



'''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__ :			List[str]												=				re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
UpperCamelCase__ :			Any												=				10
UpperCamelCase__ :			Dict												=				2_56


def        lowerCAmelCase_     (			_lowerCamelCase:     List[str]      ):
		if len(_lowerCamelCase      ) < MIN_NUM_TOKENS:
				return None
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	MinHash(num_perm=_lowerCamelCase      )
		for token in set(_lowerCamelCase      ):
				min_hash.update(token.encode()      )
		return min_hash


def        lowerCAmelCase_     (			_lowerCamelCase:     str      ):
		return {t for t in NON_ALPHA.split(_lowerCamelCase      ) if len(t.strip()      ) > 0}






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  *,
			    lowerCAmelCase__  :				float = 0.85							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	duplication_jaccard_threshold
					__SCREAMING_SNAKE_CASE :	List[str]					 =	NUM_PERM
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	MinHashLSH(threshold=self._duplication_jaccard_threshold							,  num_perm=self._num_perm		)

					__SCREAMING_SNAKE_CASE :	Any					 =	defaultdict(lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				MinHash		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	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  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	[]
					for base, duplicates in self._duplicate_clusters.items():
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[base] + list(lowerCAmelCase__		)
							# reformat the cluster to be a list of dict
							__SCREAMING_SNAKE_CASE :	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 UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.get_duplicate_clusters()
					with open(lowerCAmelCase__							,  """w"""		) as f:
							json.dump(lowerCAmelCase__							,  lowerCAmelCase__		)


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any]      ):
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	element
		__SCREAMING_SNAKE_CASE :	List[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        lowerCAmelCase_     (			_lowerCamelCase:     Type[Dataset]      ):
		with mp.Pool() as pool:
				for data in pool.imap_unordered(
				    _compute_min_hash ,   ThreadedIterator(_lowerCamelCase ,   max_queue_size=1_00_00      ) ,   chunksize=1_00 ,   ):
						if data is not None:
								yield data


def        lowerCAmelCase_     (			_lowerCamelCase:     Type[Dataset] ,   _lowerCamelCase:     float      ):
		__SCREAMING_SNAKE_CASE :	List[str]					 =	DuplicationIndex(duplication_jaccard_threshold=_lowerCamelCase      )

		for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_lowerCamelCase      )      ) ,   max_queue_size=1_00      )      ):
				di.add(_lowerCamelCase ,   _lowerCamelCase      )

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


def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     str      ):
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	get_tokens(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	get_tokens(_lowerCamelCase      )
		return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


UpperCamelCase__ :			Union[str, Any]												=				None


def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     Optional[Any]      ):
		__SCREAMING_SNAKE_CASE :	Tuple					 =	[]
		for elementa in cluster:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	_shared_dataset[elementa["""base_index"""]]["""content"""]
				for elementa in extremes:
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	_shared_dataset[elementa["""base_index"""]]["""content"""]
						if jaccard_similarity(_lowerCamelCase ,   _lowerCamelCase      ) >= jaccard_threshold:
								elementa["copies"] += 1
								break
				else:
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	1
						extremes.append(_lowerCamelCase      )
		return extremes


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


def        lowerCAmelCase_     (			_lowerCamelCase:     Type[Dataset] ,   _lowerCamelCase:     float = 0.85      ):
		__SCREAMING_SNAKE_CASE :	int					 =	make_duplicate_clusters(_lowerCamelCase ,   _lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	int					 =	{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
		__SCREAMING_SNAKE_CASE :	int					 =	{}
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	find_extremes(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )
		for extremes in extremes_clusters:
				for element in extremes:
						__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	element
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	duplicate_indices - set(extreme_dict.keys()      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dataset.filter(lambda _lowerCamelCase ,   _lowerCamelCase      : idx not in remove_indices ,   with_indices=_lowerCamelCase      )

