
import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_lowerCamelCase	:    Any          =      {
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

_lowerCamelCase	:    List[str]          =      {
    '''169M''': 768,
    '''430M''': 1_024,
    '''1B5''': 2_048,
    '''3B''': 2_560,
    '''7B''': 4_096,
    '''14B''': 5_120,
}






def 	a_		(	__lowercase       :     Tuple			)			->  str:
			_snake_case          =      list(state_dict.keys()			)
			for name in state_dict_keys:
						_snake_case          =      state_dict.pop(__lowerCAmelCase			)
						# emb -> embedding
						if name.startswith('emb.'			):
									_snake_case          =      name.replace('emb.'	, 'embeddings.'			)
						# ln_0 -> pre_ln (only present at block 0)
						if name.startswith('blocks.0.ln0'			):
									_snake_case          =      name.replace('blocks.0.ln0'	, 'blocks.0.pre_ln'			)
						# att -> attention
						_snake_case          =      re.sub(r'blocks\.(\d+)\.att'	, r'blocks.\1.attention'	, __lowerCAmelCase			)
						# ffn -> feed_forward
						_snake_case          =      re.sub(r'blocks\.(\d+)\.ffn'	, r'blocks.\1.feed_forward'	, __lowerCAmelCase			)
						# time_mix_k -> time_mix_key and reshape
						if name.endswith('.time_mix_k'			):
									_snake_case          =      name.replace('.time_mix_k'	, '.time_mix_key'			)
						# time_mix_v -> time_mix_value and reshape
						if name.endswith('.time_mix_v'			):
									_snake_case          =      name.replace('.time_mix_v'	, '.time_mix_value'			)
						# time_mix_r -> time_mix_key and reshape
						if name.endswith('.time_mix_r'			):
									_snake_case          =      name.replace('.time_mix_r'	, '.time_mix_receptance'			)

						if name != "head.weight":
									_snake_case          =      'rwkv.' + name

						_snake_case          =      weight
			return state_dict






def 	a_		(	__lowercase       :     int	, __lowercase       :     str	, __lowercase       :     int	, __lowercase       :     Optional[int]=None	, __lowercase       :     Union[str, Any]=None	, __lowercase       :     Dict=False	, __lowercase       :     Dict=None			)			->  Optional[Any]:
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print('No `--tokenizer_file` provided, we will use the default tokenizer.'			)
						_snake_case          =      50_277
						_snake_case          =      AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'			)
			else:
						_snake_case          =      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase			)
						_snake_case          =      len(__lowerCAmelCase			)
			tokenizer.save_pretrained(__lowerCAmelCase			)

			# 2. Build the config
			_snake_case          =      list(NUM_HIDDEN_LAYERS_MAPPING.keys()			)
			if size is None:
						# Try to infer size from the checkpoint name
						for candidate in possible_sizes:
									if candidate in checkpoint_file:
												_snake_case          =      candidate
												break
						if size is None:
									raise ValueError('Could not infer the size, please provide it with the `--size` argument.'			)
			if size not in possible_sizes:
						raise ValueError(f'''`size` should be one of {possible_sizes}, got {size}.'''			)

			_snake_case          =      RwkvConfig(
			    vocab_size=__lowerCAmelCase	, num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]	, hidden_size=HIDEN_SIZE_MAPPING[size]	, )
			config.save_pretrained(__lowerCAmelCase			)

			# 3. Download model file then convert state_dict
			_snake_case          =      hf_hub_download(__lowerCAmelCase	, __lowerCAmelCase			)
			_snake_case          =      torch.load(__lowerCAmelCase	, map_location='cpu'			)
			_snake_case          =      convert_state_dict(__lowerCAmelCase			)

			# 4. Split in shards and save
			_snake_case     , _snake_case          =      shard_checkpoint(__lowerCAmelCase			)
			for shard_file, shard in shards.items():
						torch.save(__lowerCAmelCase	, os.path.join(__lowerCAmelCase	, __lowerCAmelCase			)			)

			if index is not None:
						_snake_case          =      os.path.join(__lowerCAmelCase	, __lowerCAmelCase			)
						# Save the index as well
						with open(__lowerCAmelCase	, 'w'	, encoding='utf-8'			) as f:
									_snake_case          =      json.dumps(__lowerCAmelCase	, indent=2	, sort_keys=__lowerCAmelCase			) + '\n'
									f.write(__lowerCAmelCase			)

						# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
						print(
						    'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'			)
						_snake_case          =      list(shards.keys()			)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									_snake_case          =      torch.load(os.path.join(__lowerCAmelCase	, __lowerCAmelCase			)			)
									torch.save({k: v.cpu().clone() for k, v in state_dict.items()}	, os.path.join(__lowerCAmelCase	, __lowerCAmelCase			)			)

			del state_dict
			gc.collect()

			if push_to_hub:
						if model_name is None:
									raise ValueError('Please provide a `model_name` to push the model to the Hub.'			)
						_snake_case          =      AutoModelForCausalLM.from_pretrained(__lowerCAmelCase			)
						model.push_to_hub(__lowerCAmelCase	, max_shard_size='2GB'			)
						tokenizer.push_to_hub(__lowerCAmelCase			)


if __name__ == "__main__":
					_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
					)
					parser.add_argument(
					    '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
					)
					parser.add_argument(
					    '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
					)
					parser.add_argument(
					    '''--tokenizer_file''',
					    default=None,
					    type=str,
					    help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
					)
					parser.add_argument(
					    '''--size''',
					    default=None,
					    type=str,
					    help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
					)
					parser.add_argument(
					    '''--push_to_hub''',
					    action='''store_true''',
					    help='''Push to the Hub the converted model.''',
					)
					parser.add_argument(
					    '''--model_name''',
					    default=None,
					    type=str,
					    help='''Name of the pushed model on the Hub, including the username / organization.''',
					)

					_lowerCamelCase	:    int          =      parser.parse_args()
					convert_rmkv_checkpoint_to_hf_format(
					    args.repo_id,
					    args.checkpoint_file,
					    args.output_dir,
					    size=args.size,
					    tokenizer_file=args.tokenizer_file,
					    push_to_hub=args.push_to_hub,
					    model_name=args.model_name,
					)

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	:    Tuple          =      logging.getLogger(__name__)






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

			logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''			)
			if args.tokenizer_type == "bert":
						_snake_case          =      BertTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `[CLS]`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						_snake_case          =      RobertaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `<s>`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						_snake_case          =      GPTaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
						_snake_case          =      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:
						_snake_case          =      fp.readlines()

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

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

						iter += 1
						if iter % interval == 0:
									_snake_case          =      time.time()
									logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''			)
									_snake_case          =      time.time()
			logger.info('Finished binarization'			)
			logger.info(f'''{len(__lowercase			)} examples processed.'''			)

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


if __name__ == "__main__":
			main()