



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

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""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      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

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

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

						a					     =   grid_points.shape[1]
						a					     =   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_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

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

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						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"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

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

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

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





import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_lowercase: Optional[int]      			=		re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_lowercase: Dict      			=		10
_lowercase: Optional[Any]      			=		256






def     a(      A       :    List[str]       )		->       Optional[MinHash]:
					"""simple docstring"""





					if len(A       ) < MIN_NUM_TOKENS:
										return None
					a					     =   MinHash(num_perm=A       )
					for token in set(A       ):
										min_hash.update(token.encode()       )
					return min_hash






def     a(      A       :    str       )		->       Set[str]:
					"""simple docstring"""





					return {t for t in NON_ALPHA.split(A       ) if len(t.strip()       ) > 0}



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       *,
	    lowerCamelCase_ = 0.85		,       ):
						"""simple docstring"""

						a					     =   duplication_jaccard_threshold
						a					     =   NUM_PERM
						a					     =   MinHashLSH(threshold=self._duplication_jaccard_threshold		,       num_perm=self._num_perm		)

						a					     =   defaultdict(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self._index.query(lowerCamelCase_		)
						if code_key in self._index.keys:
											print(F'''Duplicate key {code_key}'''		)
											return

						self._index.insert(lowerCamelCase_		,       lowerCamelCase_		)
						if len(lowerCamelCase_		) > 0:
											for base_duplicate in close_duplicates:
																if base_duplicate in self._duplicate_clusters:
																					self._duplicate_clusters[base_duplicate].add(lowerCamelCase_		)
																					break
											else:
																self._duplicate_clusters[close_duplicates[0]].add(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   []
						for base, duplicates in self._duplicate_clusters.items():
											a					     =   [base] + list(lowerCamelCase_		)
											# reformat the cluster to be a list of dict
											a					     =   [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
											duplicate_clusters.append(lowerCamelCase_		)
						return duplicate_clusters




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.get_duplicate_clusters()
						with open(lowerCamelCase_		,       "w"		) as f:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)






def     a(      A       :    Any       )		->       List[Any]:
					"""simple docstring"""





					a     ,				a					     =   element
					a					     =   get_min_hash([t for t in NON_ALPHA.split(data["content"]       ) if len(t.strip()       ) > 0]       )
					if min_hash is not None:
										return (index, data["repo_name"], data["path"]), min_hash






def     a(      A       :    Type[Dataset]       )		->       List[Any]:
					"""simple docstring"""





					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash ,     ThreadedIterator(A ,     max_queue_size=1_0000       ) ,     chunksize=100 ,     ):
															if data is not None:
																				yield data






def     a(      A       :    Type[Dataset] ,     A       :    float       )		->       Dict:
					"""simple docstring"""





					a					     =   DuplicationIndex(duplication_jaccard_threshold=A       )

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A       )       ) ,     max_queue_size=100       )       ):
										di.add(A ,     A       )

					# Returns a List[Cluster] where Cluster is List[str] with the filenames.
					return di.get_duplicate_clusters()






def     a(      A       :    str ,     A       :    str       )		->       float:
					"""simple docstring"""





					a					     =   get_tokens(A       )
					a					     =   get_tokens(A       )
					return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_lowercase: int      			=		None






def     a(      A       :    str ,     A       :    Tuple       )		->       int:
					"""simple docstring"""





					a					     =   []
					for elementa in cluster:
										a					     =   _shared_dataset[elementa["base_index"]]["content"]
										for elementa in extremes:
															a					     =   _shared_dataset[elementa["base_index"]]["content"]
															if jaccard_similarity(A ,     A       ) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															a					     =   1
															extremes.append(A       )
					return extremes






def     a(      A       :    str ,     A       :    List[str] ,     A       :    int       )		->       Tuple:
					"""simple docstring"""





					global _shared_dataset
					a					     =   dataset
					a					     =   []
					a					     =   partial(_find_cluster_extremes_shared ,     jaccard_threshold=A       )
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        A ,     A ,     ) ,     total=len(A       ) ,     ):
															extremes_list.append(A       )
					return extremes_list







def     a(      A       :    Type[Dataset] ,     A       :    float = 0.85       )		->       Tuple[Type[Dataset], List[List[Dict]]]:
					"""simple docstring"""





					a					     =   make_duplicate_clusters(A ,     A       )
					a					     =   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
					a					     =   {}
					a					     =   find_extremes(A ,     A ,     A       )
					for extremes in extremes_clusters:
										for element in extremes:
															a					     =   element
					a					     =   duplicate_indices - set(extreme_dict.keys()       )
					a					     =   dataset.filter(lambda A ,     A       : idx not in remove_indices ,     with_indices=A       )

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															a					     =   element["base_index"] in extreme_dict
															if element["is_extreme"]:
																				a					     =   extreme_dict[element["base_index"]]["copies"]

					print(f'''Original dataset size: {len(A       )}'''       )
					print(f'''Number of duplicate clusters: {len(A       )}'''       )
					print(f'''Files in duplicate cluster: {len(A       )}'''       )
					print(f'''Unique files in duplicate cluster: {len(A       )}'''       )
					print(f'''Filtered dataset size: {len(A       )}'''       )

					return ds_filter, duplicate_clusters
