





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

__UpperCAmelCase       :   int				       =							logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase			)
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







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




       def __call__(  self :    int							,				A :    Tuple							,				*A :    List[str]							,				A :    int=None							,				A :    List[str]=None							,				**A :    Tuple		):
         return super().__call__(A							,				*A							,				num_workers=A							,				batch_size=A							,				**A		)




       def 							UpperCAmelCase__ (  self :    int							,				A :    int							,				A :    Any=64							,				A :    int = 0							,				A :    float = 512 / 1_500							,				A :    Optional[int] = 32							,				A :    Optional[int] = 1							,				):
         __snake_case:						Optional[Any]   =						load_image(A		)
         __snake_case:						Optional[Any]   =						self.image_processor.size["""longest_edge"""]
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Union[str, Any]   =						self.image_processor.generate_crop_boxes(
             A							,				A							,				A							,				A							,				A							,				A		)
         __snake_case:						str   =						self.image_processor(images=A							,				return_tensors="""pt"""		)

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

         __snake_case:						Tuple   =						grid_points.shape[1]
         __snake_case:						Tuple   =						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		):
           __snake_case:						List[str]   =						grid_points[:, i : i + points_per_batch, :, :]
           __snake_case:						Optional[Any]   =						input_labels[:, i : i + points_per_batch]
           __snake_case:						Optional[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 							UpperCAmelCase__ (  self :    List[str]							,				A :    Tuple							,				A :    str=0.88							,				A :    Union[str, Any]=0.95							,				A :    Optional[int]=0							,				A :    Optional[int]=1							,				):
         __snake_case:						Any   =						model_inputs.pop("""input_boxes"""		)
         __snake_case:						List[str]   =						model_inputs.pop("""is_last"""		)
         __snake_case:						int   =						model_inputs.pop("""original_sizes"""		).tolist()
         __snake_case:						int   =						model_inputs.pop("""reshaped_input_sizes"""		).tolist()

         __snake_case:						Optional[Any]   =						self.model(**A		)

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         __snake_case:						Union[str, Any]   =						model_outputs["""pred_masks"""]
         __snake_case:						int   =						self.image_processor.post_process_masks(
             A							,				A							,				A							,				A							,				binarize=A		)
         __snake_case:						List[Any]   =						model_outputs["""iou_scores"""]
         __snake_case  ,   __snake_case  ,   __snake_case:						Any   =						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 							UpperCAmelCase__ (  self :    Any							,				A :    Dict							,				A :    Optional[Any]=False							,				A :    Dict=False							,				A :    Dict=0.7							,				):
         __snake_case:						Optional[Any]   =						[]
         __snake_case:						Dict   =						[]
         __snake_case:						Optional[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"""		)		)

         __snake_case:						List[Any]   =						torch.cat(A		)
         __snake_case:						Any   =						torch.cat(A		)
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Optional[int]   =						self.image_processor.post_process_for_mask_generation(
             A							,				A							,				A							,				A		)

         __snake_case:						Union[str, Any]   =						defaultdict(A		)
         for output in model_outputs:
           for k, v in output.items():
             extra[k].append(A		)

         __snake_case:						str   =						{}
         if output_rle_mask:
           __snake_case:						Union[str, Any]   =						rle_mask

         if output_bboxes_mask:
           __snake_case:						Optional[Any]   =						bounding_boxes

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













import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__UpperCAmelCase       :   Tuple				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				*A :    Union[str, Any]							,				**A :    Optional[int]		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,				A							,				)
         super().__init__(*A							,				**A		)






