



from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


UpperCAmelCase__     = logging.get_logger(__name__)


if is_torch_available():
							import torch
							from torch import nn
							from torch.utils.data import Dataset

							from transformers import Trainer







							class     __lowerCAmelCase					(    A		):



										def __init__(		self     :						int				,	A     :						int = 1_01)     ->					Optional[int]:



													"""simple docstring"""






													_UpperCAmelCase	 =     length



										def __len__(		self     :						Union[str, Any])     ->					List[str]:



													"""simple docstring"""






													return self.length



										def __getitem__(		self     :						Tuple				,	A     :						Union[str, Any])     ->					int:



													"""simple docstring"""






													return i







							class     __lowerCAmelCase					:



										def __call__(		self     :						Optional[Any]				,	A     :						str)     ->					Union[str, Any]:



													"""simple docstring"""






													return {"input_ids": torch.tensor(A), "labels": torch.tensor(A)}







							class     __lowerCAmelCase					(    nn.Module		):



										def __init__(		self     :						int)     ->					Tuple:



													"""simple docstring"""






													super().__init__()
													# Add some (unused) params otherwise DDP will complain.
													_UpperCAmelCase	 =     nn.Linear(1_20				,	80)



										def 					_lowerCamelCase (		self     :						int				,	A     :						str				,	A     :						Tuple=None)     ->					List[str]:



													"""simple docstring"""






													if labels is not None:
																return torch.tensor(0.0				,	device=input_ids.device), input_ids
													else:
																return input_ids







class     __lowerCAmelCase					(    A		):



			@require_torch_neuroncore
			def 					_lowerCamelCase (		self     :						List[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
						_UpperCAmelCase	 =     self.get_auto_remove_tmp_dir()
						_UpperCAmelCase	 =     F"--output_dir {output_dir}".split()
						_UpperCAmelCase	 =     ['torchrun'] + distributed_args + args
						execute_subprocess_async(A				,	env=self.get_env())
						# successful return here == success - any errors would have caused an error in the sub-call







class     __lowerCAmelCase					(    A		):



			@require_torch_multi_gpu
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     F"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
						_UpperCAmelCase	 =     self.get_auto_remove_tmp_dir()
						_UpperCAmelCase	 =     F"--output_dir {output_dir}".split()
						_UpperCAmelCase	 =     ['torchrun'] + distributed_args + args
						execute_subprocess_async(A				,	env=self.get_env())
						# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
							# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
							#
							# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

							UpperCAmelCase__     = HfArgumentParser((TrainingArguments,))
							UpperCAmelCase__     = parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
							    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
							)

							# Essentially, what we want to verify in the distributed case is that we get all samples back,
							# in the right order. (this is crucial for prediction for instance)
							for dataset_length in [101, 40, 7]:
														UpperCAmelCase__     = DummyDataset(dataset_length)





														def 							A			(  _UpperCAmelCase       :     EvalPrediction					)       ->				Dict:







																	'''simple docstring'''
																	_UpperCAmelCase	 =     list(range(len(_UpperCAmelCase					)					)					)
																	_UpperCAmelCase	 =     p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																	if not success and training_args.local_rank == 0:
																				logger.warning(
																				    'Predictions and/or labels do not match expected results:\n  - predictions: '
																				    F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"					)
																	return {"success": success}

														UpperCAmelCase__     = Trainer(
														    model=DummyModel(),
														    args=training_args,
														    data_collator=DummyDataCollator(),
														    eval_dataset=dataset,
														    compute_metrics=compute_metrics,
														)
														UpperCAmelCase__     = trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

														UpperCAmelCase__     = trainer.predict(dataset)
														logger.info(p.metrics)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics)
																					exit(1)

														UpperCAmelCase__     = 2

														UpperCAmelCase__     = trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

														UpperCAmelCase__     = trainer.predict(dataset)
														logger.info(p.metrics)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics)
																					exit(1)

														UpperCAmelCase__     = None





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


UpperCAmelCase__     = re.compile(r"\s+")





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				str:







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





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







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





def 							A			(  _UpperCAmelCase       :     Any					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     List[Any]					)       ->				Dict:







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





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=5					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     ['auto-generated', 'autogenerated', 'automatically generated']
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Tuple=5							,     _UpperCAmelCase       :     Optional[int]=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(_UpperCAmelCase					)							,     _UpperCAmelCase					):
						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 							A			(  _UpperCAmelCase       :     Any					)       ->				Tuple:







			'''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 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[Any]=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 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				str:







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





def 							A			(  _UpperCAmelCase       :     Dict					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			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 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				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 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Any:







			'''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
UpperCAmelCase__     = HfArgumentParser(PreprocessingArguments)
UpperCAmelCase__     = parser.parse_args()
if args.num_workers is None:
							UpperCAmelCase__     = multiprocessing.cpu_count()
UpperCAmelCase__     = AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
UpperCAmelCase__     = time.time()
UpperCAmelCase__     = 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:
							UpperCAmelCase__     = time.time()
							UpperCAmelCase__     ,						UpperCAmelCase__     = 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
UpperCAmelCase__     = 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)

UpperCAmelCase__     = output_dir / "data"
data_dir.mkdir(exist_ok=True)

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