

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


_UpperCAmelCase	:     Optional[int]		       = re.compile(R"""\s+""")





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=5   ):







							'''simple docstring'''






							snake_case_           =					['auto-generated', 'autogenerated', 'automatically generated']
							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=5     ,							UpperCamelCase__=0.05   ):







							'''simple docstring'''






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







							'''simple docstring'''






							snake_case_           =					['def ', 'class ', 'for ', 'while ']
							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=4   ):







							'''simple docstring'''






							snake_case_           =					example['content'].splitlines()
							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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''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
_UpperCAmelCase	:     Optional[Any]		       = HfArgumentParser(PreprocessingArguments)
_UpperCAmelCase	:     List[Any]		       = parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase	:     int		       = multiprocessing.cpu_count()
_UpperCAmelCase	:     Optional[int]		       = AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
_UpperCAmelCase	:     Optional[int]		       = set(ds.unique("""hash"""))
_UpperCAmelCase	:     Tuple		       = len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
_UpperCAmelCase	:     List[Any]		       = time.time()
_UpperCAmelCase	:     Optional[int]		       = 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:
					_UpperCAmelCase	:     str		       = time.time()
					_UpperCAmelCase	:     int		       = 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
_UpperCAmelCase	:     Tuple		       = 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)

_UpperCAmelCase	:     str		       = output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_UpperCAmelCase	:     Optional[int]		       = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_UpperCAmelCase	:     Dict		       = [file for file in filepaths if file != file.lower()]
if upper_files:
					print(F'''{len(upper_files)} files contain uppercase characters:''')
					print("""\n""".join(upper_files) + """\n""")

_UpperCAmelCase	:     Tuple		       = [file for file in filepaths if """ """ in file]
if space_files:
					print(F'''{len(space_files)} files contain space characters:''')
					print("""\n""".join(space_files) + """\n""")

_UpperCAmelCase	:     Union[str, Any]		       = [file for file in filepaths if """-""" in file]
if hyphen_files:
					print(F'''{len(hyphen_files)} files contain hyphen characters:''')
					print("""\n""".join(hyphen_files) + """\n""")

_UpperCAmelCase	:     List[Any]		       = [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F'''{len(nodir_files)} files are not in a directory:''')
					print("""\n""".join(nodir_files) + """\n""")

_UpperCAmelCase	:     List[str]		       = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)




