





from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       Optional[Any]:
         return [
             {"col_1": 3, "col_2": "a"},
             {"col_1": 2, "col_2": "b"},
             {"col_1": 1, "col_2": "c"},
             {"col_1": 0, "col_2": "d"},
         ]







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       Tuple:
         _lowercase						    =			{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
         return Dataset.from_dict(__A	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Any:
         _lowercase						    =			self._create_example_records()
         _lowercase						    =			Dataset.from_list(__A	)
         self.assertListEqual(dset.column_names    ,['col_1', 'col_2']	)
         for i, r in enumerate(__A	):
           self.assertDictEqual(__A    ,example_records[i]	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       List[str]:
         _lowercase						    =			self._create_example_records()
         _lowercase						    =			Dataset.from_list(__A	)
         _lowercase						    =			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}	)
         self.assertEqual(dset.info    ,dset_from_dict.info	)







       def  __UpperCAmelCase			(     self						:						int	)    ->       Tuple:  # checks what happens with missing columns
         _lowercase						    =			[{'col_1': 1}, {'col_2': 'x'}]
         _lowercase						    =			Dataset.from_list(__A	)
         self.assertDictEqual(dset[0]    ,{'col_1': 1}	)
         self.assertDictEqual(dset[1]    ,{'col_1': None}	)  # NB: first record is used for columns







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       List[Any]:  # checks if the type can be inferred from the second record
         _lowercase						    =			[{'col_1': []}, {'col_1': [1, 2]}]
         _lowercase						    =			Dataset.from_list(__A	)
         self.assertEqual(dset.info.features['col_1']    ,Sequence(Value('int64'	)	)	)







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       str:
         _lowercase						    =			Dataset.from_list([]	)
         self.assertEqual(len(__A	)    ,0	)
         self.assertListEqual(dset.column_names    ,[]	)

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[Any]     	=    logging.get_logger(__name__)





@add_end_docstrings(__a		)
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int = 0		,    lowerCAmelCase__							:float = 512 / 1500		,    lowerCAmelCase__							:Optional[int] = 32		,    lowerCAmelCase__							:Optional[int] = 1		,    )  ->Any:
										lowercase         =      load_image(lowerCAmelCase__     )
										lowercase         =      self.image_processor.size["longest_edge"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.generate_crop_boxes(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      self.image_processor(images=lowerCAmelCase__		,    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(lowerCAmelCase__		,    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		,    lowerCAmelCase__		,    lowerCAmelCase__     ):
														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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict=0.88		,    lowerCAmelCase__							:Dict=0.95		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:int=1		,    )  ->str:
										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(**lowerCAmelCase__     )

										# 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(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    binarize=lowerCAmelCase__     )
										lowercase         =      model_outputs["iou_scores"]
										lowercase     ,	lowercase     ,	lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:int=0.7		,    )  ->List[Any]:
										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(lowerCAmelCase__     )
										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.post_process_for_mask_generation(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

										lowercase         =      {}
										if output_rle_mask:
														lowercase         =      rle_mask

										if output_bboxes_mask:
														lowercase         =      bounding_boxes

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