


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



@add_end_docstrings(_lowercase							)
class 							A   (	_lowercase							):
					'''simple docstring'''






					def __init__(self   :						List[str]				, **_UpperCAmelCase   :						List[Any]		)						->       List[str]:




							"""simple docstring"""


							super().__init__(**__UpperCamelCase		)
							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(__UpperCamelCase		)

					def   lowerCamelCase__					(self   :						Tuple				, **_UpperCAmelCase   :						int		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		{}
							lowercase__					      =		{}
							lowercase__					      =		{}
							# preprocess args
							if "points_per_batch" in kwargs:
									lowercase__					      =		kwargs["""points_per_batch"""]
							if "points_per_crop" in kwargs:
									lowercase__					      =		kwargs["""points_per_crop"""]
							if "crops_n_layers" in kwargs:
									lowercase__					      =		kwargs["""crops_n_layers"""]
							if "crop_overlap_ratio" in kwargs:
									lowercase__					      =		kwargs["""crop_overlap_ratio"""]
							if "crop_n_points_downscale_factor" in kwargs:
									lowercase__					      =		kwargs["""crop_n_points_downscale_factor"""]
							# postprocess args
							if "pred_iou_thresh" in kwargs:
									lowercase__					      =		kwargs["""pred_iou_thresh"""]
							if "stability_score_offset" in kwargs:
									lowercase__					      =		kwargs["""stability_score_offset"""]
							if "mask_threshold" in kwargs:
									lowercase__					      =		kwargs["""mask_threshold"""]
							if "stability_score_thresh" in kwargs:
									lowercase__					      =		kwargs["""stability_score_thresh"""]
							if "crops_nms_thresh" in kwargs:
									lowercase__					      =		kwargs["""crops_nms_thresh"""]
							if "output_rle_mask" in kwargs:
									lowercase__					      =		kwargs["""output_rle_mask"""]
							if "output_bboxes_mask" in kwargs:
									lowercase__					      =		kwargs["""output_bboxes_mask"""]
							return preprocess_kwargs, forward_params, postprocess_kwargs

					def __call__(self   :						List[Any]				, _UpperCAmelCase   :						List[str]				, *_UpperCAmelCase   :						int				, _UpperCAmelCase   :						Optional[int]=None				, _UpperCAmelCase   :						Optional[Any]=None				, **_UpperCAmelCase   :						Optional[Any]		)						->       Optional[int]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						List[Any]=64				, _UpperCAmelCase   :						int = 0				, _UpperCAmelCase   :						float = 512 / 1500				, _UpperCAmelCase   :						Optional[int] = 32				, _UpperCAmelCase   :						Optional[int] = 1				, )						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		load_image(__UpperCamelCase		)
							lowercase__					      =		self.image_processor.size["""longest_edge"""]
							lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__					      =		self.image_processor.generate_crop_boxes(
							    __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase		)
							lowercase__					      =		self.image_processor(images=__UpperCamelCase				, return_tensors="""pt"""		)

							with self.device_placement():
									if self.framework == "pt":
											lowercase__					      =		self.get_inference_context()
											with inference_context():
													lowercase__					      =		self._ensure_tensor_on_device(__UpperCamelCase				, device=self.device		)
													lowercase__					      =		self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
													lowercase__					      =		image_embeddings

							lowercase__					      =		grid_points.shape[1]
							lowercase__					      =		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				, __UpperCamelCase				, __UpperCamelCase		):
									lowercase__					      =		grid_points[:, i : i + points_per_batch, :, :]
									lowercase__					      =		input_labels[:, i : i + points_per_batch]
									lowercase__					      =		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   :						Any				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Union[str, Any]=0.88				, _UpperCAmelCase   :						Dict=0.95				, _UpperCAmelCase   :						Any=0				, _UpperCAmelCase   :						Any=1				, )						->       int:




							"""simple docstring"""


							lowercase__					      =		model_inputs.pop("""input_boxes"""		)
							lowercase__					      =		model_inputs.pop("""is_last"""		)
							lowercase__					      =		model_inputs.pop("""original_sizes"""		).tolist()
							lowercase__					      =		model_inputs.pop("""reshaped_input_sizes"""		).tolist()

							lowercase__					      =		self.model(**__UpperCamelCase		)

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


					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						str=False				, _UpperCAmelCase   :						List[Any]=False				, _UpperCAmelCase   :						Optional[Any]=0.7				, )						->       Any:




							"""simple docstring"""


							lowercase__					      =		[]
							lowercase__					      =		[]
							lowercase__					      =		[]
							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__					      =		torch.cat(__UpperCamelCase		)
							lowercase__					      =		torch.cat(__UpperCamelCase		)
							lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__					      =		self.image_processor.post_process_for_mask_generation(
							    __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase		)

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

							lowercase__					      =		{}
							if output_rle_mask:
									lowercase__					      =		rle_mask

							if output_bboxes_mask:
									lowercase__					      =		bounding_boxes

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









import pytest


A	:		Optional[Any]    =       '__dummy_dataset1__'

A	:		Tuple    =       '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'






@pytest.fixture
def 			UpperCamelCase (     )       ->  Any:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 			UpperCamelCase (     )       ->  Union[str, Any]:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Any					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		dataset_loading_script_name
		lowercase__					      =		tmp_path / """datasets""" / script_name
		script_dir.mkdir(parents=__magic_name__					)
		lowercase__					      =		script_dir / f'''{script_name}.py'''
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)

