


'''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


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


def       _snake_case     (  A )		->					Union[str, Any]:







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


def       _snake_case     (  A )		->					Union[str, Any]:
							return {t for t in NON_ALPHA.split(_lowercase ) if len(t.strip() ) > 0}




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					*,
							    lowerCamelCase_ = 0.85      ,					) ->							str:
														lowerCAmelCase__      =			duplication_jaccard_threshold
														lowerCAmelCase__      =			NUM_PERM
														lowerCAmelCase__      =			MinHashLSH(threshold=self._duplication_jaccard_threshold      ,					num_perm=self._num_perm     )

														lowerCAmelCase__      =			defaultdict(__A     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							None:
														lowerCAmelCase__      =			self._index.query(__A     )
														if code_key in self._index.keys:
																					print(F"""Duplicate key {code_key}"""     )
																					return

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[List[Dict]]:
														lowerCAmelCase__      =			[]
														for base, duplicates in self._duplicate_clusters.items():
																					lowerCAmelCase__      =			[base] + list(__A     )
																					# reformat the cluster to be a list of dict
																					lowerCAmelCase__      =			[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
																					duplicate_clusters.append(__A     )
														return duplicate_clusters







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							None:
														lowerCAmelCase__      =			self.get_duplicate_clusters()
														with open(__A      ,					'''w'''     ) as f:
																					json.dump(__A      ,					__A     )


def       _snake_case     (  A )		->					str:
							lowerCAmelCase__     ,      lowerCAmelCase__      =			element
							lowerCAmelCase__      =			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       _snake_case     (  A )		->					Dict:







							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash				,					ThreadedIterator(_lowercase				,					max_queue_size=10000 )				,					chunksize=100				,					):
																					if data is not None:
																												yield data


def       _snake_case     (  A				,					A )		->					int:
							lowerCAmelCase__      =			DuplicationIndex(duplication_jaccard_threshold=_lowercase )

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

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


def       _snake_case     (  A				,					A )		->					Tuple:
							lowerCAmelCase__      =			get_tokens(_lowercase )
							lowerCAmelCase__      =			get_tokens(_lowercase )
							return len(tokensa & tokensa ) / len(tokensa | tokensa )


__UpperCAmelCase					       =							None


def       _snake_case     (  A				,					A )		->					Optional[int]:
							lowerCAmelCase__      =			[]
							for elementa in cluster:
														lowerCAmelCase__      =			_shared_dataset[elementa['''base_index''']]['''content''']
														for elementa in extremes:
																					lowerCAmelCase__      =			_shared_dataset[elementa['''base_index''']]['''content''']
																					if jaccard_similarity(_lowercase				,					_lowercase ) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					lowerCAmelCase__      =			1
																					extremes.append(_lowercase )
							return extremes


def       _snake_case     (  A				,					A				,					A )		->					str:
							global _shared_dataset
							lowerCAmelCase__      =			dataset
							lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			partial(_find_cluster_extremes_shared				,					jaccard_threshold=_lowercase )
							with mp.Pool() as pool:
														for extremes in tqdm(
														    pool.imap_unordered(
														        _lowercase				,					_lowercase				,					)				,					total=len(_lowercase )				,					):
																					extremes_list.append(_lowercase )
							return extremes_list






def       _snake_case     (  A				,					A = 0.85 )		->					List[str]:
							lowerCAmelCase__      =			make_duplicate_clusters(_lowercase				,					_lowercase )
							lowerCAmelCase__      =			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
							lowerCAmelCase__      =			{}
							lowerCAmelCase__      =			find_extremes(_lowercase				,					_lowercase				,					_lowercase )
							for extremes in extremes_clusters:
														for element in extremes:
																					lowerCAmelCase__      =			element
							lowerCAmelCase__      =			duplicate_indices - set(extreme_dict.keys() )
							lowerCAmelCase__      =			dataset.filter(lambda A				,					A : idx not in remove_indices				,					with_indices=_lowercase )

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

							print(F"""Original dataset size: {len(_lowercase )}""" )
							print(F"""Number of duplicate clusters: {len(_lowercase )}""" )
							print(F"""Files in duplicate cluster: {len(_lowercase )}""" )
							print(F"""Unique files in duplicate cluster: {len(_lowercase )}""" )
							print(F"""Filtered dataset size: {len(_lowercase )}""" )

							return ds_filter, duplicate_clusters



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




@slow
@require_torch_gpu
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

														lowerCAmelCase__      =			image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

														assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2