"""simple docstring"""





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCAmelCase_     =	logging.get_logger(__name__)




@add_end_docstrings(__snake_case )
class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def __init__(      self							:							List[str]      ,    **_A							:							Union[str, Any]):



											"""simple docstring"""







											super().__init__(**_A)
											requires_backends(self      ,    """vision""")
											requires_backends(self      ,    """torch""")

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

											self.check_model_type(_A)





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    **_A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}
											_SCREAMING_SNAKE_CASE		:						Dict			  =    {}
											_SCREAMING_SNAKE_CASE		:						Dict			  =    {}
											# preprocess args
											if "points_per_batch" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    kwargs["""points_per_batch"""]
											if "points_per_crop" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    kwargs["""points_per_crop"""]
											if "crops_n_layers" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    kwargs["""crops_n_layers"""]
											if "crop_overlap_ratio" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs["""crop_overlap_ratio"""]
											if "crop_n_points_downscale_factor" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs["""crop_n_points_downscale_factor"""]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    kwargs["""pred_iou_thresh"""]
											if "stability_score_offset" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    kwargs["""stability_score_offset"""]
											if "mask_threshold" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						int			  =    kwargs["""mask_threshold"""]
											if "stability_score_thresh" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						str			  =    kwargs["""stability_score_thresh"""]
											if "crops_nms_thresh" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs["""crops_nms_thresh"""]
											if "output_rle_mask" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						int			  =    kwargs["""output_rle_mask"""]
											if "output_bboxes_mask" in kwargs:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    kwargs["""output_bboxes_mask"""]
											return preprocess_kwargs, forward_params, postprocess_kwargs





				def __call__(      self							:							str      ,    _A							:							Any      ,    *_A							:							int      ,    _A							:							Optional[Any]=None      ,    _A							:							Optional[Any]=None      ,    **_A							:							Union[str, Any]):



											"""simple docstring"""







											return super().__call__(_A      ,    *_A      ,    num_workers=_A      ,    batch_size=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int      ,    _A							:							Union[str, Any]=6_4      ,    _A							:							int = 0      ,    _A							:							float = 5_1_2 / 1_5_0_0      ,    _A							:							Optional[int] = 3_2      ,    _A							:							Optional[int] = 1      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    load_image(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.image_processor.size["""longest_edge"""]
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.image_processor.generate_crop_boxes(
											    _A      ,    _A      ,    _A      ,    _A      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.image_processor(images=_A      ,    return_tensors="""pt""")

											with self.device_placement():
																		if self.framework == "pt":
																									_SCREAMING_SNAKE_CASE		:						Dict			  =    self.get_inference_context()
																									with inference_context():
																																_SCREAMING_SNAKE_CASE		:						Dict			  =    self._ensure_tensor_on_device(_A      ,    device=self.device)
																																_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""))
																																_SCREAMING_SNAKE_CASE		:						List[Any]			  =    image_embeddings

											_SCREAMING_SNAKE_CASE		:						Dict			  =    grid_points.shape[1]
											_SCREAMING_SNAKE_CASE		:						int			  =    points_per_batch if points_per_batch is not None else n_points

											if points_per_batch <= 0:
																		raise ValueError(
																		    """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
																		    """To return all points at once, set points_per_batch to None""")

											for i in range(0      ,    _A      ,    _A):
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    grid_points[:, i : i + points_per_batch, :, :]
																		_SCREAMING_SNAKE_CASE		:						Any			  =    input_labels[:, i : i + points_per_batch]
																		_SCREAMING_SNAKE_CASE		:						Any			  =    i == n_points - points_per_batch
																		yield {
																		    "input_points": batched_points,
																		    "input_labels": labels,
																		    "input_boxes": crop_boxes,
																		    "is_last": is_last,
																		    **model_inputs,
																		}





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Optional[int]      ,    _A							:							Union[str, Any]=0.88      ,    _A							:							List[str]=0.95      ,    _A							:							Union[str, Any]=0      ,    _A							:							Union[str, Any]=1      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model_inputs.pop("""input_boxes""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    model_inputs.pop("""is_last""")
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model_inputs.pop("""original_sizes""").tolist()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model_inputs.pop("""reshaped_input_sizes""").tolist()

											_SCREAMING_SNAKE_CASE		:						str			  =    self.model(**_A)

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											_SCREAMING_SNAKE_CASE		:						Any			  =    model_outputs["""pred_masks"""]
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.image_processor.post_process_masks(
											    _A      ,    _A      ,    _A      ,    _A      ,    binarize=_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model_outputs["""iou_scores"""]
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.image_processor.filter_masks(
											    masks[0]      ,    iou_scores[0]      ,    original_sizes[0]      ,    input_boxes[0]      ,    _A      ,    _A      ,    _A      ,    _A      ,    )
											return {
											    "masks": masks,
											    "is_last": is_last,
											    "boxes": boxes,
											    "iou_scores": iou_scores,
											}





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Tuple      ,    _A							:							Tuple=False      ,    _A							:							int=False      ,    _A							:							Dict=0.7      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    []
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
											for model_output in model_outputs:
																		all_scores.append(model_output.pop("""iou_scores"""))
																		all_masks.extend(model_output.pop("""masks"""))
																		all_boxes.append(model_output.pop("""boxes"""))

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.cat(_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.cat(_A)
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor.post_process_for_mask_generation(
											    _A      ,    _A      ,    _A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    defaultdict(_A)
											for output in model_outputs:
																		for k, v in output.items():
																									extra[k].append(_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    {}
											if output_rle_mask:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    rle_mask

											if output_bboxes_mask:
																		_SCREAMING_SNAKE_CASE		:						int			  =    bounding_boxes

											return {"masks": output_masks, "scores": iou_scores, **optional, **extra}



"""simple docstring"""





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if dst_width < 0 or dst_height < 0:
																		raise ValueError("""Destination width/height should be > 0""")

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			lowerCAmelCase_     =	imread('''image_data/lena.jpg''', 1)
			lowerCAmelCase_     =	NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
			)
			waitKey(0)
			destroyAllWindows()


