"""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(__lowerCamelCase		)
class  _snake_case     (					__lowerCamelCase		):






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






						def __call__(       self      :			List[Any]							,					UpperCAmelCase      :			Any							,					*UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			str=None							,					UpperCAmelCase      :			List[Any]=None							,					**UpperCAmelCase      :			Tuple    ):
													return super().__call__(a_							,					*a_							,					num_workers=a_							,					batch_size=a_							,					**a_    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Union[str, Any]=64							,					UpperCAmelCase      :			Optional[Any] = 0							,					UpperCAmelCase      :			Tuple = 512 / 1500							,					UpperCAmelCase      :			int = 32							,					UpperCAmelCase      :			Optional[Any] = 1							,					):
													__lowerCamelCase					:		Tuple		   =     load_image(a_    )
													__lowerCamelCase					:		List[str]		   =     self.image_processor.size["longest_edge"]
													__lowerCamelCase					:		List[str]		   =     self.image_processor.generate_crop_boxes(
													    a_							,					a_							,					a_							,					a_							,					a_							,					a_    )
													__lowerCamelCase					:		Tuple		   =     self.image_processor(images=a_							,					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(a_							,					device=self.device    )
																																		__lowerCamelCase					:		Tuple		   =     self.model.get_image_embeddings(model_inputs.pop("pixel_values"    )    )
																																		__lowerCamelCase					:		int		   =     image_embeddings

													__lowerCamelCase					:		Union[str, Any]		   =     grid_points.shape[1]
													__lowerCamelCase					:		Any		   =     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_    ):
																				__lowerCamelCase					:		List[str]		   =     grid_points[:, i : i + points_per_batch, :, :]
																				__lowerCamelCase					:		Any		   =     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      :			List[str]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str=0.8_8							,					UpperCAmelCase      :			List[str]=0.9_5							,					UpperCAmelCase      :			Dict=0							,					UpperCAmelCase      :			Optional[Any]=1							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     model_inputs.pop("input_boxes"    )
													__lowerCamelCase					:		Optional[Any]		   =     model_inputs.pop("is_last"    )
													__lowerCamelCase					:		List[str]		   =     model_inputs.pop("original_sizes"    ).tolist()
													__lowerCamelCase					:		str		   =     model_inputs.pop("reshaped_input_sizes"    ).tolist()

													__lowerCamelCase					:		int		   =     self.model(**a_    )

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

													__lowerCamelCase					:		Any		   =     defaultdict(a_    )
													for output in model_outputs:
																				for k, v in output.items():
																											extra[k].append(a_    )

													__lowerCamelCase					:		Tuple		   =     {}
													if output_rle_mask:
																				__lowerCamelCase					:		Dict		   =     rle_mask

													if output_bboxes_mask:
																				__lowerCamelCase					:		Tuple		   =     bounding_boxes

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





from typing import TYPE_CHECKING

from ...utils import _LazyModule


__A  =				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
				from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)