




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(__magic_name__ )
class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			def __init__(      self			,						**lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							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       lowercase_    (      self			,						**lowerCamelCase__							)    ->      List[Any]:



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


			def __call__(      self			,						lowerCamelCase__			,						*lowerCamelCase__			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return super().__call__(lowerCamelCase__			,						*lowerCamelCase__			,						num_workers=lowerCamelCase__			,						batch_size=lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=64			,						lowerCamelCase__ = 0			,						lowerCamelCase__ = 512 / 1_500			,						lowerCamelCase__ = 32			,						lowerCamelCase__ = 1			,						)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    load_image(lowerCamelCase__							)
							__lowerCamelCase		       =    self.image_processor.size['longest_edge']
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processor.generate_crop_boxes(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    self.image_processor(images=lowerCamelCase__			,						return_tensors='pt'							)

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

							__lowerCamelCase		       =    grid_points.shape[1]
							__lowerCamelCase		       =    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__							):
											__lowerCamelCase		       =    grid_points[:, i : i + points_per_batch, :, :]
											__lowerCamelCase		       =    input_labels[:, i : i + points_per_batch]
											__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=0.88			,						lowerCamelCase__=0.95			,						lowerCamelCase__=0			,						lowerCamelCase__=1			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    model_inputs.pop('input_boxes'							)
							__lowerCamelCase		       =    model_inputs.pop('is_last'							)
							__lowerCamelCase		       =    model_inputs.pop('original_sizes'							).tolist()
							__lowerCamelCase		       =    model_inputs.pop('reshaped_input_sizes'							).tolist()

							__lowerCamelCase		       =    self.model(**lowerCamelCase__							)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							__lowerCamelCase		       =    model_outputs['pred_masks']
							__lowerCamelCase		       =    self.image_processor.post_process_masks(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						binarize=lowerCamelCase__							)
							__lowerCamelCase		       =    model_outputs['iou_scores']
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=False			,						lowerCamelCase__=False			,						lowerCamelCase__=0.7			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							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		       =    torch.cat(lowerCamelCase__							)
							__lowerCamelCase		       =    torch.cat(lowerCamelCase__							)
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processor.post_process_for_mask_generation(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)

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

							__lowerCamelCase		       =    {}
							if output_rle_mask:
											__lowerCamelCase		       =    rle_mask

							if output_bboxes_mask:
											__lowerCamelCase		       =    bounding_boxes

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








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


