



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

__snake_case    :		List[str]  =logging.get_logger(__name__)



@add_end_docstrings(__SCREAMING_SNAKE_CASE)
class 	lowerCamelCase__      (							__SCREAMING_SNAKE_CASE):




		'''simple docstring'''



		def __init__(self		,**__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						super().__init__(**_SCREAMING_SNAKE_CASE			)
						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(_SCREAMING_SNAKE_CASE			)



		def 		lowerCAmelCase__				(self		,**__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					{}
						lowerCAmelCase__					:    List[str]												=					{}
						lowerCAmelCase__					:    str												=					{}
						# preprocess args
						if "points_per_batch" in kwargs:
										lowerCAmelCase__					:    int												=					kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
										lowerCAmelCase__					:    List[str]												=					kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
										lowerCAmelCase__					:    Tuple												=					kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
										lowerCAmelCase__					:    Tuple												=					kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
										lowerCAmelCase__					:    List[Any]												=					kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
										lowerCAmelCase__					:    str												=					kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
										lowerCAmelCase__					:    List[Any]												=					kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
										lowerCAmelCase__					:    Dict												=					kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
										lowerCAmelCase__					:    int												=					kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
										lowerCAmelCase__					:    List[str]												=					kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
										lowerCAmelCase__					:    Any												=					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		,__lowerCamelCase		,*__lowerCamelCase		,__lowerCamelCase=None		,__lowerCamelCase=None		,**__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=64		,__lowerCamelCase = 0		,__lowerCamelCase = 5_12 / 15_00		,__lowerCamelCase = 32		,__lowerCamelCase = 1		,)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					load_image(_SCREAMING_SNAKE_CASE			)
						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processor.size["longest_edge"]
						lowerCAmelCase__					:    List[str]												=					self.image_processor.generate_crop_boxes(
						    _SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE			)
						lowerCAmelCase__					:    List[str]												=					self.image_processor(images=_SCREAMING_SNAKE_CASE		,return_tensors='''pt'''			)

						with self.device_placement():
										if self.framework == "pt":
														lowerCAmelCase__					:    Any												=					self.get_inference_context()
														with inference_context():
																		lowerCAmelCase__					:    List[Any]												=					self._ensure_tensor_on_device(_SCREAMING_SNAKE_CASE		,device=self.device			)
																		lowerCAmelCase__					:    Any												=					self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''			)			)
																		lowerCAmelCase__					:    Optional[int]												=					image_embeddings

						lowerCAmelCase__					:    str												=					grid_points.shape[1]
						lowerCAmelCase__					:    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		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE			):
										lowerCAmelCase__					:    List[Any]												=					grid_points[:, i : i + points_per_batch, :, :]
										lowerCAmelCase__					:    List[str]												=					input_labels[:, i : i + points_per_batch]
										lowerCAmelCase__					:    Union[str, 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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0.88		,__lowerCamelCase=0.95		,__lowerCamelCase=0		,__lowerCamelCase=1		,)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					model_inputs.pop('''input_boxes'''			)
						lowerCAmelCase__					:    str												=					model_inputs.pop('''is_last'''			)
						lowerCAmelCase__					:    Optional[Any]												=					model_inputs.pop('''original_sizes'''			).tolist()
						lowerCAmelCase__					:    Optional[int]												=					model_inputs.pop('''reshaped_input_sizes'''			).tolist()

						lowerCAmelCase__					:    List[str]												=					self.model(**_SCREAMING_SNAKE_CASE			)

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=False		,__lowerCamelCase=False		,__lowerCamelCase=0.7		,)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					[]
						lowerCAmelCase__					:    int												=					[]
						lowerCAmelCase__					:    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'''			)			)

						lowerCAmelCase__					:    Union[str, Any]												=					torch.cat(_SCREAMING_SNAKE_CASE			)
						lowerCAmelCase__					:    Tuple												=					torch.cat(_SCREAMING_SNAKE_CASE			)
						lowerCAmelCase__					:    Optional[int]												=					self.image_processor.post_process_for_mask_generation(
						    _SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE			)

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

						lowerCAmelCase__					:    Optional[Any]												=					{}
						if output_rle_mask:
										lowerCAmelCase__					:    Optional[Any]												=					rle_mask

						if output_bboxes_mask:
										lowerCAmelCase__					:    int												=					bounding_boxes

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






__snake_case    :		Any  ='\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

__snake_case    :		Tuple  =[{'type': 'code', 'content': INSTALL_CONTENT}]
__snake_case    :		Tuple  ={
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

