






'''simple docstring'''





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


snake_case__      :	List[Any]       	=      {
    """169M""": 1_2,
    """430M""": 2_4,
    """1B5""": 2_4,
    """3B""": 3_2,
    """7B""": 3_2,
    """14B""": 4_0,
}

snake_case__      :	Tuple       	=      {
    """169M""": 7_6_8,
    """430M""": 1_0_2_4,
    """1B5""": 2_0_4_8,
    """3B""": 2_5_6_0,
    """7B""": 4_0_9_6,
    """14B""": 5_1_2_0,
}






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     list(state_dict.keys())
   for name in state_dict_keys:
      UpperCamelCase_									=     state_dict.pop(__lowercase)
      # 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'    ,      __lowercase)
      # ffn -> feed_forward
      UpperCamelCase_									=     re.sub(r'blocks\.(\d+)\.ffn'    ,      r'blocks.\1.feed_forward'    ,      __lowercase)
      # 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     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=False    ,      __lowercase=None):
   # 1. If possible, build the tokenizer.
   if tokenizer_file is None:
      print('No `--tokenizer_file` provided, we will use the default tokenizer.')
      UpperCamelCase_									=     50277
      UpperCamelCase_									=     AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
   else:
      UpperCamelCase_									=     PreTrainedTokenizerFast(tokenizer_file=__lowercase)
      UpperCamelCase_									=     len(__lowercase)
   tokenizer.save_pretrained(__lowercase)

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

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

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

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

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

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


if __name__ == "__main__":
 snake_case__      :	Optional[Any]       	=      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.""",
 )

 snake_case__      :	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,
 )













import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   inspect.getfile(accelerate.test_utils    )
  A_						=   os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_cli.py"""]    )

  A_						=   ["""accelerate""", """launch"""]
  A_						=   Path.home() / """.cache/huggingface/accelerate"""
  A_						=   """default_config.yaml"""
  A_						=   config_folder / config_file
  A_						=   config_folder / """_default_config.yaml"""

  A_						=   Path("""tests/test_configs"""    )




  @classmethod
  def    _UpperCAmelCase     (     cls )			-> Union[str, Any]:
     if cls.config_path.is_file():
        cls.config_path.rename(cls.changed_path )




  @classmethod
  def    _UpperCAmelCase     (     cls )			-> List[str]:
     if cls.changed_path.is_file():
        cls.changed_path.rename(cls.config_path )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
        cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]		,		env=os.environ.copy() )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     for config in sorted(self.test_config_path.glob('**/*.yaml' ) ):
        with self.subTest(config_file=_UpperCAmelCase ):
           execute_subprocess_async(
               self.base_cmd + ['--config_file', str(_UpperCAmelCase ), self.test_file_path]		,		env=os.environ.copy() )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     execute_subprocess_async(['accelerate', 'test']		,		env=os.environ.copy() )




class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   """test-tpu"""
  A_						=   """us-central1-a"""
  A_						=   """ls"""
  A_						=   ["""accelerate""", """tpu-config"""]
  A_						=   """cd /usr/share"""
  A_						=   """tests/test_samples/test_command_file.sh"""
  A_						=   """Running gcloud compute tpus tpu-vm ssh"""




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd
         + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command',
             self.command,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']		,		return_stdout=_UpperCAmelCase )
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--command',
             self.command,
             '--command',
             'echo "Hello World"',
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     run_command(
         self.cmd
         + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command_file',
             self.command_file,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--install_accelerate',
             '--accelerate_version',
             '12.0.0',
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)





