


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			:						Optional[Any]        =  re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
A			:						Optional[Any]        =  1_0
A			:						Any        =  2_5_6






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    Optional[MinHash]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    Set[str]:
		"""simple docstring"""




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



class 				A       :
					'''simple docstring'''






					def __init__(self       :		Optional[int] ,   *,
					    _UpperCAmelCase       :		float = 0.85 ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						duplication_jaccard_threshold
							lowercase__	     =						NUM_PERM
							lowercase__	     =						MinHashLSH(threshold=self._duplication_jaccard_threshold ,   num_perm=self._num_perm						)

							lowercase__	     =						defaultdict(_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		MinHash						)		->  None:




							"""simple docstring"""


							lowercase__	     =						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     lowerCamelCase__	(self       :		List[Any]						)		->  List[List[Dict]]:




							"""simple docstring"""


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


					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		str						)		->  None:




							"""simple docstring"""


							lowercase__	     =						self.get_duplicate_clusters()
							with open(_UpperCAmelCase ,   """w"""						) as f:
									json.dump(_UpperCAmelCase ,   _UpperCAmelCase						)






def 					UpperCamelCase   (      __magic_name__	:				Any	)  ->    List[str]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						element
		lowercase__	     =						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 					UpperCamelCase   (      __magic_name__	:				Type[Dataset]	)  ->    Union[str, Any]:
		"""simple docstring"""




		with mp.Pool() as pool:
				for data in pool.imap_unordered(
				    _compute_min_hash       ,       ThreadedIterator(__magic_name__       ,       max_queue_size=1_0000	)       ,       chunksize=100       ,       ):
						if data is not None:
								yield data






def 					UpperCamelCase   (      __magic_name__	:				Type[Dataset]       ,       __magic_name__	:				float	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						DuplicationIndex(duplication_jaccard_threshold=__magic_name__	)

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

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






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str	)  ->    float:
		"""simple docstring"""




		lowercase__	     =						get_tokens(__magic_name__	)
		lowercase__	     =						get_tokens(__magic_name__	)
		return len(tokensa & tokensa	) / len(tokensa | tokensa	)


A			:						Tuple        =  None






def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]	)  ->    str:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Optional[Any]	)  ->    int:
		"""simple docstring"""




		global _shared_dataset
		lowercase__	     =						dataset
		lowercase__	     =						[]
		lowercase__	     =						partial(_find_cluster_extremes_shared       ,       jaccard_threshold=__magic_name__	)
		with mp.Pool() as pool:
				for extremes in tqdm(
				    pool.imap_unordered(
				        __magic_name__       ,       __magic_name__       ,       )       ,       total=len(__magic_name__	)       ,       ):
						extremes_list.append(__magic_name__	)
		return extremes_list







def 					UpperCamelCase   (      __magic_name__	:				Type[Dataset]       ,       __magic_name__	:				float = 0.8_5	)  ->    Tuple[Type[Dataset], List[List[Dict]]]:
		"""simple docstring"""




		lowercase__	     =						make_duplicate_clusters(__magic_name__       ,       __magic_name__	)
		lowercase__	     =						{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
		lowercase__	     =						{}
		lowercase__	     =						find_extremes(__magic_name__       ,       __magic_name__       ,       __magic_name__	)
		for extremes in extremes_clusters:
				for element in extremes:
						lowercase__	     =						element
		lowercase__	     =						duplicate_indices - set(extreme_dict.keys()	)
		lowercase__	     =						dataset.filter(lambda __magic_name__       ,       __magic_name__	: idx not in remove_indices       ,       with_indices=__magic_name__	)

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

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

		return ds_filter, duplicate_clusters





import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

