


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







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







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







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







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







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







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase=5				,      __lowerCamelCase=0.0_5      ):
							__snake_case						:     List[str]									=  ["""unit tests""", """test file""", """configuration file"""]
							__snake_case						:     int									=  example["""content"""].splitlines()
							__snake_case						:     Optional[Any]									=  0
							__snake_case						:     int									=  0
							# first test
							for _, line in zip(range(__lowerCamelCase      )				,      __lowerCamelCase      ):
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"config_or_test": True}
    # second test
							__snake_case						:     Any									=  example["""content"""].count("\n"      )
							__snake_case						:     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 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     List[str]									=  ["""def """, """class """, """for """, """while """]
							__snake_case						:     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 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase=4      ):
							__snake_case						:     List[str]									=  example["""content"""].splitlines()
							__snake_case						:     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 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     Optional[int]									=  tokenizer(example["content"]				,      truncation=__lowerCamelCase      )["""input_ids"""]
							__snake_case						:     Optional[Any]									=  len(example["content"]      ) / len(__lowerCamelCase      )
							return {"ratio": ratio}







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







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


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

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

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

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

# Deduplicate data and apply heuristics
_snake_case	:						int         =							time.time()
_snake_case	:						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:
			_snake_case	:						Optional[Any]         =							time.time()
			_snake_case	:						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
_snake_case	:						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)

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

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


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



