



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(_UpperCamelCase     )
class  snake_case__(_UpperCamelCase     ):

					"""simple docstring"""






					def __init__(				self   :      int ,  **SCREAMING_SNAKE_CASE   :      List[Any]	):
									super().__init__(**_lowerCAmelCase	)
									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(_lowerCAmelCase	)







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







					def __call__(				self   :      Dict ,  SCREAMING_SNAKE_CASE   :      int ,  *SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      List[str]=None ,  SCREAMING_SNAKE_CASE   :      List[str]=None ,  **SCREAMING_SNAKE_CASE   :      Union[str, Any]	):
									return super().__call__(_lowerCAmelCase ,  *_lowerCAmelCase ,  num_workers=_lowerCAmelCase ,  batch_size=_lowerCAmelCase ,  **_lowerCAmelCase	)







					def 					snake_case	(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      Tuple ,  SCREAMING_SNAKE_CASE   :      str=64 ,  SCREAMING_SNAKE_CASE   :      int = 0 ,  SCREAMING_SNAKE_CASE   :      float = 512 / 1_500 ,  SCREAMING_SNAKE_CASE   :      Optional[int] = 32 ,  SCREAMING_SNAKE_CASE   :      Optional[int] = 1 ,  ):
									lowercase__	:       str						=	load_image(_lowerCAmelCase	)
									lowercase__	:       int						=	self.image_processor.size["longest_edge"]
									lowercase__				,				lowercase__				,				lowercase__				,				lowercase__	:       Any						=	self.image_processor.generate_crop_boxes(
									    _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase	)
									lowercase__	:       int						=	self.image_processor(images=_lowerCAmelCase ,  return_tensors="pt"	)

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

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

									lowercase__	:       Any						=	self.model(**_lowerCAmelCase	)

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







					def 					snake_case	(				self   :      str ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Tuple=False ,  SCREAMING_SNAKE_CASE   :      List[str]=False ,  SCREAMING_SNAKE_CASE   :      Tuple=0.7 ,  ):
									lowercase__	:       Union[str, Any]						=	[]
									lowercase__	:       Union[str, Any]						=	[]
									lowercase__	:       Dict						=	[]
									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"	)	)

									lowercase__	:       str						=	torch.cat(_lowerCAmelCase	)
									lowercase__	:       List[Any]						=	torch.cat(_lowerCAmelCase	)
									lowercase__				,				lowercase__				,				lowercase__				,				lowercase__	:       Optional[Any]						=	self.image_processor.post_process_for_mask_generation(
									    _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase	)

									lowercase__	:       List[Any]						=	defaultdict(_lowerCAmelCase	)
									for output in model_outputs:
													for k, v in output.items():
																	extra[k].append(_lowerCAmelCase	)

									lowercase__	:       Any						=	{}
									if output_rle_mask:
													lowercase__	:       Any						=	rle_mask

									if output_bboxes_mask:
													lowercase__	:       Tuple						=	bounding_boxes

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








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


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


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							['pixel_values']



  def __init__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							PILImageResampling = PIL.Image.BICUBIC     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							Union[int, float] = 1 / 2_5_5     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					**_lowerCAmelCase							:							Optional[Any]     ,					) ->			None:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)
     snake_case_            =       size if size is not None else {"height": 2_5_6, "width": 2_5_6}
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     snake_case_            =       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
     snake_case_            =       get_size_dict(_lowerCAmelCase     ,					param_name="crop_size"	)

     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       resample
     snake_case_            =       do_center_crop
     snake_case_            =       crop_size
     snake_case_            =       do_rescale
     snake_case_            =       rescale_factor
     snake_case_            =       do_normalize
     snake_case_            =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     snake_case_            =       image_std if image_std is not None else IMAGENET_STANDARD_STD



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Dict[str, int]     ,					_lowerCAmelCase							:							PILImageResampling = PIL.Image.BICUBIC     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							int     ,					) ->			np.ndarray:




     """simple docstring"""
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     if "height" not in size or "width" not in size:
        raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
     return resize(
         _lowerCAmelCase     ,					size=(size["height"], size["width"])     ,					resample=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Dict[str, int]     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							int     ,					) ->			np.ndarray:




     """simple docstring"""
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     if "height" not in size or "width" not in size:
        raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
     return center_crop(_lowerCAmelCase     ,					size=(size["height"], size["width"])     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Union[int, float]     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							Union[str, Any]     ,					) ->			Optional[Any]:




     """simple docstring"""
     return rescale(_lowerCAmelCase     ,					scale=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Union[float, List[float]]     ,					_lowerCAmelCase							:							Union[float, List[float]]     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							Any     ,					) ->			np.ndarray:




     """simple docstring"""
     return normalize(_lowerCAmelCase     ,					mean=_lowerCAmelCase     ,					std=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							ImageInput     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							int=None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							float = None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[str, TensorType]] = None     ,					_lowerCAmelCase							:							ChannelDimension = ChannelDimension.FIRST     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			PIL.Image.Image:




     """simple docstring"""
     snake_case_            =       do_resize if do_resize is not None else self.do_resize
     snake_case_            =       resample if resample is not None else self.resample
     snake_case_            =       do_center_crop if do_center_crop is not None else self.do_center_crop
     snake_case_            =       do_rescale if do_rescale is not None else self.do_rescale
     snake_case_            =       rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_            =       do_normalize if do_normalize is not None else self.do_normalize
     snake_case_            =       image_mean if image_mean is not None else self.image_mean
     snake_case_            =       image_std if image_std is not None else self.image_std

     snake_case_            =       size if size is not None else self.size
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     snake_case_            =       crop_size if crop_size is not None else self.crop_size
     snake_case_            =       get_size_dict(_lowerCAmelCase     ,					param_name="crop_size"	)

     snake_case_            =       make_list_of_images(_lowerCAmelCase	)

     if not valid_images(_lowerCAmelCase	):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray."	)

     if do_resize and size is None or resample is None:
        raise ValueError("Size and resample must be specified if do_resize is True."	)

     if do_center_crop and crop_size is None:
        raise ValueError("Crop size must be specified if do_center_crop is True."	)

     if do_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True."	)

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True."	)

     # All transformations expect numpy arrays.
     snake_case_            =       [to_numpy_array(_lowerCAmelCase	) for image in images]

     if do_resize:
        snake_case_            =       [self.resize(image=_lowerCAmelCase     ,					size=_lowerCAmelCase     ,					resample=_lowerCAmelCase	) for image in images]

     if do_center_crop:
        snake_case_            =       [self.center_crop(image=_lowerCAmelCase     ,					size=_lowerCAmelCase	) for image in images]

     if do_rescale:
        snake_case_            =       [self.rescale(image=_lowerCAmelCase     ,					scale=_lowerCAmelCase	) for image in images]

     if do_normalize:
        snake_case_            =       [self.normalize(image=_lowerCAmelCase     ,					mean=_lowerCAmelCase     ,					std=_lowerCAmelCase	) for image in images]

     snake_case_            =       [to_channel_dimension_format(_lowerCAmelCase     ,					_lowerCAmelCase	) for image in images]

     snake_case_            =       {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase     ,					tensor_type=_lowerCAmelCase	)




