






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


__snake_case    :    Dict         =     re.compile(R"""\s+""")







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]		)  ->   str:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]		)  ->   Optional[int]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   Union[str, Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Union[str, Any]		)  ->   str:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Union[str, Any]=5		)  ->   Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						['auto-generated', 'autogenerated', 'automatically generated']
						lowerCAmelCase__    						=						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 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					List[Any]=5    ,       UpperCamelCase_       :					int=0.05		)  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						['unit tests', 'test file', 'configuration file']
						lowerCAmelCase__    						=						example['content'].splitlines()
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						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
						lowerCAmelCase__    						=						example['content'].count('\n'		)
						lowerCAmelCase__    						=						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_       :					int		)  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						['def ', 'class ', 'for ', 'while ']
						lowerCAmelCase__    						=						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_       :					Dict    ,       UpperCamelCase_       :					Any=4		)  ->   str:

						"""simple docstring"""



						lowerCAmelCase__    						=						example['content'].splitlines()
						lowerCAmelCase__    						=						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_       :					Tuple		)  ->   Dict:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any		)  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						{}
						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 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Optional[Any]:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]		)  ->   Dict:

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

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

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

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

# Deduplicate data and apply heuristics
__snake_case    :    int         =     time.time()
__snake_case    :    List[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:
					__snake_case    :    List[Any]         =     time.time()
					__snake_case     , __snake_case    :    List[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
__snake_case    :    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)

__snake_case    :    Any         =     output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__snake_case    :    List[Any]         =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
					__snake_case    :    List[Any]         =     str(data_dir / f'file-{file_number+1:012}.json')
					__snake_case    :    List[Any]         =     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
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(F"{file_root}.jpg"    ,       UpperCamelCase_    ,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
												print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						F"{anno[0]} {x_center} {y_center} {width} {height}"
																		annos_list.append(UpperCamelCase_		)
												with open(F"{file_root}.txt"    ,       'w'		) as outfile:
																		outfile.write('\n'.join(line for line in annos_list		)		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

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

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

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

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


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





