






"""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_       			=   re.compile(r'''\s+''')

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

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

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

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

def        snake_case   (     A__			,A__=5		):
			UpperCAmelCase_  :				Dict      =      ["auto-generated", "autogenerated", "automatically generated"]
			UpperCAmelCase_  :				int      =      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        snake_case   (     A__			,A__=5			,A__=0.05		):
			UpperCAmelCase_  :				str      =      ["unit tests", "test file", "configuration file"]
			UpperCAmelCase_  :				Any      =      example["content"].splitlines()
			UpperCAmelCase_  :				Dict      =      0
			UpperCAmelCase_  :				List[str]      =      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_  :				Optional[int]      =      example["content"].count("\n"		)
			UpperCAmelCase_  :				Optional[Any]      =      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        snake_case   (     A__		):
			UpperCAmelCase_  :				int      =      ["def ", "class ", "for ", "while "]
			UpperCAmelCase_  :				List[str]      =      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        snake_case   (     A__			,A__=4		):
			UpperCAmelCase_  :				str      =      example["content"].splitlines()
			UpperCAmelCase_  :				Any      =      0
			for line in lines:
						counter += line.lower().count("="		)
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      tokenizer(example["content"]			,truncation=A__		)["input_ids"]
			UpperCAmelCase_  :				Optional[int]      =      len(example["content"]		) / len(A__		)
			return {"ratio": ratio}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Any      =      {}
			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        snake_case   (     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        snake_case   (     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
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_			,	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"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class     UpperCamelCase_ :
	__magic_name__			   =       None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				UpperCAmelCase_  :				Tuple      =      self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase_  :				Union[str, Any]      =      json.loads(feat_extract.to_json_string()		)
				for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				Dict      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				List[Any]      =      os.path.join(lowerCAmelCase_			,			"feat_extract.json"		)
							feat_extract_first.to_json_file(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_json_file(lowerCAmelCase_		)

				self.assertEqual(feat_extract_second.to_dict()			,			feat_extract_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Dict:
				UpperCAmelCase_  :				Optional[Any]      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				Optional[Any]      =      feat_extract_first.save_pretrained(lowerCAmelCase_		)[0]
							check_json_file_has_correct_format(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_pretrained(lowerCAmelCase_		)

				self.assertEqual(feat_extract_second.to_dict()			,			feat_extract_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.feature_extraction_class()
				self.assertIsNotNone(lowerCAmelCase_		)



