"""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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=5				):






							"""simple docstring"""





							A__	     =							["auto-generated", "autogenerated", "automatically generated"]
							A__	     =							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=5       ,    UpperCamelCase__=0.0_5				):






							"""simple docstring"""





							A__	     =							["unit tests", "test file", "configuration file"]
							A__	     =							example["content"].splitlines()
							A__	     =							0
							A__	     =							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
							A__	     =							example["content"].count('\n'				)
							A__	     =							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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							["def ", "class ", "for ", "while "]
							A__	     =							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=4				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							{}
							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""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   UpperCAmelCase					(	UpperCamelCase__				):






							"""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"""




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__lowerCamelCase											=  "."


if __name__ == "__main__":
				__lowerCamelCase											=  os.path.join(REPO_PATH, "utils/documentation_tests.txt")
				__lowerCamelCase											=  []
				__lowerCamelCase											=  []
				with open(doctest_file_path) as fp:
								for line in fp:
												__lowerCamelCase											=  line.strip()
												__lowerCamelCase											=  os.path.join(REPO_PATH, line)
												if not (os.path.isfile(path) or os.path.isdir(path)):
																non_existent_paths.append(line)
												all_paths.append(path)
				if len(non_existent_paths) > 0:
								__lowerCamelCase											=  "\n".join(non_existent_paths)
								raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
				if all_paths != sorted(all_paths):
								raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")