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

		print(F"Original dataset size: {len(_lowerCamelCase      )}"      )
		print(F"Number of duplicate clusters: {len(_lowerCamelCase      )}"      )
		print(F"Files in duplicate cluster: {len(_lowerCamelCase      )}"      )
		print(F"Unique files in duplicate cluster: {len(_lowerCamelCase      )}"      )
		print(F"Filtered dataset size: {len(_lowerCamelCase      )}"      )

		return ds_filter, duplicate_clusters




'''simple docstring'''




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__ :			Any												=				'''▁'''

UpperCamelCase__ :			Dict												=				get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Dict    					=	BertGenerationTokenizer
			_A   :	List[Any]    					=	False
			_A   :	Union[str, Any]    					=	True

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().setUp()

					__SCREAMING_SNAKE_CASE :	Dict					 =	BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__		)
					tokenizer.save_pretrained(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	"""<s>"""
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__		)							,  lowerCAmelCase__		)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__		)							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]							,  """<unk>"""		)
					self.assertEqual(vocab_keys[1]							,  """<s>"""		)
					self.assertEqual(vocab_keys[-1]							,  """<pad>"""		)
					self.assertEqual(len(lowerCAmelCase__		)							,  1_0_0_2		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_0_0		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.tokenize("""This is a test"""		)
					self.assertListEqual(lowerCAmelCase__							,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)							,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,  )

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """9""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """é""",
					        """.""",
					    ]							,  )
					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,  )

					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """<unk>""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """<unk>""",
					        """.""",
					    ]							,  )

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""		)

			@slow
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	"""Hello World!"""
					__SCREAMING_SNAKE_CASE :	Dict					 =	[1_8_5_3_6, 2_2_6_0, 1_0_1]

					self.assertListEqual(lowerCAmelCase__							,  self.big_tokenizer.encode(lowerCAmelCase__		)		)

			@slow
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
					    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
					    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
					)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[
					    8_7_1,
					    4_1_9,
					    3_5_8,
					    9_4_6,
					    9_9_1,
					    2_5_2_1,
					    4_5_2,
					    3_5_8,
					    1_3_5_7,
					    3_8_7,
					    7_7_5_1,
					    3_5_3_6,
					    1_1_2,
					    9_8_5,
					    4_5_6,
					    1_2_6,
					    8_6_5,
					    9_3_8,
					    5_4_0_0,
					    5_7_3_4,
					    4_5_8,
					    1_3_6_8,
					    4_6_7,
					    7_8_6,
					    2_4_6_2,
					    5_2_4_6,
					    1_1_5_9,
					    6_3_3,
					    8_6_5,
					    4_5_1_9,
					    4_5_7,
					    5_8_2,
					    8_5_2,
					    2_5_5_7,
					    4_2_7,
					    9_1_6,
					    5_0_8,
					    4_0_5,
					    3_4_3_2_4,
					    4_9_7,
					    3_9_1,
					    4_0_8,
					    1_1_3_4_2,
					    1_2_4_4,
					    3_8_5,
					    1_0_0,
					    9_3_8,
					    9_8_5,
					    4_5_6,
					    5_7_4,
					    3_6_2,
					    1_2_5_9_7,
					    3_2_0_0,
					    3_1_2_9,
					    1_1_7_2,
					]

					self.assertListEqual(lowerCAmelCase__							,  self.big_tokenizer.encode(lowerCAmelCase__		)		)

			@require_torch
			@slow
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					import torch

					from transformers import BertGenerationConfig, BertGenerationEncoder

					# Build sequence
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
					__SCREAMING_SNAKE_CASE :	int					 =	""" """.join(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.big_tokenizer.encode_plus(lowerCAmelCase__							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.big_tokenizer.batch_encode_plus(
					    [sequence + """ """ + sequence]							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	BertGenerationConfig()
					__SCREAMING_SNAKE_CASE :	Any					 =	BertGenerationEncoder(lowerCAmelCase__		)

					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

					with torch.no_grad():
							model(**lowerCAmelCase__		)
							model(**lowerCAmelCase__		)



			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{"""input_ids""": [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=lowerCAmelCase__							,  model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,  revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,  )