


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


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

def 	lowerCAmelCase__			(      a__:		Any	)  ->		Optional[int]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Dict	)  ->		Dict:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Any	)  ->		Tuple:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str	)  ->		Dict:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		Optional[int]=5	)  ->		List[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      ['auto-generated', 'autogenerated', 'automatically generated']
							_UpperCAmelCase					=      example['content'].splitlines()
							for _, line in zip(range(a__	)      ,  a__	):
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"autogenerated": True}
							else:
														return {"autogenerated": False}

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		Union[str, Any]=5      ,  a__:		str=0.05	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      ['unit tests', 'test file', 'configuration file']
							_UpperCAmelCase					=      example['content'].splitlines()
							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      0
							# first test
							for _, line in zip(range(a__	)      ,  a__	):
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"config_or_test": True}
    # second test
							_UpperCAmelCase					=      example['content'].count('\n'	)
							_UpperCAmelCase					=      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__			(      a__:		Optional[int]	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      ['def ', 'class ', 'for ', 'while ']
							_UpperCAmelCase					=      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__			(      a__:		List[str]      ,  a__:		Dict=4	)  ->		Optional[Any]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		int	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      tokenizer(example['content']      ,  truncation=a__	)['input_ids']
							_UpperCAmelCase					=      len(example['content']	) / len(a__	)
							return {"ratio": ratio}

def 	lowerCAmelCase__			(      a__:		str	)  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      {}
							results.update(get_hash(a__	)	)
							results.update(line_stats(a__	)	)
							results.update(alpha_stats(a__	)	)
							results.update(char_token_ratio(a__	)	)
							results.update(is_autogenerated(a__	)	)
							results.update(is_config_or_test(a__	)	)
							results.update(has_no_keywords(a__	)	)
							results.update(has_few_assignments(a__	)	)
							return results

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		List[str]      ,  a__:		Tuple	)  ->		Union[str, Any]:


							'''simple docstring'''





							if not check_uniques(a__      ,  a__	):
														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__			(      a__:		Union[str, Any]	)  ->		Optional[Any]:


							'''simple docstring'''





							with open(a__      ,  'rb'	) as f_in:
														with gzip.open(str(a__	) + '.gz'      ,  'wb'      ,  compresslevel=6	) as f_out:
																					shutil.copyfileobj(a__      ,  a__	)
							os.unlink(a__	)


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

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

# Run preprocessing
lowerCAmelCase__  :Optional[int]             =							time.time()
lowerCAmelCase__  :Optional[Any]             =							ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

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

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

lowerCAmelCase__  :Optional[int]             =							output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()	)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()	)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--longformer_model''',
		    default=None,
		    type=str,
		    required=True,
		    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
		)
		parser.add_argument(
		    '''--longformer_question_answering_ckpt_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch Lightning Checkpoint.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
