

"""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


a		=re.compile(r"""\s+""")






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	return {"hash": hashlib.mda(re.sub(__lowerCAmelCase     ,			''     ,			example['content']      ).encode('utf-8'      )      ).hexdigest()}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    Optional[int] 	=			[len(__lowerCAmelCase      ) for line in example['''content'''].splitlines()]
	return {"line_mean": np.mean(__lowerCAmelCase      ), "line_max": max(__lowerCAmelCase      )}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    Union[str, Any] 	=			np.mean([c.isalnum() for c in example['content']]      )
	return {"alpha_frac": alpha_frac}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:





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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=5      )  ->					int:
	__lowerCamelCase    :    Optional[int] 	=			['''auto-generated''', '''autogenerated''', '''automatically generated''']
	__lowerCamelCase    :    Dict 	=			example['''content'''].splitlines()
	for _, line in zip(range(__lowerCAmelCase      )     ,			__lowerCAmelCase      ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"autogenerated": True}
	else:
		return {"autogenerated": False}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=5     ,			lowerCamelCase__=0.05      )  ->					Optional[int]:
	__lowerCamelCase    :    Tuple 	=			['''unit tests''', '''test file''', '''configuration file''']
	__lowerCamelCase    :    str 	=			example['''content'''].splitlines()
	__lowerCamelCase    :    Any 	=			0
	__lowerCamelCase    :    Dict 	=			0
	# first test
	for _, line in zip(range(__lowerCAmelCase      )     ,			__lowerCAmelCase      ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	__lowerCamelCase    :    Tuple 	=			example['''content'''].count('\n'      )
	__lowerCamelCase    :    str 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    Optional[int] 	=			['''def ''', '''class ''', '''for ''', '''while ''']
	__lowerCamelCase    :    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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=4      )  ->					str:
	__lowerCamelCase    :    List[str] 	=			example['''content'''].splitlines()
	__lowerCamelCase    :    Any 	=			0
	for line in lines:
		counter += line.lower().count('='      )
		if counter > minimum:
			return {"has_few_assignments": False}
	return {"has_few_assignments": True}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Any:
	__lowerCamelCase    :    List[str] 	=			tokenizer(example['content']     ,			truncation=__lowerCAmelCase      )['''input_ids''']
	__lowerCamelCase    :    Union[str, Any] 	=			len(example['content']      ) / len(__lowerCAmelCase      )
	return {"ratio": ratio}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Any 	=			{}
	results.update(get_hash(__lowerCAmelCase      )      )
	results.update(line_stats(__lowerCAmelCase      )      )
	results.update(alpha_stats(__lowerCAmelCase      )      )
	results.update(char_token_ratio(__lowerCAmelCase      )      )
	results.update(is_autogenerated(__lowerCAmelCase      )      )
	results.update(is_config_or_test(__lowerCAmelCase      )      )
	results.update(has_no_keywords(__lowerCAmelCase      )      )
	results.update(has_few_assignments(__lowerCAmelCase      )      )
	return results






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:





	if not check_uniques(__lowerCAmelCase     ,			__lowerCAmelCase      ):
		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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:





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


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

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

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

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

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

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







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


a		=open  # noqa: we just need to have a builtin inside this module to test it properly




