
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

_lowercase     							=						logging.get_logger(__name__)
@add_end_docstrings(A_      )
class 				__A					( A_      ):





			def __init__(self							,    **__magic_name__				):
						super().__init__(**__magic_name__				)
						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(__magic_name__				)





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





			def __call__(self							,    __magic_name__							,    *__magic_name__							,    __magic_name__=None							,    __magic_name__=None							,    **__magic_name__				):
						return super().__call__(__magic_name__							,    *__magic_name__							,    num_workers=__magic_name__							,    batch_size=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__=64							,    __magic_name__ = 0							,    __magic_name__ = 512 / 1500							,    __magic_name__ = 32							,    __magic_name__ = 1							,    ):
						lowerCamelCase__ :      str								=		load_image(__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		self.image_processor.size["""longest_edge"""]
						lowerCamelCase__ :      List[str]								=		self.image_processor.generate_crop_boxes(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      List[str]								=		self.image_processor(images=__magic_name__							,    return_tensors="""pt"""				)

						with self.device_placement():
									if self.framework == "pt":
												lowerCamelCase__ :      str								=		self.get_inference_context()
												with inference_context():
															lowerCamelCase__ :      int								=		self._ensure_tensor_on_device(__magic_name__							,    device=self.device				)
															lowerCamelCase__ :      List[str]								=		self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""				)				)
															lowerCamelCase__ :      int								=		image_embeddings

						lowerCamelCase__ :      str								=		grid_points.shape[1]
						lowerCamelCase__ :      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							,    __magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Any								=		grid_points[:, i : i + points_per_batch, :, :]
									lowerCamelCase__ :      List[str]								=		input_labels[:, i : i + points_per_batch]
									lowerCamelCase__ :      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							,    __magic_name__							,    __magic_name__=0.88							,    __magic_name__=0.95							,    __magic_name__=0							,    __magic_name__=1							,    ):
						lowerCamelCase__ :      Tuple								=		model_inputs.pop("""input_boxes"""				)
						lowerCamelCase__ :      List[str]								=		model_inputs.pop("""is_last"""				)
						lowerCamelCase__ :      Union[str, Any]								=		model_inputs.pop("""original_sizes"""				).tolist()
						lowerCamelCase__ :      List[str]								=		model_inputs.pop("""reshaped_input_sizes"""				).tolist()

						lowerCamelCase__ :      Any								=		self.model(**__magic_name__				)

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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__=False							,    __magic_name__=False							,    __magic_name__=0.7							,    ):
						lowerCamelCase__ :      int								=		[]
						lowerCamelCase__ :      Optional[int]								=		[]
						lowerCamelCase__ :      List[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"""				)				)

						lowerCamelCase__ :      Union[str, Any]								=		torch.cat(__magic_name__				)
						lowerCamelCase__ :      Any								=		torch.cat(__magic_name__				)
						lowerCamelCase__ :      Dict								=		self.image_processor.post_process_for_mask_generation(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)

						lowerCamelCase__ :      Optional[int]								=		defaultdict(__magic_name__				)
						for output in model_outputs:
									for k, v in output.items():
												extra[k].append(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		{}
						if output_rle_mask:
									lowerCamelCase__ :      Dict								=		rle_mask

						if output_bboxes_mask:
									lowerCamelCase__ :      str								=		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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''pixel_values''']





			def __init__(self							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = 1 / 255							,    __magic_name__ = True							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(**__magic_name__				)
						lowerCamelCase__ :      str								=		size if size is not None else {"""height""": 256, """width""": 256}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      str								=		do_resize
						lowerCamelCase__ :      Any								=		size
						lowerCamelCase__ :      Tuple								=		resample
						lowerCamelCase__ :      Union[str, Any]								=		do_center_crop
						lowerCamelCase__ :      List[str]								=		crop_size
						lowerCamelCase__ :      int								=		do_rescale
						lowerCamelCase__ :      Any								=		rescale_factor
						lowerCamelCase__ :      Dict								=		do_normalize
						lowerCamelCase__ :      Dict								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCamelCase__ :      int								=		image_std if image_std is not None else IMAGENET_STANDARD_STD





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						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(
						    __magic_name__							,    size=(size["""height"""], size["""width"""])							,    resample=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						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(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return normalize(__magic_name__							,    mean=__magic_name__							,    std=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = ChannelDimension.FIRST							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		do_resize if do_resize is not None else self.do_resize
						lowerCamelCase__ :      str								=		resample if resample is not None else self.resample
						lowerCamelCase__ :      str								=		do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase__ :      Optional[int]								=		do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase__ :      str								=		rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase__ :      Union[str, Any]								=		do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase__ :      Tuple								=		image_mean if image_mean is not None else self.image_mean
						lowerCamelCase__ :      List[Any]								=		image_std if image_std is not None else self.image_std

						lowerCamelCase__ :      str								=		size if size is not None else self.size
						lowerCamelCase__ :      List[str]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      Optional[Any]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      Optional[int]								=		make_list_of_images(__magic_name__				)

						if not valid_images(__magic_name__				):
									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.
						lowerCamelCase__ :      Optional[int]								=		[to_numpy_array(__magic_name__				) for image in images]

						if do_resize:
									lowerCamelCase__ :      int								=		[self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				) for image in images]

						if do_center_crop:
									lowerCamelCase__ :      List[Any]								=		[self.center_crop(image=__magic_name__							,    size=__magic_name__				) for image in images]

						if do_rescale:
									lowerCamelCase__ :      Dict								=		[self.rescale(image=__magic_name__							,    scale=__magic_name__				) for image in images]

						if do_normalize:
									lowerCamelCase__ :      Optional[Any]								=		[self.normalize(image=__magic_name__							,    mean=__magic_name__							,    std=__magic_name__				) for image in images]

						lowerCamelCase__ :      List[str]								=		[to_channel_dimension_format(__magic_name__							,    __magic_name__				) for image in images]

						lowerCamelCase__ :      Dict								=		{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)



