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

SCREAMING_SNAKE_CASE		:     List[Any]         =	logging.get_logger(__name__)

@add_end_docstrings(__a				)
class        UpperCamelCase						(				__a				):





						def __init__(self			,     **__UpperCamelCase						)				->				Any:
												super().__init__(**__UpperCamelCase						)
												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(__UpperCamelCase						)





						def        A_							(self			,     **__UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[str]  =		{}
												UpperCamelCase_		:							Any  =		{}
												UpperCamelCase_		:							str  =		{}
												# preprocess args
												if "points_per_batch" in kwargs:
																		UpperCamelCase_		:							Union[str, Any]  =		kwargs["""points_per_batch"""]
												if "points_per_crop" in kwargs:
																		UpperCamelCase_		:							Tuple  =		kwargs["""points_per_crop"""]
												if "crops_n_layers" in kwargs:
																		UpperCamelCase_		:							Tuple  =		kwargs["""crops_n_layers"""]
												if "crop_overlap_ratio" in kwargs:
																		UpperCamelCase_		:							Optional[int]  =		kwargs["""crop_overlap_ratio"""]
												if "crop_n_points_downscale_factor" in kwargs:
																		UpperCamelCase_		:							List[str]  =		kwargs["""crop_n_points_downscale_factor"""]
												# postprocess args
												if "pred_iou_thresh" in kwargs:
																		UpperCamelCase_		:							int  =		kwargs["""pred_iou_thresh"""]
												if "stability_score_offset" in kwargs:
																		UpperCamelCase_		:							Any  =		kwargs["""stability_score_offset"""]
												if "mask_threshold" in kwargs:
																		UpperCamelCase_		:							List[Any]  =		kwargs["""mask_threshold"""]
												if "stability_score_thresh" in kwargs:
																		UpperCamelCase_		:							Optional[int]  =		kwargs["""stability_score_thresh"""]
												if "crops_nms_thresh" in kwargs:
																		UpperCamelCase_		:							str  =		kwargs["""crops_nms_thresh"""]
												if "output_rle_mask" in kwargs:
																		UpperCamelCase_		:							str  =		kwargs["""output_rle_mask"""]
												if "output_bboxes_mask" in kwargs:
																		UpperCamelCase_		:							Optional[int]  =		kwargs["""output_bboxes_mask"""]
												return preprocess_kwargs, forward_params, postprocess_kwargs





						def __call__(self			,     __UpperCamelCase			,     *__UpperCamelCase			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     **__UpperCamelCase						)				->				int:
												return super().__call__(__UpperCamelCase			,     *__UpperCamelCase			,     num_workers=__UpperCamelCase			,     batch_size=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=64			,     __UpperCamelCase = 0			,     __UpperCamelCase = 512 / 1_500			,     __UpperCamelCase = 32			,     __UpperCamelCase = 1			,     )				->				Dict:
												UpperCamelCase_		:							int  =		load_image(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		self.image_processor.size["""longest_edge"""]
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.image_processor.generate_crop_boxes(
												    __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		self.image_processor(images=__UpperCamelCase			,     return_tensors="""pt"""						)

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

												UpperCamelCase_		:							List[str]  =		grid_points.shape[1]
												UpperCamelCase_		:							List[Any]  =		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			,     __UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							Any  =		grid_points[:, i : i + points_per_batch, :, :]
																		UpperCamelCase_		:							Union[str, Any]  =		input_labels[:, i : i + points_per_batch]
																		UpperCamelCase_		:							int  =		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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=0.88			,     __UpperCamelCase=0.95			,     __UpperCamelCase=0			,     __UpperCamelCase=1			,     )				->				List[str]:
												UpperCamelCase_		:							Any  =		model_inputs.pop("""input_boxes"""						)
												UpperCamelCase_		:							List[str]  =		model_inputs.pop("""is_last"""						)
												UpperCamelCase_		:							int  =		model_inputs.pop("""original_sizes"""						).tolist()
												UpperCamelCase_		:							int  =		model_inputs.pop("""reshaped_input_sizes"""						).tolist()

												UpperCamelCase_		:							Optional[Any]  =		self.model(**__UpperCamelCase						)

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





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=False			,     __UpperCamelCase=False			,     __UpperCamelCase=0.7			,     )				->				str:
												UpperCamelCase_		:							Optional[int]  =		[]
												UpperCamelCase_		:							str  =		[]
												UpperCamelCase_		:							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"""						)						)

												UpperCamelCase_		:							Optional[int]  =		torch.cat(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		torch.cat(__UpperCamelCase						)
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.image_processor.post_process_for_mask_generation(
												    __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)

												UpperCamelCase_		:							Dict  =		defaultdict(__UpperCamelCase						)
												for output in model_outputs:
																		for k, v in output.items():
																								extra[k].append(__UpperCamelCase						)

												UpperCamelCase_		:							Any  =		{}
												if output_rle_mask:
																		UpperCamelCase_		:							Optional[Any]  =		rle_mask

												if output_bboxes_mask:
																		UpperCamelCase_		:							Tuple  =		bounding_boxes

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







from __future__ import annotations






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (electron_conc, hole_conc, intrinsic_conc).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""			)
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""			)
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""			)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






