



"""simple docstring"""

import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		int 					=					fname.split(os.path.sep			)[-1]
					return re.search(R"""^(.*)_\d+\.jpg$"""				,    SCREAMING_SNAKE_CASE__			).groups()[0]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=None   ,		lowercase__=None       ):
											snake_case_					:		Union[str, Any] 					=					file_names
											snake_case_					:		Tuple 					=					image_transform
											snake_case_					:		Union[str, Any] 					=					label_to_id







						def __len__(self       ):
											return len(self.file_names       )







						def __getitem__(self   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					self.file_names[idx]
											snake_case_					:		Dict 					=					PIL.Image.open(lowercase__       )
											snake_case_					:		Union[str, Any] 					=					raw_image.convert("""RGB"""       )
											if self.image_transform is not None:
																snake_case_					:		Union[str, Any] 					=					self.image_transform(lowercase__       )
											snake_case_					:		Dict 					=					extract_label(lowercase__       )
											if self.label_to_id is not None:
																snake_case_					:		Union[str, Any] 					=					self.label_to_id[label]
											return {"image": image, "label": label}







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					if args.with_tracking:
										snake_case_					:		Union[str, Any] 					=					Accelerator(
										    cpu=args.cpu				,    mixed_precision=args.mixed_precision				,    log_with="""all"""				,    project_dir=args.project_dir			)
					else:
										snake_case_					:		List[Any] 					=					Accelerator(cpu=args.cpu				,    mixed_precision=args.mixed_precision			)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_					:		Tuple 					=					config["""lr"""]
					snake_case_					:		Any 					=					int(config["""num_epochs"""]			)
					snake_case_					:		str 					=					int(config["""seed"""]			)
					snake_case_					:		Any 					=					int(config["""batch_size"""]			)
					snake_case_					:		Any 					=					config["""image_size"""]
					if not isinstance(SCREAMING_SNAKE_CASE__				,    (list, tuple)			):
										snake_case_					:		Dict 					=					(image_size, image_size)

					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps				,    """isdigit"""			):
										if args.checkpointing_steps == "epoch":
															snake_case_					:		List[str] 					=					args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															snake_case_					:		Any 					=					int(args.checkpointing_steps			)
										else:
															raise ValueError(
															    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
					else:
										snake_case_					:		Dict 					=					None

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										snake_case_					:		Any 					=					os.path.split(SCREAMING_SNAKE_CASE__			)[-1].split("""."""			)[0]
										accelerator.init_trackers(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					# Grab all the image filenames
					snake_case_					:		Any 					=					[os.path.join(args.data_dir				,    SCREAMING_SNAKE_CASE__			) for fname in os.listdir(args.data_dir			) if fname.endswith(""".jpg"""			)]

					# Build the label correspondences
					snake_case_					:		int 					=					[extract_label(SCREAMING_SNAKE_CASE__			) for fname in file_names]
					snake_case_					:		List[str] 					=					list(set(SCREAMING_SNAKE_CASE__			)			)
					id_to_label.sort()
					snake_case_					:		str 					=					{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__			)}

					# Set the seed before splitting the data.
					np.random.seed(SCREAMING_SNAKE_CASE__			)
					torch.manual_seed(SCREAMING_SNAKE_CASE__			)
					torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__			)

					# Split our filenames between train and validation
					snake_case_					:		Union[str, Any] 					=					np.random.permutation(len(SCREAMING_SNAKE_CASE__			)			)
					snake_case_					:		Dict 					=					int(0.8 * len(SCREAMING_SNAKE_CASE__			)			)
					snake_case_					:		Union[str, Any] 					=					random_perm[:cut]
					snake_case_					:		Optional[int] 					=					random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					snake_case_					:		Any 					=					Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__				,    scale=(0.5, 1.0)			), ToTensor()]			)
					snake_case_					:		Tuple 					=					PetsDataset(
					    [file_names[i] for i in train_split]				,    image_transform=SCREAMING_SNAKE_CASE__				,    label_to_id=SCREAMING_SNAKE_CASE__			)

					# For evaluation, we use a deterministic Resize
					snake_case_					:		Tuple 					=					Compose([Resize(SCREAMING_SNAKE_CASE__			), ToTensor()]			)
					snake_case_					:		str 					=					PetsDataset([file_names[i] for i in eval_split]				,    image_transform=SCREAMING_SNAKE_CASE__				,    label_to_id=SCREAMING_SNAKE_CASE__			)

					# Instantiate dataloaders.
					snake_case_					:		int 					=					DataLoader(SCREAMING_SNAKE_CASE__				,    shuffle=SCREAMING_SNAKE_CASE__				,    batch_size=SCREAMING_SNAKE_CASE__				,    num_workers=4			)
					snake_case_					:		Optional[int] 					=					DataLoader(SCREAMING_SNAKE_CASE__				,    shuffle=SCREAMING_SNAKE_CASE__				,    batch_size=SCREAMING_SNAKE_CASE__				,    num_workers=4			)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_					:		Tuple 					=					create_model("""resnet50d"""				,    pretrained=SCREAMING_SNAKE_CASE__				,    num_classes=len(SCREAMING_SNAKE_CASE__			)			)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					snake_case_					:		Tuple 					=					model.to(accelerator.device			)

