

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 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      **lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase       )
							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(lowercase       )


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			{}
							A__        =			{}
							A__        =			{}
							# preprocess args
							if "points_per_batch" in kwargs:
											A__        =			kwargs["points_per_batch"]
							if "points_per_crop" in kwargs:
											A__        =			kwargs["points_per_crop"]
							if "crops_n_layers" in kwargs:
											A__        =			kwargs["crops_n_layers"]
							if "crop_overlap_ratio" in kwargs:
											A__        =			kwargs["crop_overlap_ratio"]
							if "crop_n_points_downscale_factor" in kwargs:
											A__        =			kwargs["crop_n_points_downscale_factor"]
							# postprocess args
							if "pred_iou_thresh" in kwargs:
											A__        =			kwargs["pred_iou_thresh"]
							if "stability_score_offset" in kwargs:
											A__        =			kwargs["stability_score_offset"]
							if "mask_threshold" in kwargs:
											A__        =			kwargs["mask_threshold"]
							if "stability_score_thresh" in kwargs:
											A__        =			kwargs["stability_score_thresh"]
							if "crops_nms_thresh" in kwargs:
											A__        =			kwargs["crops_nms_thresh"]
							if "output_rle_mask" in kwargs:
											A__        =			kwargs["output_rle_mask"]
							if "output_bboxes_mask" in kwargs:
											A__        =			kwargs["output_bboxes_mask"]
							return preprocess_kwargs, forward_params, postprocess_kwargs


			def __call__(    self      ,      lowercase      ,      *lowercase      ,      lowercase=None      ,      lowercase=None      ,      **lowercase       )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=64      ,      lowercase = 0      ,      lowercase = 512 / 1500      ,      lowercase = 32      ,      lowercase = 1      ,      )  ->						str:
							'''simple docstring'''






							A__        =			load_image(lowercase       )
							A__        =			self.image_processor.size["longest_edge"]
							A__    ,				A__    ,				A__    ,				A__        =			self.image_processor.generate_crop_boxes(
							    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )
							A__        =			self.image_processor(images=lowercase      ,      return_tensors="pt"       )

							with self.device_placement():
											if self.framework == "pt":
															A__        =			self.get_inference_context()
															with inference_context():
																			A__        =			self._ensure_tensor_on_device(lowercase      ,      device=self.device       )
																			A__        =			self.model.get_image_embeddings(model_inputs.pop("pixel_values"       )       )
																			A__        =			image_embeddings

							A__        =			grid_points.shape[1]
							A__        =			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      ,      lowercase      ,      lowercase       ):
											A__        =			grid_points[:, i : i + points_per_batch, :, :]
											A__        =			input_labels[:, i : i + points_per_batch]
											A__        =			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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=0.88      ,      lowercase=0.95      ,      lowercase=0      ,      lowercase=1      ,      )  ->						Dict:
							'''simple docstring'''






							A__        =			model_inputs.pop("input_boxes"       )
							A__        =			model_inputs.pop("is_last"       )
							A__        =			model_inputs.pop("original_sizes"       ).tolist()
							A__        =			model_inputs.pop("reshaped_input_sizes"       ).tolist()

							A__        =			self.model(**lowercase       )

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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=False      ,      lowercase=False      ,      lowercase=0.7      ,      )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			[]
							A__        =			[]
							A__        =			[]
							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"       )       )

							A__        =			torch.cat(lowercase       )
							A__        =			torch.cat(lowercase       )
							A__    ,				A__    ,				A__    ,				A__        =			self.image_processor.post_process_for_mask_generation(
							    lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )

							A__        =			defaultdict(lowercase       )
							for output in model_outputs:
											for k, v in output.items():
															extra[k].append(lowercase       )

							A__        =			{}
							if output_rle_mask:
											A__        =			rle_mask

							if output_bboxes_mask:
											A__        =			bounding_boxes

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




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
								A__        =			v.half()
				if save_path is None:  # overwrite src_path
								A__        =			src_path
				torch.save(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	fire.Fire(convert)

