



"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a_			   =					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

a_			   =					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
a_			   =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class    __lowercase     :







						"""simple docstring"""







						_A		:							Optional[str]							       =		field(
						    default="""cifar10"""					,    metadata={"""help""": """Name of a dataset from the datasets package"""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}					,    )
						_A		:							Optional[str]							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """A folder containing the training data."""})
						_A		:							Optional[str]							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """A folder containing the validation data."""})
						_A		:							Optional[float]							       =		field(
						    default=0.15					,    metadata={"""help""": """Percent to split off of train for validation."""})
						_A		:							int							       =		field(default=32					,    metadata={"""help""": """The size of the square patches to use for masking."""})
						_A		:							float							       =		field(
						    default=0.6					,    metadata={"""help""": """Percentage of patches to mask."""}					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of training examples to this """
						            """value if set."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
						            """value if set."""
						        )
						    }					,    )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					{}
											if self.train_dir is not None:
																snake_case_					:		Any 					=					self.train_dir
											if self.validation_dir is not None:
																snake_case_					:		Tuple 					=					self.validation_dir
											snake_case_					:		int 					=					data_files if data_files else None







@dataclass
class    __lowercase     :







						"""simple docstring"""







						_A		:							str							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
						            """checkpoint identifier on the hub. """
						            """Don't set if you want to train a model from scratch."""
						        )
						    }					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCAmelCase)}					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """Override some existing default config settings when a model is trained from scratch. Example: """
						            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
						        )
						    }					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}					,    )
						_A		:							str							       =		field(
						    default="""main"""					,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}					,    )
						_A		:							str							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """Name or path of preprocessor config."""})
						_A		:							bool							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
						            """with private models)."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Stride to use for the encoder."""}					,    )







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__=1_92   ,		lowercase__=32   ,		lowercase__=4   ,		lowercase__=0.6       ):
											snake_case_					:		Optional[Any] 					=					input_size
											snake_case_					:		List[Any] 					=					mask_patch_size
											snake_case_					:		Tuple 					=					model_patch_size
											snake_case_					:		Dict 					=					mask_ratio

											if self.input_size % self.mask_patch_size != 0:
																raise ValueError("""Input size must be divisible by mask patch size"""       )
											if self.mask_patch_size % self.model_patch_size != 0:
																raise ValueError("""Mask patch size must be divisible by model patch size"""       )

											snake_case_					:		List[Any] 					=					self.input_size // self.mask_patch_size
											snake_case_					:		List[str] 					=					self.mask_patch_size // self.model_patch_size

											snake_case_					:		Optional[int] 					=					self.rand_size**2
											snake_case_					:		Union[str, Any] 					=					int(np.ceil(self.token_count * self.mask_ratio       )       )







						def __call__(self       ):
											snake_case_					:		Optional[int] 					=					np.random.permutation(self.token_count       )[: self.mask_count]
											snake_case_					:		Optional[Any] 					=					np.zeros(self.token_count   ,		dtype=lowercase__       )
											snake_case_					:		Optional[Any] 					=					1

											snake_case_					:		Any 					=					mask.reshape((self.rand_size, self.rand_size)       )
											snake_case_					:		List[Any] 					=					mask.repeat(self.scale   ,		axis=0       ).repeat(self.scale   ,		axis=1       )

											return torch.tensor(mask.flatten()       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Any 					=					torch.stack([example["""pixel_values"""] for example in examples]			)
					snake_case_					:		Optional[int] 					=					torch.stack([example["""mask"""] for example in examples]			)
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Any 					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
					if len(sys.argv			) == 2 and sys.argv[1].endswith(""".json"""			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										snake_case_					:		Tuple 					=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										snake_case_					:		List[Any] 					=					parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry("""run_mim"""				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					# Setup logging
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,    datefmt="""%m/%d/%Y %H:%M:%S"""				,    handlers=[logging.StreamHandler(sys.stdout			)]				,    )

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					snake_case_					:		Optional[int] 					=					training_args.get_process_log_level()
					logger.setLevel(SCREAMING_SNAKE_CASE__			)
					transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__			)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
					    + f'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
					logger.info(f'Training/evaluation parameters {training_args}'			)

					# Detecting last checkpoint.
					snake_case_					:		List[Any] 					=					None
					if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
										snake_case_					:		Dict 					=					get_last_checkpoint(training_args.output_dir			)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
															raise ValueError(
															    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
															    """Use --overwrite_output_dir to overcome."""			)
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
															    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""			)

    # Initialize our dataset.
					snake_case_					:		List[Any] 					=					load_dataset(
					    data_args.dataset_name				,    data_args.dataset_config_name				,    data_files=data_args.data_files				,    cache_dir=model_args.cache_dir				,    use_auth_token=True if model_args.use_auth_token else None				,    )

					# If we don't have a validation split, split off a percentage of train as validation.
					snake_case_					:		int 					=					None if """validation""" in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split				,    SCREAMING_SNAKE_CASE__			) and data_args.train_val_split > 0.0:
										snake_case_					:		List[Any] 					=					ds["""train"""].train_test_split(data_args.train_val_split			)
										snake_case_					:		List[str] 					=					split["""train"""]
										snake_case_					:		List[str] 					=					split["""test"""]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					snake_case_					:		Dict 					=					{
					    """cache_dir""": model_args.cache_dir,
					    """revision""": model_args.model_revision,
					    """use_auth_token""": True if model_args.use_auth_token else None,
					}
					if model_args.config_name_or_path:
										snake_case_					:		str 					=					AutoConfig.from_pretrained(model_args.config_name_or_path				,    **SCREAMING_SNAKE_CASE__			)
					elif model_args.model_name_or_path:
										snake_case_					:		str 					=					AutoConfig.from_pretrained(model_args.model_name_or_path				,    **SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		Dict 					=					CONFIG_MAPPING[model_args.model_type]()
										logger.warning("""You are instantiating a new config instance from scratch."""			)
										if model_args.config_overrides is not None:
															logger.info(f'Overriding config: {model_args.config_overrides}'			)
															config.update_from_string(model_args.config_overrides			)
															logger.info(f'New config: {config}'			)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(SCREAMING_SNAKE_CASE__				,    """decoder_type"""			):
										snake_case_					:		str 					=					"""simmim"""

