

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


SCREAMING_SNAKE_CASE_        =      re.compile(R'\s+')

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       Optional[int]:
				return {"hash": hashlib.mda(re.sub(lowerCAmelCase  ,      ""  ,      example["content"] ).encode("utf-8" ) ).hexdigest()}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       List[str]:
				_UpperCAmelCase       :		Optional[Any] 		=    [len(lowerCAmelCase ) for line in example["content"].splitlines()]
				return {"line_mean": np.mean(lowerCAmelCase ), "line_max": max(lowerCAmelCase )}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    np.mean([c.isalnum() for c in example["content"]] )
				return {"alpha_frac": alpha_frac}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Optional[int] )   ->       Dict:
				if example["hash"] in uniques:
								uniques.remove(example["hash"] )
								return True
				else:
								return False

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Union[str, Any]=5 )   ->       List[str]:
				_UpperCAmelCase       :		List[str] 		=    ["auto-generated", "autogenerated", "automatically generated"]
				_UpperCAmelCase       :		Optional[int] 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  List[Any]=5  ,      lowerCAmelCase:  int=0.05 )   ->       Optional[Any]:
				_UpperCAmelCase       :		Dict 		=    ["unit tests", "test file", "configuration file"]
				_UpperCAmelCase       :		Any 		=    example["content"].splitlines()
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		Optional[Any] 		=    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
				_UpperCAmelCase       :		str 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       Union[str, Any]:
				_UpperCAmelCase       :		List[str] 		=    ["def ", "class ", "for ", "while "]
				_UpperCAmelCase       :		Tuple 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  List[str]=4 )   ->       List[Any]:
				_UpperCAmelCase       :		Optional[Any] 		=    example["content"].splitlines()
				_UpperCAmelCase       :		List[str] 		=    0
				for line in lines:
								counter += line.lower().count("=" )
								if counter > minimum:
												return {"has_few_assignments": False}
				return {"has_few_assignments": True}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       Optional[Any]:
				_UpperCAmelCase       :		Optional[Any] 		=    tokenizer(example["content"]  ,      truncation=lowerCAmelCase )["input_ids"]
				_UpperCAmelCase       :		List[str] 		=    len(example["content"] ) / len(lowerCAmelCase )
				return {"ratio": ratio}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any] )   ->       Tuple:
				_UpperCAmelCase       :		Union[str, Any] 		=    {}
				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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  List[str] )   ->       Tuple:
				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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Union[str, Any]:
				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
SCREAMING_SNAKE_CASE_        =      HfArgumentParser(PreprocessingArguments)
SCREAMING_SNAKE_CASE_        =      parser.parse_args()
if args.num_workers is None:
					SCREAMING_SNAKE_CASE_        =      multiprocessing.cpu_count()
SCREAMING_SNAKE_CASE_        =      AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

SCREAMING_SNAKE_CASE_        =      output_dir / 'data'
data_dir.mkdir(exist_ok=True)

SCREAMING_SNAKE_CASE_        =      time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
					SCREAMING_SNAKE_CASE_        =      str(data_dir / F'''file-{file_number+1:012}.json''')
					SCREAMING_SNAKE_CASE_        =      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


