"""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


lowerCAmelCase_     =	re.compile(R'''\s+''')



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    np.mean([c.isalnum() for c in example["""content"""]]  )
							return {"alpha_frac": alpha_frac}



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							if example["hash"] in uniques:
														uniques.remove(example["""hash"""]  )
														return True
							else:
														return False



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    tokenizer(example["""content"""]     ,	truncation=__SCREAMING_SNAKE_CASE  )["""input_ids"""]
							_SCREAMING_SNAKE_CASE		:						int			  =    len(example["""content"""]  ) / len(__SCREAMING_SNAKE_CASE  )
							return {"ratio": ratio}



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						List[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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							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
lowerCAmelCase_     =	HfArgumentParser(PreprocessingArguments)
lowerCAmelCase_     =	parser.parse_args()
if args.num_workers is None:
			lowerCAmelCase_     =	multiprocessing.cpu_count()
lowerCAmelCase_     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowerCAmelCase_     =	time.time()
lowerCAmelCase_     =	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_     =	time.time()
			lowerCAmelCase_ ,					lowerCAmelCase_     =	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_     =	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_     =	output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["image_processor", "tokenizer"]
				a			=						"ChineseCLIPImageProcessor"
				a			=						("BertTokenizer", "BertTokenizerFast")





				def __init__(      self							:							Dict      ,    _A							:							Tuple=None      ,    _A							:							List[Any]=None      ,    **_A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""      ,    _A      ,    )
																		_SCREAMING_SNAKE_CASE		:						str			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						int			  =    image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError("""You need to specify an `image_processor`.""")
											if tokenizer is None:
																		raise ValueError("""You need to specify a `tokenizer`.""")

											super().__init__(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.image_processor





				def __call__(      self							:							Optional[int]      ,    _A							:							Optional[Any]=None      ,    _A							:							Any=None      ,    _A							:							Tuple=None      ,    **_A							:							int):



											"""simple docstring"""







											if text is None and images is None:
																		raise ValueError("""You have to specify either text or images. Both cannot be none.""")

											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(_A      ,    return_tensors=_A      ,    **_A)

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processor(_A      ,    return_tensors=_A      ,    **_A)

											if text is not None and images is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_A)      ,    tensor_type=_A)





				def 		_lowerCAmelCase		(      self							:							str      ,    *_A							:							Any      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.batch_decode(*_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    *_A							:							List[Any]      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.decode(*_A      ,    **_A)





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





				@property
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,    _A      ,    )
											return self.image_processor_class


