





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

_A					:				Optional[Any]				=			logging.get_logger(__name__)



@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				**A_     ):





        '''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  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

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



    def __call__(       self   ,				A_   ,				*A_   ,				A_=None   ,				A_=None   ,				**A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_=64   ,				A_ = 0   ,				A_ = 5_12 / 15_00   ,				A_ = 32   ,				A_ = 1   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			load_image(A_     )
        SCREAMING_SNAKE_CASE__											=			self.image_processor.size['''longest_edge''']
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor.generate_crop_boxes(
            A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			self.image_processor(images=A_   ,				return_tensors='''pt'''     )

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

        SCREAMING_SNAKE_CASE__											=			grid_points.shape[1]
        SCREAMING_SNAKE_CASE__											=			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__											=			grid_points[:, i : i + points_per_batch, :, :]
            SCREAMING_SNAKE_CASE__											=			input_labels[:, i : i + points_per_batch]
            SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_=0.88   ,				A_=0.95   ,				A_=0   ,				A_=1   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			model_inputs.pop('''input_boxes'''     )
        SCREAMING_SNAKE_CASE__											=			model_inputs.pop('''is_last'''     )
        SCREAMING_SNAKE_CASE__											=			model_inputs.pop('''original_sizes'''     ).tolist()
        SCREAMING_SNAKE_CASE__											=			model_inputs.pop('''reshaped_input_sizes'''     ).tolist()

        SCREAMING_SNAKE_CASE__											=			self.model(**A_     )

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        SCREAMING_SNAKE_CASE__											=			model_outputs['''pred_masks''']
        SCREAMING_SNAKE_CASE__											=			self.image_processor.post_process_masks(
            A_   ,				A_   ,				A_   ,				A_   ,				binarize=A_     )
        SCREAMING_SNAKE_CASE__											=			model_outputs['''iou_scores''']
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_=False   ,				A_=False   ,				A_=0.7   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        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__											=			torch.cat(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.cat(A_     )
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor.post_process_for_mask_generation(
            A_   ,				A_   ,				A_   ,				A_     )

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

        SCREAMING_SNAKE_CASE__											=			{}
        if output_rle_mask:
            SCREAMING_SNAKE_CASE__											=			rle_mask

        if output_bboxes_mask:
            SCREAMING_SNAKE_CASE__											=			bounding_boxes

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







from torch import nn


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    if act_fn in ["swish", "silu"]:
        return nn.SiLU()
    elif act_fn == "mish":
        return nn.Mish()
    elif act_fn == "gelu":
        return nn.GELU()
    else:
        raise ValueError(f'''Unsupported activation function: {act_fn}'''	)
