
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


snake_case :		str							      =  re.compile(R"\s+")






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Dict:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  int   )				->		List[str]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     np.mean([c.isalnum() for c in example["content"]]   )
	return {"alpha_frac": alpha_frac}






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  List[str]=5   )				->		Any:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Tuple=5      ,   _snake_case      :  Optional[int]=0.05   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     ["unit tests", "test file", "configuration file"]
	__magic_name__		:		Any	   =     example["content"].splitlines()
	__magic_name__		:		Optional[Any]	   =     0
	__magic_name__		:		Tuple	   =     0
	# first test
	for _, line in zip(range(_snake_case   )      ,   _snake_case   ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	__magic_name__		:		Union[str, Any]	   =     example["content"].count("\n"   )
	__magic_name__		:		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   lowerCAmelCase_						(					_snake_case      :  Any   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     ["def ", "class ", "for ", "while "]
	__magic_name__		:		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   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  int=4   )				->		Union[str, Any]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     tokenizer(example["content"]      ,   truncation=_snake_case   )["input_ids"]
	__magic_name__		:		List[Any]	   =     len(example["content"]   ) / len(_snake_case   )
	return {"ratio": ratio}






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		str	   =     {}
	results.update(get_hash(_snake_case   )   )
	results.update(line_stats(_snake_case   )   )
	results.update(alpha_stats(_snake_case   )   )
	results.update(char_token_ratio(_snake_case   )   )
	results.update(is_autogenerated(_snake_case   )   )
	results.update(is_config_or_test(_snake_case   )   )
	results.update(has_no_keywords(_snake_case   )   )
	results.update(has_few_assignments(_snake_case   )   )
	return results






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  List[str]   )				->		Union[str, Any]:







	'''simple docstring'''


	if not check_uniques(_snake_case      ,   _snake_case   ):
		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_						(					_snake_case      :  Union[str, Any]   )				->		List[Any]:







	'''simple docstring'''


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


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

# Load dataset
snake_case :		int							      =  time.time()
snake_case :		Tuple							      =  load_dataset(args.dataset_name, split="train")
print(F"Time to load dataset: {time.time()-t_start:.2f}")

# Run preprocessing
snake_case :		str							      =  time.time()
snake_case :		str							      =  ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

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

# Deduplicate data and apply heuristics
snake_case :		Any							      =  time.time()
snake_case :		Union[str, 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:
				snake_case :		Tuple							      =  time.time()
				snake_case      ,snake_case :		Any							      =  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
snake_case :		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)

snake_case :		Dict							      =  output_dir / "data"
data_dir.mkdir(exist_ok=True)

snake_case :		Tuple							      =  time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
				snake_case :		Optional[Any]							      =  str(data_dir / F"file-{file_number+1:012}.json")
				snake_case :		Tuple							      =  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}")


from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	if not tree:
		return True
	if tree.left and tree.right:
		return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
	else:
		return not tree.left and not tree.right






def   lowerCAmelCase_						(					)				->		None:  # Main function for testing.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
