





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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   List[str]:
 __lowerCAmelCase					:	List[Any]      =       list(state_dict.keys()   )
 for name in state_dict_keys:
  __lowerCAmelCase					:	str      =       state_dict.pop(SCREAMING_SNAKE_CASE   )
  # emb -> embedding
  if name.startswith("""emb."""   ):
   __lowerCAmelCase					:	Optional[Any]      =       name.replace("""emb."""   ,   """embeddings."""   )
  # ln_0 -> pre_ln (only present at block 0)
  if name.startswith("""blocks.0.ln0"""   ):
   __lowerCAmelCase					:	List[str]      =       name.replace("""blocks.0.ln0"""   ,   """blocks.0.pre_ln"""   )
  # att -> attention
  __lowerCAmelCase					:	str      =       re.sub(r"""blocks\.(\d+)\.att"""   ,   r"""blocks.\1.attention"""   ,   SCREAMING_SNAKE_CASE   )
  # ffn -> feed_forward
  __lowerCAmelCase					:	Any      =       re.sub(r"""blocks\.(\d+)\.ffn"""   ,   r"""blocks.\1.feed_forward"""   ,   SCREAMING_SNAKE_CASE   )
  # time_mix_k -> time_mix_key and reshape
  if name.endswith(""".time_mix_k"""   ):
   __lowerCAmelCase					:	str      =       name.replace(""".time_mix_k"""   ,   """.time_mix_key"""   )
  # time_mix_v -> time_mix_value and reshape
  if name.endswith(""".time_mix_v"""   ):
   __lowerCAmelCase					:	Tuple      =       name.replace(""".time_mix_v"""   ,   """.time_mix_value"""   )
  # time_mix_r -> time_mix_key and reshape
  if name.endswith(""".time_mix_r"""   ):
   __lowerCAmelCase					:	Any      =       name.replace(""".time_mix_r"""   ,   """.time_mix_receptance"""   )

  if name != "head.weight":
   __lowerCAmelCase					:	str      =       """rwkv.""" + name

  __lowerCAmelCase					:	int      =       weight
 return state_dict






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

 # 2. Build the config
 __lowerCAmelCase					:	Optional[Any]      =       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:
    __lowerCAmelCase					:	Union[str, Any]      =       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}.'''   )

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

 # 3. Download model file then convert state_dict
 __lowerCAmelCase					:	Optional[Any]      =       hf_hub_download(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Tuple      =       torch.load(SCREAMING_SNAKE_CASE   ,   map_location="""cpu"""   )
 __lowerCAmelCase					:	Dict      =       convert_state_dict(SCREAMING_SNAKE_CASE   )

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

 if index is not None:
  __lowerCAmelCase					:	List[Any]      =       os.path.join(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  # Save the index as well
  with open(SCREAMING_SNAKE_CASE   ,   """w"""   ,   encoding="""utf-8"""   ) as f:
   __lowerCAmelCase					:	Optional[int]      =       json.dumps(SCREAMING_SNAKE_CASE   ,   indent=2   ,   sort_keys=SCREAMING_SNAKE_CASE   ) + """\n"""
   f.write(SCREAMING_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."""   )
  __lowerCAmelCase					:	int      =       list(shards.keys()   )

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   __lowerCAmelCase					:	List[Any]      =       torch.load(os.path.join(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   )
   torch.save({k: v.cpu().clone() for k, v in state_dict.items()}   ,   os.path.join(SCREAMING_SNAKE_CASE   ,   SCREAMING_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."""   )
  __lowerCAmelCase					:	Tuple      =       AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE   )
  model.push_to_hub(SCREAMING_SNAKE_CASE   ,   max_shard_size="""2GB"""   )
  tokenizer.push_to_hub(SCREAMING_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,
      )












from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class        snake_case_    (		__lowercase						):
  A_      							=   'gptj'
  A_      							=   {
      'max_position_embeddings': 'n_positions',
      'hidden_size': 'n_embd',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }






