




"""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

A_      			=       logging.get_logger(__name__)






@add_end_docstrings(__a				)
class       lowercase(  __a				):







 '''simple docstring'''





 def __init__(       self:	Any,   **a_:	Tuple	):




   '''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 				UpperCamelCase_     (       self:	List[Any],   **a_:	str	):




   '''simple docstring'''



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


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




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Any,   a_:	Any=64,   a_:	int = 0,   a_:	float = 512 / 1_500,   a_:	Optional[int] = 32,   a_:	Optional[int] = 1,   ):




   '''simple docstring'''



   _snake_case							:	List[str]			=						load_image(a_	)
   _snake_case							:	str			=						self.image_processor.size["""longest_edge"""]
   _snake_case	,      _snake_case	,      _snake_case	,      _snake_case							:	List[Any]			=						self.image_processor.generate_crop_boxes(
       a_,   a_,   a_,   a_,   a_,   a_	)
   _snake_case							:	int			=						self.image_processor(images=a_,   return_tensors="""pt"""	)

   with self.device_placement():
     if self.framework == "pt":
       _snake_case							:	Tuple			=						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							:	str			=						image_embeddings

   _snake_case							:	List[str]			=						grid_points.shape[1]
   _snake_case							:	Optional[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_	):
     _snake_case							:	Optional[Any]			=						grid_points[:, i : i + points_per_batch, :, :]
     _snake_case							:	str			=						input_labels[:, i : i + points_per_batch]
     _snake_case							:	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:	str,   a_:	str,   a_:	Optional[Any]=0.88,   a_:	Optional[Any]=0.95,   a_:	Dict=0,   a_:	Any=1,   ):




   '''simple docstring'''



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

   _snake_case							:	List[Any]			=						self.model(**a_	)

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




   '''simple docstring'''



   _snake_case							:	Tuple			=						[]
   _snake_case							:	List[Any]			=						[]
   _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"""	)	)

   _snake_case							:	List[Any]			=						torch.cat(a_	)
   _snake_case							:	int			=						torch.cat(a_	)
   _snake_case	,      _snake_case	,      _snake_case	,      _snake_case							:	Any			=						self.image_processor.post_process_for_mask_generation(
       a_,   a_,   a_,   a_	)

   _snake_case							:	List[str]			=						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							:	List[str]			=						rle_mask

   if output_bboxes_mask:
     _snake_case							:	Union[str, Any]			=						bounding_boxes

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








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


