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     UpperCamelCase						(	__lowercase						:			Tuple     ):
			'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[Any]     ):
			'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Any     ):
			'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			List[str]     ):
			'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			List[str]=5     ):
			'''simple docstring'''




			A_		:    int         =				['auto-generated', 'autogenerated', 'automatically generated']
			A_		:    Dict         =				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     UpperCamelCase						(	__lowercase						:			List[str]						,__lowercase						:			int=5						,__lowercase						:			int=0.05     ):
			'''simple docstring'''




			A_		:    List[Any]         =				['unit tests', 'test file', 'configuration file']
			A_		:    List[Any]         =				example['content'].splitlines()
			A_		:    Any         =				0
			A_		:    Optional[Any]         =				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
			A_		:    Tuple         =				example['content'].count('\n'     )
			A_		:    List[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     UpperCamelCase						(	__lowercase						:			Tuple     ):
			'''simple docstring'''




			A_		:    Optional[int]         =				['def ', 'class ', 'for ', 'while ']
			A_		:    Dict         =				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						(	__lowercase						:			Optional[int]						,__lowercase						:			List[str]=4     ):
			'''simple docstring'''




			A_		:    int         =				example['content'].splitlines()
			A_		:    str         =				0
			for line in lines:
						counter += line.lower().count('='     )
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]     ):
			'''simple docstring'''




			A_		:    Union[str, Any]         =				tokenizer(example['content']						,truncation=__lowercase     )['input_ids']
			A_		:    Optional[int]         =				len(example['content']     ) / len(__lowercase     )
			return {"ratio": ratio}




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]     ):
			'''simple docstring'''




			A_		:    int         =				{}
			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     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			str						,__lowercase						:			List[Any]     ):
			'''simple docstring'''




			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     UpperCamelCase						(	__lowercase						:			str     ):
			'''simple docstring'''




			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
_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}""")







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase            =		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AutoformerForPrediction""",
	    """AutoformerModel""",
	    """AutoformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






