






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:     Optional[Any]   					=					re.compile(R'''\s+''')




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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       _UpperCAmelCase							=    [len(__snake_case ) for line in example["""content"""].splitlines()]
       return {"line_mean": np.mean(__snake_case ), "line_max": max(__snake_case )}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       _UpperCAmelCase							=    np.mean([c.isalnum() for c in example["""content"""]] )
       return {"alpha_frac": alpha_frac}




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       if example["hash"] in uniques:
              uniques.remove(example["""hash"""] )
              return True
       else:
              return False




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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    tokenizer(example["""content"""]   ,      truncation=__snake_case )["""input_ids"""]
       _UpperCAmelCase							=    len(example["""content"""] ) / len(__snake_case )
       return {"ratio": ratio}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    {}
       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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						int:
       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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       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
__a:     Optional[int]   					=					HfArgumentParser(PreprocessingArguments)
__a:     List[Any]   					=					parser.parse_args()
if args.num_workers is None:
  __a:     Tuple   					=					multiprocessing.cpu_count()
__a:     Tuple   					=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
__a:     Dict   					=					time.time()
__a:     str   					=					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:     Tuple   					=					len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

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

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







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__a:     List[Any]   					=					(720, 1280)  # Height, Width
__a:     str   					=					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__a:     Optional[Any]   					=					1 / 100
__a:     Optional[Any]   					=					''''''
__a:     Tuple   					=					''''''
__a:     List[Any]   					=					''''''
__a:     Union[str, Any]   					=					250




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataset(__snake_case   ,      __snake_case )
       for index in range(__snake_case ):
              _UpperCAmelCase							=    random.sample(range(len(__snake_case ) )   ,      4 )
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    update_image_and_anno(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      filter_scale=__snake_case   ,      )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _UpperCAmelCase							=    random_chars(3_2 )
              _UpperCAmelCase							=    path.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              _UpperCAmelCase							=    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""   ,      __snake_case   ,      [cva.IMWRITE_JPEG_QUALITY, 8_5] )
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
              _UpperCAmelCase							=    []
              for anno in new_annos:
                     _UpperCAmelCase							=    anno[3] - anno[1]
                     _UpperCAmelCase							=    anno[4] - anno[2]
                     _UpperCAmelCase							=    anno[1] + width / 2
                     _UpperCAmelCase							=    anno[2] + height / 2
                     _UpperCAmelCase							=    f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(__snake_case )
              with open(f"""{file_root}.txt"""   ,      """w""" ) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						tuple[list, list]:
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for label_file in glob.glob(os.path.join(__snake_case   ,      """*.txt""" ) ):
              _UpperCAmelCase							=    label_file.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              with open(__snake_case ) as in_file:
                     _UpperCAmelCase							=    in_file.readlines()
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      f"""{label_name}.jpg""" )

              _UpperCAmelCase							=    []
              for obj_list in obj_lists:
                     _UpperCAmelCase							=    obj_list.rstrip("""\n""" ).split(""" """ )
                     _UpperCAmelCase							=    float(obj[1] ) - float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) - float(obj[4] ) / 2
                     _UpperCAmelCase							=    float(obj[1] ) + float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) + float(obj[4] ) / 2

                     boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
              if not boxes:
                     continue
              img_paths.append(__snake_case )
              labels.append(__snake_case )
       return img_paths, labels




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = 0.0   ,      )				->						tuple[list, list, str]:
       _UpperCAmelCase							=    np.zeros([output_size[0], output_size[1], 3]   ,      dtype=np.uinta )
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    int(scale_x * output_size[1] )
       _UpperCAmelCase							=    int(scale_y * output_size[0] )

       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for i, index in enumerate(__snake_case ):
              _UpperCAmelCase							=    all_img_list[index]
              path_list.append(__snake_case )
              _UpperCAmelCase							=    all_annos[index]
              _UpperCAmelCase							=    cva.imread(__snake_case )
              if i == 0:  # top-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 1:  # top-right
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (output_size[1] - divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 2:  # bottom-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              else:  # bottom-right
                     _UpperCAmelCase							=    cva.resize(
                         __snake_case   ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              _UpperCAmelCase							=    [
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       assert number_char > 1, "The number of character should greater than 1"
       _UpperCAmelCase							=    ascii_lowercase + digits
       return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )


if __name__ == "__main__":
  main()
  print('''DONE ✅''')