					# adapt config
					snake_case_					:		Tuple 					=					model_args.image_size if model_args.image_size is not None else config.image_size
					snake_case_					:		str 					=					model_args.patch_size if model_args.patch_size is not None else config.patch_size
					snake_case_					:		List[Any] 					=					(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        """image_size""": model_args.image_size,
					        """patch_size""": model_args.patch_size,
					        """encoder_stride""": model_args.encoder_stride,
					    }			)

					# create image processor
					if model_args.image_processor_name:
										snake_case_					:		Union[str, Any] 					=					AutoImageProcessor.from_pretrained(model_args.image_processor_name				,    **SCREAMING_SNAKE_CASE__			)
					elif model_args.model_name_or_path:
										snake_case_					:		Optional[Any] 					=					AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,    **SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		str 					=					{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										snake_case_					:		Tuple 					=					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										snake_case_					:		List[Any] 					=					AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path				,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)				,    config=SCREAMING_SNAKE_CASE__				,    cache_dir=model_args.cache_dir				,    revision=model_args.model_revision				,    use_auth_token=True if model_args.use_auth_token else None				,    )
					else:
										logger.info("""Training new model from scratch"""			)
										snake_case_					:		Optional[int] 					=					AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__			)

					if training_args.do_train:
										snake_case_					:		str 					=					ds["""train"""].column_names
					else:
										snake_case_					:		Union[str, Any] 					=					ds["""validation"""].column_names

					if data_args.image_column_name is not None:
										snake_case_					:		Any 					=					data_args.image_column_name
					elif "image" in column_names:
										snake_case_					:		str 					=					"""image"""
					elif "img" in column_names:
										snake_case_					:		Dict 					=					"""img"""
					else:
										snake_case_					:		Optional[int] 					=					column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					snake_case_					:		List[str] 					=					Compose(
					    [
					        Lambda(lambda SCREAMING_SNAKE_CASE__			: img.convert("""RGB"""			) if img.mode != "RGB" else img			),
					        RandomResizedCrop(model_args.image_size				,    scale=(0.67, 1.0)				,    ratio=(3.0 / 4.0, 4.0 / 3.0)			),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean				,    std=image_processor.image_std			),
					    ]			)

					# create mask generator
					snake_case_					:		Any 					=					MaskGenerator(
					    input_size=model_args.image_size				,    mask_patch_size=data_args.mask_patch_size				,    model_patch_size=model_args.patch_size				,    mask_ratio=data_args.mask_ratio				,    )

					def preprocess_images(SCREAMING_SNAKE_CASE__ :  List[Any]			):
										snake_case_					:		List[str] 					=					[transforms(SCREAMING_SNAKE_CASE__			) for image in examples[image_column_name]]
										snake_case_					:		Any 					=					[mask_generator() for i in range(len(examples[image_column_name]			)			)]

										return examples

					if training_args.do_train:
										if "train" not in ds:
															raise ValueError("""--do_train requires a train dataset"""			)
										if data_args.max_train_samples is not None:
															snake_case_					:		Optional[int] 					=					ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
										# Set the training transforms
										ds["train"].set_transform(SCREAMING_SNAKE_CASE__			)

					if training_args.do_eval:
										if "validation" not in ds:
															raise ValueError("""--do_eval requires a validation dataset"""			)
										if data_args.max_eval_samples is not None:
															snake_case_					:		Union[str, Any] 					=					(
															    ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
															)
										# Set the validation transforms
										ds["validation"].set_transform(SCREAMING_SNAKE_CASE__			)

					# Initialize our trainer
					snake_case_					:		Tuple 					=					Trainer(
					    model=SCREAMING_SNAKE_CASE__				,    args=SCREAMING_SNAKE_CASE__				,    train_dataset=ds["""train"""] if training_args.do_train else None				,    eval_dataset=ds["""validation"""] if training_args.do_eval else None				,    tokenizer=SCREAMING_SNAKE_CASE__				,    data_collator=SCREAMING_SNAKE_CASE__				,    )

					# Training
					if training_args.do_train:
										snake_case_					:		Any 					=					None
										if training_args.resume_from_checkpoint is not None:
															snake_case_					:		str 					=					training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															snake_case_					:		int 					=					last_checkpoint
										snake_case_					:		List[Any] 					=					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__			)
										trainer.save_model()
										trainer.log_metrics("""train"""				,    train_result.metrics			)
										trainer.save_metrics("""train"""				,    train_result.metrics			)
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										snake_case_					:		List[Any] 					=					trainer.evaluate()
										trainer.log_metrics("""eval"""				,    SCREAMING_SNAKE_CASE__			)
										trainer.save_metrics("""eval"""				,    SCREAMING_SNAKE_CASE__			)

					# Write model card and (optionally) push to hub
					snake_case_					:		Optional[Any] 					=					{
					    """finetuned_from""": model_args.model_name_or_path,
					    """tasks""": """masked-image-modeling""",
					    """dataset""": data_args.dataset_name,
					    """tags""": ["""masked-image-modeling"""],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**SCREAMING_SNAKE_CASE__			)
					else:
										trainer.create_model_card(**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




