






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

__a:     Tuple   					=					{
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}




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

              if name != "head.weight":
                     _UpperCAmelCase							=    """rwkv.""" + name

              _UpperCAmelCase							=    weight
       return state_dict




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None   ,      __snake_case=None   ,      __snake_case=False   ,      __snake_case=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.""" )
              _UpperCAmelCase							=    5_0_2_7_7
              _UpperCAmelCase							=    AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
       else:
              _UpperCAmelCase							=    PreTrainedTokenizerFast(tokenizer_file=__snake_case )
              _UpperCAmelCase							=    len(__snake_case )
       tokenizer.save_pretrained(__snake_case )

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

       _UpperCAmelCase							=    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
       _UpperCAmelCase							=    hf_hub_download(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )
       _UpperCAmelCase							=    convert_state_dict(__snake_case )

       # 4. Split in shards and save
       _UpperCAmelCase	,			_UpperCAmelCase							=    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:
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      __snake_case )
              # Save the index as well
              with open(__snake_case   ,      """w"""   ,      encoding="""utf-8""" ) as f:
                     _UpperCAmelCase							=    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.""" )
              _UpperCAmelCase							=    list(shards.keys() )

              del state_dict
              del shards
              gc.collect()

              for shard_file in shard_files:
                     _UpperCAmelCase							=    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.""" )
              _UpperCAmelCase							=    AutoModelForCausalLM.from_pretrained(__snake_case )
              model.push_to_hub(__snake_case   ,      max_shard_size="""2GB""" )
              tokenizer.push_to_hub(__snake_case )


if __name__ == "__main__":
  __a:     Dict   					=					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:     Union[str, Any]   					=					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,
  )







'''simple docstring'''



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 				lowercase_					(     __A			:							List[Any]      )							->   Union[str, Any]:



 """simple docstring"""




 return EnvironmentCommand()


def 				lowercase_					(     __A			:							int      )							->   Dict:



 """simple docstring"""




 return EnvironmentCommand(args.accelerate_config_file      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @staticmethod
      def 					A__   (			UpperCAmelCase      :		ArgumentParser		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[int]						=parser.add_parser('''env'''		)
       download_parser.set_defaults(func=UpperCAmelCase		)
       download_parser.add_argument(
           '''--accelerate-config_file'''  ,   default=UpperCAmelCase  ,   help='''The accelerate config file to use for the default values in the launching script.'''  ,   )
       download_parser.set_defaults(func=UpperCAmelCase		)






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   *UpperCAmelCase      :		Tuple		)	->	None:


       '''simple docstring'''

       lowercase       : List[str]						=accelerate_config_file






      def 					A__   (			self      :		Tuple		)	->	int:


       '''simple docstring'''

       lowercase       : Optional[Any]						='''not installed'''
       if is_safetensors_available():
        import safetensors

        lowercase       : List[Any]						=safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''		) is not None:
        import safetensors

        lowercase       : List[str]						=f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

       lowercase       : List[str]						='''not installed'''
       lowercase       : str						='''not found'''
       if is_accelerate_available():
        import accelerate
        from accelerate.commands.config import default_config_file, load_config_from_file

        lowercase       : List[str]						=accelerate.__version__
        # Get the default from the config file.
        if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase		):
         lowercase       : Dict						=load_config_from_file(self._accelerate_config_file		).to_dict()

        lowercase       : Optional[int]						=(
            '''\n'''.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]		)
            if isinstance(UpperCAmelCase  ,   UpperCAmelCase		)
            else f'\t{accelerate_config}'
        )

       lowercase       : Optional[int]						='''not installed'''
       lowercase       : Optional[Any]						='''NA'''
       if is_torch_available():
        import torch

        lowercase       : List[Any]						=torch.__version__
        lowercase       : Dict						=torch.cuda.is_available()

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_tf_available():
        import tensorflow as tf

        lowercase       : List[Any]						=tf.__version__
        try:
         # deprecated in v2.1
         lowercase       : Optional[Any]						=tf.test.is_gpu_available()
        except AttributeError:
         # returns list of devices, convert to bool
         lowercase       : Optional[int]						=bool(tf.config.list_physical_devices('''GPU'''		)		)

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_flax_available():
        import flax
        import jax
        import jaxlib

        lowercase       : Dict						=flax.__version__
        lowercase       : List[Any]						=jax.__version__
        lowercase       : Any						=jaxlib.__version__
        lowercase       : Tuple						=jax.lib.xla_bridge.get_backend().platform

       lowercase       : str						={
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'{safetensors_version}',
           '''Accelerate version''': f'{accelerate_version}',
           '''Accelerate config''': f'{accelerate_config_str}',
           '''PyTorch version (GPU?)''': f'{pt_version} ({pt_cuda_available})',
           '''Tensorflow version (GPU?)''': f'{tf_version} ({tf_cuda_available})',
           '''Flax version (CPU?/GPU?/TPU?)''': f'{flax_version} ({jax_backend})',
           '''Jax version''': f'{jax_version}',
           '''JaxLib version''': f'{jaxlib_version}',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''		)
       print(self.format_dict(UpperCAmelCase		)		)

       return info






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Any		)	->	Any:


       '''simple docstring'''

       return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"



