



"""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


snake_case_				     =       re.compile(R"""\s+""")

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

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

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

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

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

def  _lowerCAmelCase      (   lowercase_		,	lowercase_=5		,	lowercase_=0.0_5						):
						UpperCAmelCase             = ['unit tests', 'test file', 'configuration file']
						UpperCAmelCase             = example['content'].splitlines()
						UpperCAmelCase             = 0
						UpperCAmelCase             = 0
						# first test
						for _, line in zip(range(lowercase_						)		,	lowercase_						):
												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  _lowerCAmelCase      (   lowercase_						):
						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  _lowerCAmelCase      (   lowercase_		,	lowercase_=4						):
						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  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = tokenizer(example['content']		,	truncation=lowercase_						)['input_ids']
						UpperCAmelCase             = len(example['content']						) / len(lowercase_						)
						return {"ratio": ratio}

def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = {}
						results.update(get_hash(lowercase_						)						)
						results.update(line_stats(lowercase_						)						)
						results.update(alpha_stats(lowercase_						)						)
						results.update(char_token_ratio(lowercase_						)						)
						results.update(is_autogenerated(lowercase_						)						)
						results.update(is_config_or_test(lowercase_						)						)
						results.update(has_no_keywords(lowercase_						)						)
						results.update(has_few_assignments(lowercase_						)						)
						return results

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						if not check_uniques(lowercase_		,	lowercase_						):
												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      (   lowercase_						):
						with open(lowercase_		,	'rb'						) as f_in:
												with gzip.open(str(lowercase_						) + '.gz'		,	'wb'		,	compresslevel=6						) as f_out:
																		shutil.copyfileobj(lowercase_		,	lowercase_						)
						os.unlink(lowercase_						)


# Settings
snake_case_				     =       HfArgumentParser(PreprocessingArguments)
snake_case_				     =       parser.parse_args()
if args.num_workers is None:
				snake_case_				     =       multiprocessing.cpu_count()
snake_case_				     =       AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		JukeboxTokenizer
 lowerCamelCase	:str    			=		{
     '''artist''': '''Zac Brown Band''',
     '''genres''': '''Country''',
     '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
 }



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        import torch

        _A        =  JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""							)
        _A        =  tokenizer(**self.metas							)["""input_ids"""]
        # fmt: off
        _A        =  [
            torch.tensor([[
                0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
                76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                76, 76]]							),
            torch.tensor([[0, 0, 0, 10_69, 11]]							),
            torch.tensor([[0, 0, 0, 10_69, 11]]							),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0]		,				EXPECTED_OUTPUT[0]							)							)
        self.assertTrue(torch.allclose(tokens[1]		,				EXPECTED_OUTPUT[1]							)							)
        self.assertTrue(torch.allclose(tokens[2]		,				EXPECTED_OUTPUT[2]							)							)



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        import torch

        _A        =  JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""							)
        _A        =  tokenizer(**self.metas							)["""input_ids"""]
        # fmt: off
        _A        =  [
            torch.tensor([[
                0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
                31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                77, 77, 77, 77, 77, 77]]							),
            torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]							),
            torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]							),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0]		,				EXPECTED_OUTPUT[0]							)							)
        self.assertTrue(torch.allclose(tokens[1]		,				EXPECTED_OUTPUT[1]							)							)
        self.assertTrue(torch.allclose(tokens[2]		,				EXPECTED_OUTPUT[2]							)							)




