





"""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


__magic_name__       :					int						=			re.compile(R"""\s+""")

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			[len(SCREAMING_SNAKE_CASE     ) for line in example["""content"""].splitlines()]
						return {"line_mean": np.mean(SCREAMING_SNAKE_CASE     ), "line_max": max(SCREAMING_SNAKE_CASE     )}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			np.mean([c.isalnum() for c in example["""content"""]]     )
						return {"alpha_frac": alpha_frac}

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

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=5							,     SCREAMING_SNAKE_CASE=0.05     ):
						UpperCamelCase				:		List[str]				   =			["""unit tests""", """test file""", """configuration file"""]
						UpperCamelCase				:		int				   =			example["""content"""].splitlines()
						UpperCamelCase				:		Optional[Any]				   =			0
						UpperCamelCase				:		int				   =			0
						# first test
						for _, line in zip(range(SCREAMING_SNAKE_CASE     )							,     SCREAMING_SNAKE_CASE     ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"config_or_test": True}
    # second test
						UpperCamelCase				:		Any				   =			example["""content"""].count("""\n"""     )
						UpperCamelCase				:		List[str]				   =			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							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			["""def """, """class """, """for """, """while """]
						UpperCamelCase				:		Any				   =			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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=4     ):
						UpperCamelCase				:		List[str]				   =			example["""content"""].splitlines()
						UpperCamelCase				:		Optional[Any]				   =			0
						for line in lines:
												counter += line.lower().count("""="""     )
												if counter > minimum:
																		return {"has_few_assignments": False}
						return {"has_few_assignments": True}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			tokenizer(example["""content"""]							,     truncation=SCREAMING_SNAKE_CASE     )["""input_ids"""]
						UpperCamelCase				:		Optional[Any]				   =			len(example["""content"""]     ) / len(SCREAMING_SNAKE_CASE     )
						return {"ratio": ratio}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		str				   =			{}
						results.update(get_hash(SCREAMING_SNAKE_CASE     )     )
						results.update(line_stats(SCREAMING_SNAKE_CASE     )     )
						results.update(alpha_stats(SCREAMING_SNAKE_CASE     )     )
						results.update(char_token_ratio(SCREAMING_SNAKE_CASE     )     )
						results.update(is_autogenerated(SCREAMING_SNAKE_CASE     )     )
						results.update(is_config_or_test(SCREAMING_SNAKE_CASE     )     )
						results.update(has_no_keywords(SCREAMING_SNAKE_CASE     )     )
						results.update(has_few_assignments(SCREAMING_SNAKE_CASE     )     )
						return results

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


# Settings
__magic_name__       :					List[Any]						=			HfArgumentParser(PreprocessingArguments)
__magic_name__       :					Union[str, Any]						=			parser.parse_args()
if args.num_workers is None:
		__magic_name__       :					List[Any]						=			multiprocessing.cpu_count()
__magic_name__       :					Optional[Any]						=			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
__magic_name__       :					List[str]						=			time.time()
__magic_name__       :					Tuple						=			ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

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

# Deduplicate data and apply heuristics
__magic_name__       :					int						=			time.time()
__magic_name__       :					Union[str, 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:
		__magic_name__       :					Optional[Any]						=			time.time()
		__magic_name__						,							__magic_name__       :					str						=			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
__magic_name__       :					Dict						=			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)

__magic_name__       :					List[Any]						=			output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__magic_name__       :					Optional[int]						=			time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		__magic_name__       :					Any						=			str(data_dir / f'''file-{file_number+1:012}.json''')
		__magic_name__       :					Dict						=			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"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__magic_name__       :					Union[str, Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



