



'''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


snake_case_			:     Any  	=  re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
snake_case_			:     str  	=  10
snake_case_			:     Union[str, Any]  	=  256






def A__   (					UpperCAmelCase_							):
						if len(UpperCAmelCase_							) < MIN_NUM_TOKENS:
												return None
						_UpperCamelCase :		str			    =       MinHash(num_perm=UpperCAmelCase_							)
						for token in set(UpperCAmelCase_							):
												min_hash.update(token.encode()							)
						return min_hash






def A__   (					UpperCAmelCase_							):
						return {t for t in NON_ALPHA.split(UpperCAmelCase_							) if len(t.strip()							) > 0}






class 							lowercase__     :





	def __init__(			self :    Tuple			,*,
	    lowerCamelCase__ :    float = 0.8_5			,):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       duplication_jaccard_threshold
							_UpperCamelCase :		Dict			    =       NUM_PERM
							_UpperCamelCase :		str			    =       MinHashLSH(threshold=self._duplication_jaccard_threshold			,num_perm=self._num_perm			)

							_UpperCamelCase :		int			    =       defaultdict(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    MinHash			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       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 :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       []
							for base, duplicates in self._duplicate_clusters.items():
													_UpperCamelCase :		List[Any]			    =       [base] + list(lowerCamelCase__			)
													# reformat the cluster to be a list of dict
													_UpperCamelCase :		List[str]			    =       [{'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 :    Optional[int]			,lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.get_duplicate_clusters()
							with open(lowerCamelCase__			,'w'			) as f:
													json.dump(lowerCamelCase__			,lowerCamelCase__			)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Any			    =       element
						_UpperCamelCase :		Union[str, Any]			    =       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__   (					UpperCAmelCase_							):
						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash  ,  ThreadedIterator(UpperCAmelCase_  ,  max_queue_size=1_0_0_0_0							)  ,  chunksize=1_0_0  ,  ):
																		if data is not None:
																								yield data






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Dict			    =       DuplicationIndex(duplication_jaccard_threshold=UpperCAmelCase_							)

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

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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Optional[Any]			    =       get_tokens(UpperCAmelCase_							)
						_UpperCamelCase :		Optional[Any]			    =       get_tokens(UpperCAmelCase_							)
						return len(tokensa & tokensa							) / len(tokensa | tokensa							)


snake_case_			:     Optional[int]  	=  None






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Tuple			    =       []
						for elementa in cluster:
												_UpperCamelCase :		Dict			    =       _shared_dataset[elementa['base_index']]['content']
												for elementa in extremes:
																		_UpperCamelCase :		Dict			    =       _shared_dataset[elementa['base_index']]['content']
																		if jaccard_similarity(UpperCAmelCase_  ,  UpperCAmelCase_							) >= jaccard_threshold:
																								elementa["copies"] += 1
																								break
												else:
																		_UpperCamelCase :		int			    =       1
																		extremes.append(UpperCAmelCase_							)
						return extremes






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						global _shared_dataset
						_UpperCamelCase :		Any			    =       dataset
						_UpperCamelCase :		int			    =       []
						_UpperCamelCase :		List[Any]			    =       partial(_find_cluster_extremes_shared  ,  jaccard_threshold=UpperCAmelCase_							)
						with mp.Pool() as pool:
												for extremes in tqdm(
												    pool.imap_unordered(
												        UpperCAmelCase_  ,  UpperCAmelCase_  ,  )  ,  total=len(UpperCAmelCase_							)  ,  ):
																		extremes_list.append(UpperCAmelCase_							)
						return extremes_list






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_ = 0.85							):
						_UpperCamelCase :		List[Any]			    =       make_duplicate_clusters(UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase :		int			    =       {x['base_index'] for cluster in duplicate_clusters for x in cluster}
						_UpperCamelCase :		Tuple			    =       {}
						_UpperCamelCase :		Any			    =       find_extremes(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						for extremes in extremes_clusters:
												for element in extremes:
																		_UpperCamelCase :		Any			    =       element
						_UpperCamelCase :		Tuple			    =       duplicate_indices - set(extreme_dict.keys()							)
						_UpperCamelCase :		Union[str, Any]			    =       dataset.filter(lambda UpperCAmelCase_  ,  UpperCAmelCase_							: idx not in remove_indices  ,  with_indices=UpperCAmelCase_							)

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

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

						return ds_filter, duplicate_clusters







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_			:     Optional[Any]  	=  {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPFeatureExtractor']
					snake_case_			:     Dict  	=  ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Tuple  	=  [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     str  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


