




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


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





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





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





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





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





def __UpperCamelCase			(       _A ,  _A=5  ):
						lowerCAmelCase_      			=  ['''auto-generated''', '''autogenerated''', '''automatically generated''']
						lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A ,  _A=5 ,  _A=0.0_5  ):
						lowerCAmelCase_      			=  ['''unit tests''', '''test file''', '''configuration file''']
						lowerCAmelCase_      			=  example['''content'''].splitlines()
						lowerCAmelCase_      			=  0
						lowerCAmelCase_      			=  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
						lowerCAmelCase_      			=  example['''content'''].count('''\n'''  )
						lowerCAmelCase_      			=  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			(       _A  ):
						lowerCAmelCase_      			=  ['''def ''', '''class ''', '''for ''', '''while ''']
						lowerCAmelCase_      			=  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			(       _A ,  _A=4  ):
						lowerCAmelCase_      			=  example['''content'''].splitlines()
						lowerCAmelCase_      			=  0
						for line in lines:
												counter += line.lower().count('''='''  )
												if counter > minimum:
																		return {"has_few_assignments": False}
						return {"has_few_assignments": True}





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  tokenizer(example['''content'''] ,  truncation=_A  )['''input_ids''']
						lowerCAmelCase_      			=  len(example['''content''']  ) / len(_A  )
						return {"ratio": ratio}





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  {}
						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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						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 __UpperCamelCase			(       _A  ):
						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
_A     =			HfArgumentParser(PreprocessingArguments)
_A     =			parser.parse_args()
if args.num_workers is None:
						_A     =			multiprocessing.cpu_count()
_A     =			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

_A     =			time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
						_A     =			str(data_dir / f"file-{file_number+1:012}.json")
						_A     =			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
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  max_position_embeddings=args.max_target_positions ,  num_layers=args.decoder_layers ,  attention_heads=args.decoder_attention_heads ,  ffn_dim=args.decoder_ffn_embed_dim ,  d_model=args.decoder_embed_dim ,  layerdrop=args.decoder_layerdrop ,  dropout=args.dropout ,  attention_dropout=args.attention_dropout ,  activation_dropout=args.activation_dropout ,  activation_function='''gelu''' ,  scale_embedding=not args.no_scale_embedding ,  tie_word_embeddings=args.share_decoder_input_output_embed ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




