





"""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


__magic_name__       :					int						=			re.compile(R"""\s+""")

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			[len(SCREAMING_SNAKE_CASE     ) for line in example["""content"""].splitlines()]
						return {"line_mean": np.mean(SCREAMING_SNAKE_CASE     ), "line_max": max(SCREAMING_SNAKE_CASE     )}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			np.mean([c.isalnum() for c in example["""content"""]]     )
						return {"alpha_frac": alpha_frac}

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=5     ):
						UpperCamelCase				:		List[str]				   =			["""auto-generated""", """autogenerated""", """automatically generated"""]
						UpperCamelCase				:		str				   =			example["""content"""].splitlines()
						for _, line in zip(range(SCREAMING_SNAKE_CASE     )							,     SCREAMING_SNAKE_CASE     ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"autogenerated": True}
						else:
												return {"autogenerated": False}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=5							,     SCREAMING_SNAKE_CASE=0.05     ):
						UpperCamelCase				:		List[str]				   =			["""unit tests""", """test file""", """configuration file"""]
						UpperCamelCase				:		int				   =			example["""content"""].splitlines()
						UpperCamelCase				:		Optional[Any]				   =			0
						UpperCamelCase				:		int				   =			0
						# first test
						for _, line in zip(range(SCREAMING_SNAKE_CASE     )							,     SCREAMING_SNAKE_CASE     ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"config_or_test": True}
    # second test
						UpperCamelCase				:		Any				   =			example["""content"""].count("""\n"""     )
						UpperCamelCase				:		List[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      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			["""def """, """class """, """for """, """while """]
						UpperCamelCase				:		Any				   =			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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=4     ):
						UpperCamelCase				:		List[str]				   =			example["""content"""].splitlines()
						UpperCamelCase				:		Optional[Any]				   =			0
						for line in lines:
												counter += line.lower().count("""="""     )
												if counter > minimum:
																		return {"has_few_assignments": False}
						return {"has_few_assignments": True}

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		str				   =			{}
						results.update(get_hash(SCREAMING_SNAKE_CASE     )     )
						results.update(line_stats(SCREAMING_SNAKE_CASE     )     )
						results.update(alpha_stats(SCREAMING_SNAKE_CASE     )     )
						results.update(char_token_ratio(SCREAMING_SNAKE_CASE     )     )
						results.update(is_autogenerated(SCREAMING_SNAKE_CASE     )     )
						results.update(is_config_or_test(SCREAMING_SNAKE_CASE     )     )
						results.update(has_no_keywords(SCREAMING_SNAKE_CASE     )     )
						results.update(has_few_assignments(SCREAMING_SNAKE_CASE     )     )
						return results

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if not check_uniques(SCREAMING_SNAKE_CASE							,     SCREAMING_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      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """rb"""     ) as f_in:
												with gzip.open(str(SCREAMING_SNAKE_CASE     ) + """.gz"""							,     """wb"""							,     compresslevel=6     ) as f_out:
																		shutil.copyfileobj(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						os.unlink(SCREAMING_SNAKE_CASE     )


# Settings
__magic_name__       :					List[Any]						=			HfArgumentParser(PreprocessingArguments)
__magic_name__       :					Union[str, Any]						=			parser.parse_args()
if args.num_workers is None:
		__magic_name__       :					List[Any]						=			multiprocessing.cpu_count()
__magic_name__       :					Optional[Any]						=			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
__magic_name__       :					int						=			time.time()
__magic_name__       :					Union[str, 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:
		__magic_name__       :					Optional[Any]						=			time.time()
		__magic_name__						,							__magic_name__       :					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
__magic_name__       :					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)

__magic_name__       :					List[Any]						=			output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)