


"""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__    :							Union[str, Any]            =					re.compile(r'''\s+''')




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=5       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    ["auto-generated", "autogenerated", "automatically generated"]
					__SCREAMING_SNAKE_CASE						       =    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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=5  ,						lowerCAmelCase_=0.05       ):




					'''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(lowerCAmelCase_       )  ,						lowerCAmelCase_       ):
										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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=4       ):




					'''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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    tokenizer(example["content"]  ,						truncation=lowerCAmelCase_       )["input_ids"]
					__SCREAMING_SNAKE_CASE						       =    len(example["content"]       ) / len(lowerCAmelCase_       )
					return {"ratio": ratio}




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    {}
					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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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

# Load dataset
a__    :							Union[str, Any]            =					time.time()
a__    :							Tuple            =					load_dataset(args.dataset_name, split='''train''')
print(F"Time to load dataset: {time.time()-t_start:.2f}")

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

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

# Deduplicate data and apply heuristics
a__    :							Optional[int]            =					time.time()
a__    :							str            =					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__    :							Optional[Any]            =					time.time()
		a__      ,					a__    :							str            =					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__    :							List[Any]            =					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__    :							List[str]            =					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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




"""simple docstring"""







from datetime import datetime

import requests




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
					__SCREAMING_SNAKE_CASE						       =    requests.get(base_url + url       ).json()[0]["urls"][0]["src"]
					return requests.get(lowerCAmelCase_       ).content


if __name__ == "__main__":
		a__    :							str            =					input('''Enter Video/IGTV url: ''').strip()
		a__    :							List[Any]            =					F"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
		with open(file_name, '''wb''') as fp:
				fp.write(download_video(url))
		print(F"Done. Video saved to disk as {file_name}.")