					# Freezing the base model
					for param in model.parameters():
										snake_case_					:		Any 					=					False
					for param in model.get_classifier().parameters():
										snake_case_					:		List[str] 					=					True

					# We normalize the batches of images to be a bit faster.
					snake_case_					:		Dict 					=					torch.tensor(model.default_cfg["""mean"""]			)[None, :, None, None].to(accelerator.device			)
					snake_case_					:		Any 					=					torch.tensor(model.default_cfg["""std"""]			)[None, :, None, None].to(accelerator.device			)

					# Instantiate optimizer
					snake_case_					:		str 					=					torch.optim.Adam(params=model.parameters()				,    lr=lr / 2_5			)

					# Instantiate learning rate scheduler
					snake_case_					:		Dict 					=					OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__				,    max_lr=SCREAMING_SNAKE_CASE__				,    epochs=SCREAMING_SNAKE_CASE__				,    steps_per_epoch=len(SCREAMING_SNAKE_CASE__			)			)

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Any 					=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					# We need to keep track of how many total steps we have iterated over
					snake_case_					:		Optional[Any] 					=					0
					# We also need to keep track of the starting epoch so files are named properly
					snake_case_					:		List[str] 					=					0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'			)
															accelerator.load_state(args.resume_from_checkpoint			)
															snake_case_					:		Tuple 					=					os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															snake_case_					:		List[Any] 					=					[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															snake_case_					:		int 					=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_					:		Union[str, Any] 					=					os.path.splitext(SCREAMING_SNAKE_CASE__			)[0]

										if "epoch" in training_difference:
															snake_case_					:		Tuple 					=					int(training_difference.replace("""epoch_"""				,    """"""			)			) + 1
															snake_case_					:		str 					=					None
										else:
															snake_case_					:		Tuple 					=					int(training_difference.replace("""step_"""				,    """"""			)			)
															snake_case_					:		Optional[Any] 					=					resume_step // len(SCREAMING_SNAKE_CASE__			)
															resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__			)

    # Now we train the model
					for epoch in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										model.train()
										if args.with_tracking:
															snake_case_					:		Dict 					=					0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															snake_case_					:		Tuple 					=					accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															snake_case_					:		Dict 					=					train_dataloader
										for batch in active_dataloader:
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															snake_case_					:		Any 					=					{k: v.to(accelerator.device			) for k, v in batch.items()}
															snake_case_					:		List[str] 					=					(batch["""image"""] - mean) / std
															snake_case_					:		int 					=					model(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		List[str] 					=					torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__				,    batch["""label"""]			)
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(SCREAMING_SNAKE_CASE__			)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															overall_step += 1
															if isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
																				snake_case_					:		Dict 					=					f'step_{overall_step}'
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_					:		Dict 					=					os.path.join(args.output_dir				,    SCREAMING_SNAKE_CASE__			)
																									accelerator.save_state(SCREAMING_SNAKE_CASE__			)
										model.eval()
										snake_case_					:		List[str] 					=					0
										snake_case_					:		Any 					=					0
										for step, batch in enumerate(SCREAMING_SNAKE_CASE__			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															snake_case_					:		List[str] 					=					{k: v.to(accelerator.device			) for k, v in batch.items()}
															snake_case_					:		Optional[Any] 					=					(batch["""image"""] - mean) / std
															with torch.no_grad():
																				snake_case_					:		int 					=					model(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		List[Any] 					=					outputs.argmax(dim=-1			)
															snake_case_					,			snake_case_					:		Optional[int] 					=					accelerator.gather_for_metrics((predictions, batch["""label"""])			)
															snake_case_					:		Dict 					=					predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										snake_case_					:		Tuple 					=					accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}: {1_0_0 * eval_metric:.2f}'			)
										if args.with_tracking:
															accelerator.log(
															    {
															        """accuracy""": 1_0_0 * eval_metric,
															        """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__			),
															        """epoch""": epoch,
															    }				,    step=SCREAMING_SNAKE_CASE__				,    )
										if checkpointing_steps == "epoch":
															snake_case_					:		Dict 					=					f'epoch_{epoch}'
															if args.output_dir is not None:
																				snake_case_					:		Dict 					=					os.path.join(args.output_dir				,    SCREAMING_SNAKE_CASE__			)
															accelerator.save_state(SCREAMING_SNAKE_CASE__			)

					if args.with_tracking:
										accelerator.end_training()







