






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :					Optional[Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					List[str]   							=					{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''megatron-bert'''

							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:str=29_056       ,							lowerCAmelCase__			:Tuple=1_024       ,							lowerCAmelCase__			:int=24       ,							lowerCAmelCase__			:Optional[Any]=16       ,							lowerCAmelCase__			:List[str]=4_096       ,							lowerCAmelCase__			:List[str]="gelu"       ,							lowerCAmelCase__			:Any=0.1       ,							lowerCAmelCase__			:List[str]=0.1       ,							lowerCAmelCase__			:Optional[Any]=512       ,							lowerCAmelCase__			:Dict=2       ,							lowerCAmelCase__			:int=0.0_2       ,							lowerCAmelCase__			:Union[str, Any]=1E-1_2       ,							lowerCAmelCase__			:int=0       ,							lowerCAmelCase__			:List[Any]="absolute"       ,							lowerCAmelCase__			:Tuple=True       ,							**lowerCAmelCase__			:Dict       ,							)				->  int:


										'''simple docstring'''




										super().__init__(pad_token_id=lowerCAmelCase__       ,							**lowerCAmelCase__				)

										snake_case_				:					int   =       vocab_size
										snake_case_				:					str   =       hidden_size
										snake_case_				:					Union[str, Any]   =       num_hidden_layers
										snake_case_				:					Tuple   =       num_attention_heads
										snake_case_				:					List[Any]   =       hidden_act
										snake_case_				:					Tuple   =       intermediate_size
										snake_case_				:					List[str]   =       hidden_dropout_prob
										snake_case_				:					List[str]   =       attention_probs_dropout_prob
										snake_case_				:					str   =       max_position_embeddings
										snake_case_				:					str   =       type_vocab_size
										snake_case_				:					List[Any]   =       initializer_range
										snake_case_				:					Dict   =       layer_norm_eps
										snake_case_				:					List[Any]   =       position_embedding_type
										snake_case_				:					str   =       use_cache












'''simple docstring'''




import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


__lowerCamelCase   :					List[str]   							=					re.compile(R'''\s+''')

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			return {"hash": hashlib.mda(re.sub(__magic_name__     ,""     ,example["content"]						).encode("utf-8"						)						).hexdigest()}

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       [len(__magic_name__						) for line in example["content"].splitlines()]
			return {"line_mean": np.mean(__magic_name__						), "line_max": max(__magic_name__						)}

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       np.mean([c.isalnum() for c in example["content"]]						)
			return {"alpha_frac": alpha_frac}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if example["hash"] in uniques:
						uniques.remove(example["hash"]						)
						return True
			else:
						return False

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					List[str]   =       ["auto-generated", "autogenerated", "automatically generated"]
			snake_case_				:					Optional[Any]   =       example["content"].splitlines()
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5     ,__magic_name__=0.05						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       ["unit tests", "test file", "configuration file"]
			snake_case_				:					int   =       example["content"].splitlines()
			snake_case_				:					Optional[Any]   =       0
			snake_case_				:					Any   =       0
			# first test
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			snake_case_				:					Tuple   =       example["content"].count("\n"						)
			snake_case_				:					int   =       int(coeff * nlines						)
			for line in lines:
						count_config += line.lower().count("config"						)
						count_test += line.lower().count("test"						)
						if count_config > threshold or count_test > threshold:
									return {"config_or_test": True}
			return {"config_or_test": False}

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					List[Any]   =       ["def ", "class ", "for ", "while "]
			snake_case_				:					Optional[Any]   =       example["content"].splitlines()
			for line in lines:
						for keyword in keywords:
									if keyword in line.lower():
												return {"has_no_keywords": False}
			return {"has_no_keywords": True}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=4						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       example["content"].splitlines()
			snake_case_				:					Tuple   =       0
			for line in lines:
						counter += line.lower().count("="						)
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       tokenizer(example["content"]     ,truncation=__magic_name__						)["input_ids"]
			snake_case_				:					int   =       len(example["content"]						) / len(__magic_name__						)
			return {"ratio": ratio}

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       {}
			results.update(get_hash(__magic_name__						)						)
			results.update(line_stats(__magic_name__						)						)
			results.update(alpha_stats(__magic_name__						)						)
			results.update(char_token_ratio(__magic_name__						)						)
			results.update(is_autogenerated(__magic_name__						)						)
			results.update(is_config_or_test(__magic_name__						)						)
			results.update(has_no_keywords(__magic_name__						)						)
			results.update(has_few_assignments(__magic_name__						)						)
			return results

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if not check_uniques(__magic_name__     ,__magic_name__						):
						return False
			elif example["autogenerated"]:
						return False
			elif example["line_max"] > args.line_max:
						return False
			elif example["line_mean"] > args.line_mean:
						return False
			elif example["alpha_frac"] < args.alpha_frac:
						return False
			elif example["ratio"] < args.min_token_ratio:
						return False
			elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_few_assignments"]:
						return False
			else:
						return True

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			with open(__magic_name__     ,"rb"						) as f_in:
						with gzip.open(str(__magic_name__						) + ".gz"     ,"wb"     ,compresslevel=6						) as f_out:
									shutil.copyfileobj(__magic_name__     ,__magic_name__						)
			os.unlink(__magic_name__						)


# Settings
__lowerCamelCase   :					List[Any]   							=					HfArgumentParser(PreprocessingArguments)
__lowerCamelCase   :					str   							=					parser.parse_args()
if args.num_workers is None:
		__lowerCamelCase   :					List[Any]   							=					multiprocessing.cpu_count()
__lowerCamelCase   :					Optional[int]   							=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__lowerCamelCase   :					Any   							=					time.time()
__lowerCamelCase   :					str   							=					load_dataset(args.dataset_name, split='''train''')
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Any   							=					ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__lowerCamelCase   :					Any   							=					set(ds.unique('''hash'''))
__lowerCamelCase   :					Optional[int]   							=					len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Tuple   							=					ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(f'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(f'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
		__lowerCamelCase   :					List[str]   							=					time.time()
		__lowerCamelCase							,      __lowerCamelCase   :					Tuple   							=					deduplicate_dataset(ds_filter, args.jaccard_threshold)
		print(f'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
		print(f'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
__lowerCamelCase   :					List[Any]   							=					Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
		with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
				json.dump(duplicate_clusters, f)

__lowerCamelCase   :					List[str]   							=					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

__lowerCamelCase   :					int   							=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		__lowerCamelCase   :					Union[str, Any]   							=					str(data_dir / f'''file-{file_number+1:012}.json''')
		__lowerCamelCase   :					List[Any]   							=					min(len(ds_filter), index + args.samples_per_file)
		ds_filter.select(list(range(index, end_index))).to_json(file_path)
		compress_file(file_path)






print(f'''Time to save dataset: {time.time()-t_start:.2f}''')




