

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


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

__A      		=     {
    '''169M''': 7_68,
    '''430M''': 10_24,
    '''1B5''': 20_48,
    '''3B''': 25_60,
    '''7B''': 40_96,
    '''14B''': 51_20,
}



def 				snake_case_(_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							list(state_dict.keys()					)
  for name in state_dict_keys:
    _snake_case						=							state_dict.pop(__a					)
    # 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'''			,	__a					)
    # ffn -> feed_forward
    _snake_case						=							re.sub(R'''blocks\.(\d+)\.ffn'''			,	R'''blocks.\1.feed_forward'''			,	__a					)
    # 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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=None			,	_UpperCamelCase=None			,	_UpperCamelCase=False			,	_UpperCamelCase=None					)							->     int:



  """simple docstring"""






  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=__a					)
    _snake_case						=							len(__a					)
  tokenizer.save_pretrained(__a					)

  # 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=__a			,	num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,	hidden_size=HIDEN_SIZE_MAPPING[size]			,	)
  config.save_pretrained(__a					)

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

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

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

    # 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(__a			,	__a					)					)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,	os.path.join(__a			,	__a					)					)

  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(__a					)
    model.push_to_hub(__a			,	max_shard_size='''2GB'''					)
    tokenizer.push_to_hub(__a					)


if __name__ == "__main__":
 __A      		=     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.''',
 )

 __A      		=     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,
 )




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      		=     logging.get_logger(__name__)

__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A      		=     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

        if token_ids_a is None:
          return [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