  def __init__(							self			:					Tuple       ,  _snake_case			:					Optional[int]=50400       ,  _snake_case			:					Union[str, Any]=2048       ,  _snake_case			:					Tuple=4096       ,  _snake_case			:					Optional[int]=28       ,  _snake_case			:					Tuple=16       ,  _snake_case			:					Optional[Any]=64       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					str="gelu_new"       ,  _snake_case			:					str=0.0       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					List[str]=0.0       ,  _snake_case			:					Tuple=1E-5       ,  _snake_case			:					List[str]=0.02       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[Any]=50256       ,  _snake_case			:					List[str]=50256       ,  _snake_case			:					str=False       ,  **_snake_case			:					Tuple       ,  )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       vocab_size
   __lowerCAmelCase					:	Any      =       n_positions
   __lowerCAmelCase					:	Optional[Any]      =       n_embd
   __lowerCAmelCase					:	Optional[int]      =       n_layer
   __lowerCAmelCase					:	Optional[int]      =       n_head
   __lowerCAmelCase					:	List[Any]      =       n_inner
   __lowerCAmelCase					:	List[str]      =       rotary_dim
   __lowerCAmelCase					:	int      =       activation_function
   __lowerCAmelCase					:	str      =       resid_pdrop
   __lowerCAmelCase					:	Union[str, Any]      =       embd_pdrop
   __lowerCAmelCase					:	Dict      =       attn_pdrop
   __lowerCAmelCase					:	Optional[Any]      =       layer_norm_epsilon
   __lowerCAmelCase					:	Optional[Any]      =       initializer_range
   __lowerCAmelCase					:	Dict      =       use_cache

   __lowerCAmelCase					:	List[str]      =       bos_token_id
   __lowerCAmelCase					:	List[Any]      =       eos_token_id

   super().__init__(
       bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  tie_word_embeddings=_snake_case       ,  **_snake_case      )






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					PretrainedConfig       ,  _snake_case			:					str = "default"       ,  _snake_case			:					List[PatchingSpec] = None       ,  _snake_case			:					bool = False       ,  )->str:






   '''simple docstring'''

   super().__init__(_snake_case       ,  task=_snake_case       ,  patching_specs=_snake_case       ,  use_past=_snake_case      )
   if not getattr(self._config       ,  """pad_token_id"""       ,  _snake_case      ):
    # TODO: how to do that better?
    __lowerCAmelCase					:	Dict      =       0






  @property
  def 				UpperCAmelCase__						(							self			:					Tuple      )->Mapping[str, Mapping[int, str]]:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
   if self.use_past:
    self.fill_with_past_key_values_(_snake_case       ,  direction="""inputs"""      )
    __lowerCAmelCase					:	Dict      =       {0: """batch""", 1: """past_sequence + sequence"""}
   else:
    __lowerCAmelCase					:	Optional[Any]      =       {0: """batch""", 1: """sequence"""}

   return common_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->int:






   '''simple docstring'''

   return self._config.n_layer






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return self._config.n_head






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					PreTrainedTokenizer       ,  _snake_case			:					int = -1       ,  _snake_case			:					int = -1       ,  _snake_case			:					bool = False       ,  _snake_case			:					Optional[TensorType] = None       ,  )->Mapping[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       super(_snake_case       ,  self      ).generate_dummy_inputs(
       _snake_case       ,  batch_size=_snake_case       ,  seq_length=_snake_case       ,  is_pair=_snake_case       ,  framework=_snake_case      )

   # We need to order the input in the way they appears in the forward()
   __lowerCAmelCase					:	Union[str, Any]      =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

   # Need to add the past_keys
   if self.use_past:
    if not is_torch_available():
     raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
    else:
     import torch

     __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     __lowerCAmelCase					:	Optional[Any]      =       seqlen + 2
     __lowerCAmelCase					:	Dict      =       (
         batch,
         self.num_attention_heads,
         past_key_values_length,
         self._config.hidden_size // self.num_attention_heads,
     )
     __lowerCAmelCase					:	Tuple      =       [
         (torch.zeros(_snake_case      ), torch.zeros(_snake_case      )) for _ in range(self.num_layers      )
     ]

   __lowerCAmelCase					:	int      =       common_inputs["""attention_mask"""]
   if self.use_past:
    __lowerCAmelCase					:	Union[str, Any]      =       ordered_inputs["""attention_mask"""].dtype
    __lowerCAmelCase					:	List[str]      =       torch.cat(
        [ordered_inputs["""attention_mask"""], torch.ones(_snake_case       ,  _snake_case       ,  dtype=_snake_case      )]       ,  dim=1      )

   return ordered_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return 13