



"""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


a					:	Any							=		re.compile(r'''\s+''')


def  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A=5      )    ->  List[Any]:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A=5					,      _A=0.05      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   ["""unit tests""", """test file""", """configuration file"""]
    _UpperCAmelCase          =   example["""content"""].splitlines()
    _UpperCAmelCase          =   0
    _UpperCAmelCase          =   0
    # first test
    for _, line in zip(range(lowerCAmelCase__      )					,      lowerCAmelCase__      ):
        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  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    _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  _UpperCamelCase	(						_A					,      _A=4      )    ->  Dict:





    """simple docstring"""

    _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  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

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


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





    """simple docstring"""

    _UpperCAmelCase          =   {}
    results.update(get_hash(lowerCAmelCase__      )      )
    results.update(line_stats(lowerCAmelCase__      )      )
    results.update(alpha_stats(lowerCAmelCase__      )      )
    results.update(char_token_ratio(lowerCAmelCase__      )      )
    results.update(is_autogenerated(lowerCAmelCase__      )      )
    results.update(is_config_or_test(lowerCAmelCase__      )      )
    results.update(has_no_keywords(lowerCAmelCase__      )      )
    results.update(has_few_assignments(lowerCAmelCase__      )      )
    return results


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  str:





    """simple docstring"""

    if not check_uniques(lowerCAmelCase__					,      lowerCAmelCase__      ):
        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	(						_A      )    ->  List[str]:





    """simple docstring"""

    with open(lowerCAmelCase__					,      """rb"""      ) as f_in:
        with gzip.open(str(lowerCAmelCase__      ) + """.gz"""					,      """wb"""					,      compresslevel=6      ) as f_out:
            shutil.copyfileobj(lowerCAmelCase__					,      lowerCAmelCase__      )
    os.unlink(lowerCAmelCase__      )


# Settings
a					:	Optional[int]							=		HfArgumentParser(PreprocessingArguments)
a					:	Optional[Any]							=		parser.parse_args()
if args.num_workers is None:
 a					:	Dict							=		multiprocessing.cpu_count()
a					:	Tuple							=		AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
a					:	List[Any]							=		time.time()
a					:	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:
 a					:	List[Any]							=		time.time()
 a					:	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
a					:	Optional[Any]							=		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					:	int							=		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					:	Dict							=		str(data_dir / F"file-{file_number+1:012}.json")
 a					:	Optional[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}")

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)