

'''simple docstring'''





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


_a:    Tuple 							=			logging.get_logger(__name__)

_a:    Union[str, Any] 							=			{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 						__UpperCamelCase    (					_UpperCAmelCase						):
	SCREAMING_SNAKE_CASE__    				=     'megatron-bert'




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						int=29_056		,	lowerCAmelCase	:						List[str]=1_024		,	lowerCAmelCase	:						Optional[int]=24		,	lowerCAmelCase	:						Any=16		,	lowerCAmelCase	:						List[Any]=4_096		,	lowerCAmelCase	:						int="gelu"		,	lowerCAmelCase	:						Optional[int]=0.1		,	lowerCAmelCase	:						str=0.1		,	lowerCAmelCase	:						str=512		,	lowerCAmelCase	:						Union[str, Any]=2		,	lowerCAmelCase	:						List[str]=0.02		,	lowerCAmelCase	:						Tuple=1e-12		,	lowerCAmelCase	:						Any=0		,	lowerCAmelCase	:						Tuple="absolute"		,	lowerCAmelCase	:						Any=True		,	**lowerCAmelCase	:						Optional[Any]		,	):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			type_vocab_size
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			position_embedding_type
		UpperCAmelCase_       				=			use_cache


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


_a:    Optional[int] 							=			re.compile(r"""\s+""")



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



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			[len(A    ) for line in example["content"].splitlines()]
	return {"line_mean": np.mean(A    ), "line_max": max(A    )}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			np.mean([c.isalnum() for c in example["content"]]    )
	return {"alpha_frac": alpha_frac}



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



def       __lowerCAmelCase      (						A       ,     A=5    ):
	UpperCAmelCase_       				=			["auto-generated", "autogenerated", "automatically generated"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	for _, line in zip(range(A    )       ,     A    ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"autogenerated": True}
	else:
		return {"autogenerated": False}



def       __lowerCAmelCase      (						A       ,     A=5       ,     A=0.05    ):
	UpperCAmelCase_       				=			["unit tests", "test file", "configuration file"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	UpperCAmelCase_       				=			0
	UpperCAmelCase_       				=			0
	# first test
	for _, line in zip(range(A    )       ,     A    ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	UpperCAmelCase_       				=			example["content"].count("\n"    )
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			["def ", "class ", "for ", "while "]
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A       ,     A=4    ):
	UpperCAmelCase_       				=			example["content"].splitlines()
	UpperCAmelCase_       				=			0
	for line in lines:
		counter += line.lower().count("="    )
		if counter > minimum:
			return {"has_few_assignments": False}
	return {"has_few_assignments": True}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			tokenizer(example["content"]       ,     truncation=A    )["input_ids"]
	UpperCAmelCase_       				=			len(example["content"]    ) / len(A    )
	return {"ratio": ratio}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			{}
	results.update(get_hash(A    )    )
	results.update(line_stats(A    )    )
	results.update(alpha_stats(A    )    )
	results.update(char_token_ratio(A    )    )
	results.update(is_autogenerated(A    )    )
	results.update(is_config_or_test(A    )    )
	results.update(has_no_keywords(A    )    )
	results.update(has_few_assignments(A    )    )
	return results



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	if not check_uniques(A       ,     A    ):
		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       __lowerCAmelCase      (						A    ):
	with open(A       ,     "rb"    ) as f_in:
		with gzip.open(str(A    ) + ".gz"       ,     "wb"       ,     compresslevel=6    ) as f_out:
			shutil.copyfileobj(A       ,     A    )
	os.unlink(A    )


# Settings
_a:    str 							=			HfArgumentParser(PreprocessingArguments)
_a:    Any 							=			parser.parse_args()
if args.num_workers is None:
				_a:    Tuple 							=			multiprocessing.cpu_count()
_a:    str 							=			AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_a:    Union[str, Any] 							=			time.time()
_a:    List[str] 							=			load_dataset(args.dataset_name, split="""train""")
print(F'Time to load dataset: {time.time()-t_start:.2f}')

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

# Deduplicate hashes
_a:    Union[str, Any] 							=			set(ds.unique("""hash"""))
_a:    Dict 							=			len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
_a:    Optional[Any] 							=			time.time()
_a:    Any 							=			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:
				_a:    Tuple 							=			time.time()
				_a							,			_a:    Dict 							=			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
_a:    List[str] 							=			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)

_a:    int 							=			output_dir / """data"""
data_dir.mkdir(exist_ok=True)

_a:    Dict 							=			time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
				_a:    int 							=			str(data_dir / F'file-{file_number+1:012}.json')
				_a:    int 							=			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}')