





"""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


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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]   )					->Optional[MinHash]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str   )					->Set[str]:






	'''simple docstring'''






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


class 			__UpperCamelCase							:






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

								a :						Dict      =							defaultdict(lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								a :						List[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        __a  (  self )      ->					List[List[Dict]]:
								a :						Tuple      =							[]
								for base, duplicates in self._duplicate_clusters.items():
									a :						Optional[int]      =							[base] + list(lowerCAmelCase__ )
									# reformat the cluster to be a list of dict
									a :						Union[str, Any]      =							[{"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:
								a :						List[Any]      =							self.get_duplicate_clusters()
								with open(lowerCAmelCase__ ,   "w" ) as f:
									json.dump(lowerCAmelCase__ ,   lowerCAmelCase__ )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->str:






	'''simple docstring'''






	a,			a :						int      =							element
	a :						Dict      =							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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Type[Dataset]   )					->Union[str, Any]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Type[Dataset]       ,			_lowercase		:       float   )					->Optional[int]:






	'''simple docstring'''






	a :						Union[str, Any]      =							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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->float:






	'''simple docstring'''






	a :						List[Any]      =							get_tokens(_lowercase   )
	a :						Tuple      =							get_tokens(_lowercase   )
	return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


a		:					Dict	      =     None






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       List[str]   )					->List[Any]:






	'''simple docstring'''






	a :						str      =							[]
	for elementa in cluster:
		a :						Any      =							_shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			a :						Dict      =							_shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(_lowercase       ,			_lowercase   ) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			a :						Tuple      =							1
			extremes.append(_lowercase   )
	return extremes






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]       ,			_lowercase		:       str       ,			_lowercase		:       Dict   )					->int:






	'''simple docstring'''






	global _shared_dataset
	a :						List[str]      =							dataset
	a :						Dict      =							[]
	a :						Tuple      =							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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Type[Dataset]       ,			_lowercase		:       float = 0.85   )					->Tuple[Type[Dataset], List[List[Dict]]]:






	'''simple docstring'''






	a :						Any      =							make_duplicate_clusters(_lowercase       ,			_lowercase   )
	a :						int      =							{x["base_index"] for cluster in duplicate_clusters for x in cluster}
	a :						Union[str, Any]      =							{}
	a :						Any      =							find_extremes(_lowercase       ,			_lowercase       ,			_lowercase   )
	for extremes in extremes_clusters:
		for element in extremes:
			a :						Optional[Any]      =							element
	a :						Dict      =							duplicate_indices - set(extreme_dict.keys()   )
	a :						Any      =							dataset.filter(lambda _lowercase       ,			_lowercase   : idx not in remove_indices       ,			with_indices=_lowercase   )

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			a :						List[Any]      =							element["base_index"] in extreme_dict
			if element["is_extreme"]:
				a :						List[Any]      =							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 argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


