




"""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(lowercase		)
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			def __init__(						self						,   **lowercase						):
							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						,   ):
							if "text_queries" in kwargs:
											_lowerCamelCase  :    List[Any]					=					kwargs.pop('text_queries'						)

							if isinstance(lowercase						,   (str, Image.Image)						):
											_lowerCamelCase  :    Any					=					{'image': image, 'candidate_labels': candidate_labels}
							else:
											_lowerCamelCase  :    Optional[Any]					=					image
							_lowerCamelCase  :    Dict					=					super().__call__(lowercase						,   **lowercase						)
							return results
			def       A_					(						self						,   **lowercase						):
							_lowerCamelCase  :    Any					=					{}
							if "threshold" in kwargs:
											_lowerCamelCase  :    Optional[Any]					=					kwargs['threshold']
							if "top_k" in kwargs:
											_lowerCamelCase  :    int					=					kwargs['top_k']
							return {}, {}, postprocess_params
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					load_image(inputs['image']						)
							_lowerCamelCase  :    List[Any]					=					inputs['candidate_labels']
							if isinstance(lowercase						,   lowercase						):
											_lowerCamelCase  :    List[str]					=					candidate_labels.split(','						)

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

							_lowerCamelCase  :    Optional[Any]					=					self.model(**lowercase						)

							_lowerCamelCase  :    str					=					{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
							return model_outputs
			def       A_					(						self						,   lowercase						,   lowercase=0.1						,   lowercase=None						):
							_lowerCamelCase  :    Optional[int]					=					[]
							for model_output in model_outputs:
											_lowerCamelCase  :    Optional[int]					=					model_output['candidate_label']
											_lowerCamelCase  :    Optional[Any]					=					BaseModelOutput(lowercase						)
											_lowerCamelCase  :    Dict					=					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  :    int					=					outputs['scores'][index].item()
															_lowerCamelCase  :    Any					=					self._get_bounding_box(outputs['boxes'][index][0]						)

															_lowerCamelCase  :    List[Any]					=					{'score': score, 'label': label, 'box': box}
															results.append(lowercase						)

							_lowerCamelCase  :    Dict					=					sorted(lowercase						,   key=lambda lowercase						: x["score"]						,   reverse=lowercase						)
							if top_k:
											_lowerCamelCase  :    List[str]					=					results[:top_k]

							return results






			def       A_					(						self						,   lowercase						):
							if self.framework != "pt":
											raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					box.int().tolist()
							_lowerCamelCase  :    Any					=					{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				return "".join(chr(ord(lowercase__	) - 32	) if 'a' <= char <= 'z' else char for char in word	)


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

			testmod()