





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       :     Optional[Any]   =     re.compile(r"""\s+""")



def 		A_				(     _lowerCAmelCase     )						-> Optional[int]:
 return {"hash": hashlib.mda(re.sub(_a							,   ""							,   example["content"]     ).encode("utf-8"     )     ).hexdigest()}



def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Dict       	=   [len(_a     ) for line in example["content"].splitlines()]
 return {"line_mean": np.mean(_a     ), "line_max": max(_a     )}



def 		A_				(     _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					Union[str, Any]       	=   np.mean([c.isalnum() for c in example["content"]]     )
 return {"alpha_frac": alpha_frac}



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 if example["hash"] in uniques:
  uniques.remove(example["hash"]     )
  return True
 else:
  return False



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



def 		A_				(     _lowerCAmelCase     )						-> Any:
 UpperCamelCase	:					Optional[int]       	=   tokenizer(example["content"]							,   truncation=_a     )["input_ids"]
 UpperCamelCase	:					int       	=   len(example["content"]     ) / len(_a     )
 return {"ratio": ratio}



def 		A_				(     _lowerCAmelCase     )						-> Dict:
 UpperCamelCase	:					int       	=   {}
 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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 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 		A_				(     _lowerCAmelCase     )						-> List[Any]:
 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       :     List[str]   =     HfArgumentParser(PreprocessingArguments)
__lowerCamelCase       :     Any   =     parser.parse_args()
if args.num_workers is None:
    __lowerCamelCase       :     Dict   =     multiprocessing.cpu_count()
__lowerCamelCase       :     Optional[int]   =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__lowerCamelCase       :     str   =     time.time()
__lowerCamelCase       :     Optional[int]   =     load_dataset(args.dataset_name, split="""train""")
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")

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

# Deduplicate hashes
__lowerCamelCase       :     str   =     set(ds.unique("""hash"""))
__lowerCamelCase       :     Union[str, Any]   =     len(uniques) / len(ds)
print(f"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
__lowerCamelCase       :     Union[str, Any]   =     time.time()
__lowerCamelCase       :     Dict   =     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       :     Any   =     time.time()
    __lowerCamelCase  ,	__lowerCamelCase       :     Optional[Any]   =     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       :     Dict   =     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       :     Optional[Any]   =     output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__lowerCamelCase       :     Tuple   =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    __lowerCamelCase       :     Optional[int]   =     str(data_dir / f"""file-{file_number+1:012}.json""")
    __lowerCamelCase       :     List[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
from bsa import BeautifulSoup

if __name__ == "__main__":
   A     =input('Enter image url: ').strip()
   print(f"""Downloading image from {url} ...""")
   A     =BeautifulSoup(requests.get(url).content, 'html.parser')
   # The image URL is in the content field of the first meta tag with property og:image
   A     =soup.find('meta', {'property': 'og:image'})['content']
   A     =requests.get(image_url).content
   A     =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, 'wb') as fp:
      fp.write(image_data)
   print(f"""Done. Image saved to disk as {file_name}.""")




