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






def 							_lowercase							(     UpperCamelCase_		)		->		str:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		Tuple:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_=5		)		->		Optional[int]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_=5	,	UpperCamelCase_=0.05		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= ['unit tests', 'test file', 'configuration file']
					SCREAMING_SNAKE_CASE__													= example['content'].splitlines()
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0
					# first test
					for _, line in zip(range(UpperCamelCase_		)	,	UpperCamelCase_		):
										for keyword in keywords:
															if keyword in line.lower():
																				return {"config_or_test": True}
    # second test
					SCREAMING_SNAKE_CASE__													= example['content'].count('\n'		)
					SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= ['def ', 'class ', 'for ', 'while ']
					SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_=4		)		->		Optional[Any]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= tokenizer(example['content']	,	truncation=UpperCamelCase_		)['input_ids']
					SCREAMING_SNAKE_CASE__													= len(example['content']		) / len(UpperCamelCase_		)
					return {"ratio": ratio}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= {}
					results.update(get_hash(UpperCamelCase_		)		)
					results.update(line_stats(UpperCamelCase_		)		)
					results.update(alpha_stats(UpperCamelCase_		)		)
					results.update(char_token_ratio(UpperCamelCase_		)		)
					results.update(is_autogenerated(UpperCamelCase_		)		)
					results.update(is_config_or_test(UpperCamelCase_		)		)
					results.update(has_no_keywords(UpperCamelCase_		)		)
					results.update(has_few_assignments(UpperCamelCase_		)		)
					return results






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Tuple:
					'''simple docstring'''


					if not check_uniques(UpperCamelCase_	,	UpperCamelCase_		):
										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 							_lowercase							(     UpperCamelCase_		)		->		str:
					'''simple docstring'''


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


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

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

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

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

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

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







import os
from math import logaa






def 							_lowercase							(     UpperCamelCase_ = "base_exp.txt"		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0
					for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase_		)	,	UpperCamelCase_		)		)		):
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= list(map(UpperCamelCase_	,	line.split(','		)		)		)
										if x * logaa(UpperCamelCase_		) > largest:
															SCREAMING_SNAKE_CASE__													= x * logaa(UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= i + 1
					return result


if __name__ == "__main__":
				print(solution())





