
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__						=				re.compile(R"\s+")


def 				__A(lowerCAmelCase			)		->					Union[str, Any]:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase			)		->					Optional[Any]:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase			)		->					Optional[Any]:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Optional[int]:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase=5			)		->					Any:

		"""simple docstring"""






		_UpperCamelCase         =						["""auto-generated""", """autogenerated""", """automatically generated"""]
		_UpperCamelCase         =						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 				__A(lowerCAmelCase    ,	lowerCAmelCase=5    ,	lowerCAmelCase=0.05			)		->					List[str]:

		"""simple docstring"""






		_UpperCamelCase         =						["""unit tests""", """test file""", """configuration file"""]
		_UpperCamelCase         =						example["""content"""].splitlines()
		_UpperCamelCase         =						0
		_UpperCamelCase         =						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
		_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 				__A(lowerCAmelCase			)		->					List[str]:

		"""simple docstring"""






		_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 				__A(lowerCAmelCase    ,	lowerCAmelCase=4			)		->					Tuple:

		"""simple docstring"""






		_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 				__A(lowerCAmelCase			)		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						tokenizer(example["""content"""]    ,	truncation=snake_case__			)["""input_ids"""]
		_UpperCamelCase         =						len(example["""content"""]			) / len(snake_case__			)
		return {"ratio": ratio}


def 				__A(lowerCAmelCase			)		->					Any:

		"""simple docstring"""






		_UpperCamelCase         =						{}
		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 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)		->					Optional[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 				__A(lowerCAmelCase			)		->					int:

		"""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
lowerCamelCase__						=				HfArgumentParser(PreprocessingArguments)
lowerCamelCase__						=				parser.parse_args()
if args.num_workers is None:
		lowerCamelCase__						=				multiprocessing.cpu_count()
lowerCamelCase__						=				AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
lowerCamelCase__						=				set(ds.unique("hash"))
lowerCamelCase__						=				len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
lowerCamelCase__						=				time.time()
lowerCamelCase__						=				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__						=				time.time()
		lowerCamelCase__						=				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__						=				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__						=				output_dir / "data"
data_dir.mkdir(exist_ok=True)

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






"""simple docstring"""




from __future__ import annotations

def 						A							(  snake_case__     ):




			'''simple docstring'''


			for i in range(1		,					len(matrix[0]     )     ):
						matrix[0][i] += matrix[0][i - 1]

			# preprocessing the first column
			for i in range(1		,					len(snake_case__     )     ):
						matrix[i][0] += matrix[i - 1][0]

			# updating the path cost for current position
			for i in range(1		,					len(snake_case__     )     ):
						for j in range(1		,					len(matrix[0]     )     ):
									matrix[i][j] += min(matrix[i - 1][j]		,					matrix[i][j - 1]     )

			return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()


