





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__						:			Optional[int]									=re.compile(r'''\s+''')





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				return {"hash": hashlib.mda(re.sub(_UpperCAmelCase			,      """"""			,      example["""content"""]				).encode("""utf-8"""				)				).hexdigest()}





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[int]:
				lowerCamelCase	 =[len(_UpperCAmelCase				) for line in example["""content"""].splitlines()]
				return {"line_mean": np.mean(_UpperCAmelCase				), "line_max": max(_UpperCAmelCase				)}





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =np.mean([c.isalnum() for c in example["""content"""]]				)
				return {"alpha_frac": alpha_frac}





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, Any]:
				if example["hash"] in uniques:
								uniques.remove(example["""hash"""]				)
								return True
				else:
								return False





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=5				)						->  Optional[Any]:
				lowerCamelCase	 =["""auto-generated""", """autogenerated""", """automatically generated"""]
				lowerCamelCase	 =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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=5			,      _UpperCAmelCase=0.0_5				)						->  Union[str, Any]:
				lowerCamelCase	 =["""unit tests""", """test file""", """configuration file"""]
				lowerCamelCase	 =example["""content"""].splitlines()
				lowerCamelCase	 =0
				lowerCamelCase	 =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
				lowerCamelCase	 =example["""content"""].count("""\n"""				)
				lowerCamelCase	 =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      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =["""def """, """class """, """for """, """while """]
				lowerCamelCase	 =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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=4				)						->  int:
				lowerCamelCase	 =example["""content"""].splitlines()
				lowerCamelCase	 =0
				for line in lines:
								counter += line.lower().count("""="""				)
								if counter > minimum:
												return {"has_few_assignments": False}
				return {"has_few_assignments": True}





def      _lowercase					(						_UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =tokenizer(example["""content"""]			,      truncation=_UpperCAmelCase				)["""input_ids"""]
				lowerCamelCase	 =len(example["""content"""]				) / len(_UpperCAmelCase				)
				return {"ratio": ratio}





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				lowerCamelCase	 ={}
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				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      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				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__						:			Optional[int]									=HfArgumentParser(PreprocessingArguments)
UpperCAmelCase__						:			Optional[int]									=parser.parse_args()
if args.num_workers is None:
							UpperCAmelCase__						:			int									=multiprocessing.cpu_count()
UpperCAmelCase__						:			Any									=AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
UpperCAmelCase__						:			str									=time.time()
UpperCAmelCase__						:			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:
							UpperCAmelCase__						:			List[str]									=time.time()
							UpperCAmelCase__						:			Union[str, 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
UpperCAmelCase__						:			Tuple									=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__						:			Optional[int]									=output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

UpperCAmelCase__						:			Optional[Any]									=time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
							UpperCAmelCase__						:			Union[str, Any]									=str(data_dir / F"file-{file_number+1:012}.json")
							UpperCAmelCase__						:			Tuple									=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}")












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





