


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Optional[MinHash]:
						if len(SCREAMING_SNAKE_CASE_  ) < MIN_NUM_TOKENS:
												return None
						SCREAMING_SNAKE_CASE													=					MinHash(num_perm=SCREAMING_SNAKE_CASE_  )
						for token in set(SCREAMING_SNAKE_CASE_  ):
												min_hash.update(token.encode()  )
						return min_hash


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						Set[str]:
						return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE_  ) if len(t.strip()  ) > 0}
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       *,
		    lowerCAmelCase__ = 0.85						,       )     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					duplication_jaccard_threshold
								SCREAMING_SNAKE_CASE													=					NUM_PERM
								SCREAMING_SNAKE_CASE													=					MinHashLSH(threshold=self._duplication_jaccard_threshold						,       num_perm=self._num_perm				)

								SCREAMING_SNAKE_CASE													=					defaultdict(lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								SCREAMING_SNAKE_CASE													=					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 							__A						(	self				)     ->							List[List[Dict]]:
								SCREAMING_SNAKE_CASE													=					[]
								for base, duplicates in self._duplicate_clusters.items():
														SCREAMING_SNAKE_CASE													=					[base] + list(lowerCAmelCase__				)
														# reformat the cluster to be a list of dict
														SCREAMING_SNAKE_CASE													=					[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
														duplicate_clusters.append(lowerCAmelCase__				)
								return duplicate_clusters







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





def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						str:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					element
						SCREAMING_SNAKE_CASE													=					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     lowercase			(SCREAMING_SNAKE_CASE_		:				Type[Dataset]  )   ->						Tuple:
						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash ,			ThreadedIterator(SCREAMING_SNAKE_CASE_ ,			max_queue_size=1_00_00  ) ,			chunksize=1_00 ,			):
																		if data is not None:
																								yield data


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Type[Dataset] ,			SCREAMING_SNAKE_CASE_		:				float  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE_  )

						for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE_  )  ) ,			max_queue_size=1_00  )  ):
												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     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						float:
						SCREAMING_SNAKE_CASE													=					get_tokens(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					get_tokens(SCREAMING_SNAKE_CASE_  )
						return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


__UpperCamelCase						=      None


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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Any:
						global _shared_dataset
						SCREAMING_SNAKE_CASE													=					dataset
						SCREAMING_SNAKE_CASE													=					[]
						SCREAMING_SNAKE_CASE													=					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     lowercase			(SCREAMING_SNAKE_CASE_		:				Type[Dataset] ,			SCREAMING_SNAKE_CASE_		:				float = 0.85  )   ->						Tuple[Type[Dataset], List[List[Dict]]]:
						SCREAMING_SNAKE_CASE													=					make_duplicate_clusters(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					{x['base_index'] for cluster in duplicate_clusters for x in cluster}
						SCREAMING_SNAKE_CASE													=					{}
						SCREAMING_SNAKE_CASE													=					find_extremes(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						for extremes in extremes_clusters:
												for element in extremes:
																		SCREAMING_SNAKE_CASE													=					element
						SCREAMING_SNAKE_CASE													=					duplicate_indices - set(extreme_dict.keys()  )
						SCREAMING_SNAKE_CASE													=					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:
																		SCREAMING_SNAKE_CASE													=					element['base_index'] in extreme_dict
																		if element["is_extreme"]:
																								SCREAMING_SNAKE_CASE													=					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






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@property
		def 							__A						(	self				)     ->							List[Any]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'				)

								# using the PNDM scheduler by default
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 1e-2