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					argparse.ArgumentParser(description="""Simple example of training script."""			)
					parser.add_argument("""--data_dir"""				,    required=SCREAMING_SNAKE_CASE__				,    help="""The data folder on disk."""			)
					parser.add_argument("""--fp16"""				,    action="""store_true"""				,    help="""If passed, will use FP16 training."""			)
					parser.add_argument(
					    """--mixed_precision"""				,    type=SCREAMING_SNAKE_CASE__				,    default=SCREAMING_SNAKE_CASE__				,    choices=["""no""", """fp16""", """bf16""", """fp8"""]				,    help="""Whether to use mixed precision. Choose"""
					    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
					    """and an Nvidia Ampere GPU."""				,    )
					parser.add_argument("""--cpu"""				,    action="""store_true"""				,    help="""If passed, will train on the CPU."""			)
					parser.add_argument(
					    """--checkpointing_steps"""				,    type=SCREAMING_SNAKE_CASE__				,    default=SCREAMING_SNAKE_CASE__				,    help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""				,    )
					parser.add_argument(
					    """--output_dir"""				,    type=SCREAMING_SNAKE_CASE__				,    default="""."""				,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,    )
					parser.add_argument(
					    """--resume_from_checkpoint"""				,    type=SCREAMING_SNAKE_CASE__				,    default=SCREAMING_SNAKE_CASE__				,    help="""If the training should continue from a checkpoint folder."""				,    )
					parser.add_argument(
					    """--with_tracking"""				,    action="""store_true"""				,    help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,    )
					parser.add_argument(
					    """--project_dir"""				,    type=SCREAMING_SNAKE_CASE__				,    default="""logs"""				,    help="""Location on where to store experiment tracking logs` and relevent project information"""				,    )
					snake_case_					:		Tuple 					=					parser.parse_args()
					snake_case_					:		Dict 					=					{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
					training_function(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
			main()









"""simple docstring"""

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


a_			   =					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
a_			   =					10
a_			   =					256







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					if len(SCREAMING_SNAKE_CASE__			) < MIN_NUM_TOKENS:
										return None
					snake_case_					:		Union[str, Any] 					=					MinHash(num_perm=SCREAMING_SNAKE_CASE__			)
					for token in set(SCREAMING_SNAKE_CASE__			):
										min_hash.update(token.encode()			)
					return min_hash







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





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







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		*,
						    lowercase__ = 0.85   ,		):
											snake_case_					:		Tuple 					=					duplication_jaccard_threshold
											snake_case_					:		Optional[Any] 					=					NUM_PERM
											snake_case_					:		Tuple 					=					MinHashLSH(threshold=self._duplication_jaccard_threshold   ,		num_perm=self._num_perm       )

											snake_case_					:		List[Any] 					=					defaultdict(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		int 					=					self._index.query(lowercase__       )
											if code_key in self._index.keys:
																print(f'Duplicate key {code_key}'       )
																return

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







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







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					self.get_duplicate_clusters()
											with open(lowercase__   ,		"""w"""       ) as f:
																json.dump(lowercase__   ,		lowercase__       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					,			snake_case_					:		str 					=					element
					snake_case_					:		Tuple 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]			):



					"""simple docstring"""





					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(SCREAMING_SNAKE_CASE__				,    max_queue_size=1_0_0_0_0			)				,    chunksize=1_0_0				,    ):
															if data is not None:
																				yield data







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float			):



					"""simple docstring"""





					snake_case_					:		int 					=					DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__			)

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE__			)			)				,    max_queue_size=1_0_0			)			):
										di.add(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		int 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					return len(tokensa & tokensa			) / len(tokensa | tokensa			)


a_			   =					None







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[]
					for elementa in cluster:
										snake_case_					:		Union[str, Any] 					=					_shared_dataset[elementa["""base_index"""]]["""content"""]
										for elementa in extremes:
															snake_case_					:		Any 					=					_shared_dataset[elementa["""base_index"""]]["""content"""]
															if jaccard_similarity(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															snake_case_					:		Union[str, Any] 					=					1
															extremes.append(SCREAMING_SNAKE_CASE__			)
					return extremes







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float = 0.85			):



					"""simple docstring"""





					snake_case_					:		List[str] 					=					make_duplicate_clusters(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		str 					=					{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
					snake_case_					:		str 					=					{}
					snake_case_					:		Dict 					=					find_extremes(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for extremes in extremes_clusters:
										for element in extremes:
															snake_case_					:		int 					=					element
					snake_case_					:		Optional[int] 					=					duplicate_indices - set(extreme_dict.keys()			)
					snake_case_					:		List[Any] 					=					dataset.filter(lambda SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			: idx not in remove_indices				,    with_indices=SCREAMING_SNAKE_CASE__			)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															snake_case_					:		List[Any] 					=					element["""base_index"""] in extreme_dict
															if element["is_extreme"]:
																				snake_case_					:		str 					=					extreme_dict[element["""base_index"""]]["""copies"""]

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

					return ds_filter, duplicate_clusters




