

"""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		:       str						    =	re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_UpperCAmelCase		:       Optional[Any]						    =	10
_UpperCAmelCase		:       List[str]						    =	256





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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







class 			lowercase    :




				def __init__( self				,       *,
				    snake_case = 0.85				,       ):
											snake_case_          =   duplication_jaccard_threshold
											snake_case_          =   NUM_PERM
											snake_case_          =   MinHashLSH(threshold=self._duplication_jaccard_threshold				,       num_perm=self._num_perm	)

											snake_case_          =   defaultdict(__UpperCAmelCase	)




				def    a							( self				,       snake_case				,       snake_case	):
											snake_case_          =   self._index.query(__UpperCAmelCase	)
											if code_key in self._index.keys:
																		print(F'''Duplicate key {code_key}'''	)
																		return

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




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




				def    a							( self				,       snake_case	):
											snake_case_          =   self.get_duplicate_clusters()
											with open(__UpperCAmelCase				,       'w'	) as f:
																		json.dump(__UpperCAmelCase				,       __UpperCAmelCase	)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_    ,   snake_case_          =   element
							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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase__				)

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

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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   get_tokens(UpperCamelCase__				)
							snake_case_          =   get_tokens(UpperCamelCase__				)
							return len(tokensa & tokensa				) / len(tokensa | tokensa				)


_UpperCAmelCase		:       Tuple						    =	None





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							global _shared_dataset
							snake_case_          =   dataset
							snake_case_          =   []
							snake_case_          =   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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = 0.85				):







							'''simple docstring'''






							snake_case_          =   make_duplicate_clusters(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   {x['base_index'] for cluster in duplicate_clusters for x in cluster}
							snake_case_          =   {}
							snake_case_          =   find_extremes(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							for extremes in extremes_clusters:
														for element in extremes:
																					snake_case_          =   element
							snake_case_          =   duplicate_indices - set(extreme_dict.keys()				)
							snake_case_          =   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:
																					snake_case_          =   element['base_index'] in extreme_dict
																					if element["is_extreme"]:
																												snake_case_          =   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







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel




