=_UpperCamelCase    ,		target_sizes=model_output['target_size']       )[0]

												for index in outputs["scores"].nonzero():
																	UpperCAmelCase_   :				List[Any]						=						outputs['scores'][index].item()
																	UpperCAmelCase_   :				Union[str, Any]						=						self._get_bounding_box(outputs['boxes'][index][0]       )

																	UpperCAmelCase_   :				Tuple						=						{'score': score, 'label': label, 'box': box}
																	results.append(_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						sorted(_UpperCamelCase    ,		key=lambda _UpperCamelCase       : x["score"]    ,		reverse=_UpperCamelCase       )
							if top_k:
												UpperCAmelCase_   :				List[Any]						=						results[:top_k]

							return results



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














def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






