


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


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

UpperCAmelCase__   	=							{
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}
def 				UpperCAmelCase_					(							__snake_case						)     ->			List[Any]:







 """simple docstring"""


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

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

  _lowercase							      =weight
 return state_dict
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=False     ,						__snake_case=None						)     ->			int:







 """simple docstring"""


 if tokenizer_file is None:
  print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''						)
  _lowercase							      =50277
  _lowercase							      =AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''						)
 else:
  _lowercase							      =PreTrainedTokenizerFast(tokenizer_file=__snake_case						)
  _lowercase							      =len(__snake_case						)
 tokenizer.save_pretrained(__snake_case						)

 # 2. Build the config
 _lowercase							      =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:
    _lowercase							      =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}."						)

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

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

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

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

  # 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.'''						)
  _lowercase							      =list(shards.keys()						)

  del state_dict
  del shards
  gc.collect()

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

 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.'''						)
  _lowercase							      =AutoModelForCausalLM.from_pretrained(__snake_case						)
  model.push_to_hub(__snake_case     ,						max_shard_size='''2GB'''						)
  tokenizer.push_to_hub(__snake_case						)


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

  UpperCAmelCase__   	=							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,
  )





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

