




"""simple docstring"""

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase  :			int        =       logging.getLogger(__name__)

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				str	   =		argparse.ArgumentParser(
	    description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''	)
	parser.add_argument('''--file_path'''  ,      type=_UpperCAmelCase  ,      default='''data/dump.txt'''  ,      help='''The path to the data.'''	)
	parser.add_argument('''--tokenizer_type'''  ,      type=_UpperCAmelCase  ,      default='''bert'''  ,      choices=['''bert''', '''roberta''', '''gpt2''']	)
	parser.add_argument('''--tokenizer_name'''  ,      type=_UpperCAmelCase  ,      default='''bert-base-uncased'''  ,      help='''The tokenizer to use.'''	)
	parser.add_argument('''--dump_file'''  ,      type=_UpperCAmelCase  ,      default='''data/dump'''  ,      help='''The dump file prefix.'''	)
	A_  :				List[Any]	   =		parser.parse_args()

	logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""	)
	if args.tokenizer_type == "bert":
		A_  :				Dict	   =		BertTokenizer.from_pretrained(args.tokenizer_name	)
		A_  :				Optional[int]	   =		tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
		A_  :				Dict	   =		tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		A_  :				Optional[int]	   =		RobertaTokenizer.from_pretrained(args.tokenizer_name	)
		A_  :				Optional[int]	   =		tokenizer.special_tokens_map['''cls_token''']  # `<s>`
		A_  :				Any	   =		tokenizer.special_tokens_map['''sep_token''']  # `</s>`
	elif args.tokenizer_type == "gpt2":
		A_  :				List[str]	   =		GPTaTokenizer.from_pretrained(args.tokenizer_name	)
		A_  :				Optional[int]	   =		tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
		A_  :				List[Any]	   =		tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

	logger.info(f"""Loading text from {args.file_path}"""	)
	with open(args.file_path  ,      '''r'''  ,      encoding='''utf8'''	) as fp:
		A_  :				Dict	   =		fp.readlines()

	logger.info('''Start encoding'''	)
	logger.info(f"""{len(_UpperCAmelCase	)} examples to process."""	)

	A_  :				int	   =		[]
	A_  :				Optional[int]	   =		0
	A_  :				int	   =		1_0000
	A_  :				Any	   =		time.time()
	for text in data:
		A_  :				Dict	   =		f"""{bos} {text.strip()} {sep}"""
		A_  :				str	   =		tokenizer.encode(_UpperCAmelCase  ,      add_special_tokens=_UpperCAmelCase	)
		rslt.append(_UpperCAmelCase	)

		iter += 1
		if iter % interval == 0:
			A_  :				List[Any]	   =		time.time()
			logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""	)
			A_  :				Optional[int]	   =		time.time()
	logger.info('''Finished binarization'''	)
	logger.info(f"""{len(_UpperCAmelCase	)} examples processed."""	)

	A_  :				Optional[Any]	   =		f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	A_  :				int	   =		tokenizer.vocab_size
	if vocab_size < (1 << 16):
		A_  :				List[Any]	   =		[np.uintaa(_UpperCAmelCase	) for d in rslt]
	else:
		A_  :				int	   =		[np.intaa(_UpperCAmelCase	) for d in rslt]
	random.shuffle(rslt_	)
	logger.info(f"""Dump to {dp_file}"""	)
	with open(_UpperCAmelCase  ,      '''wb'''	) as handle:
		pickle.dump(rslt_  ,      _UpperCAmelCase  ,      protocol=pickle.HIGHEST_PROTOCOL	)


if __name__ == "__main__":
		main()











"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      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  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       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.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





