





"""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


UpperCAmelCase     :     Union[str, Any]							=	{
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

UpperCAmelCase     :     Tuple							=	{
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



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

    if name != "head.weight":
      a__     : List[Any]  ='''rwkv.''' + name

    a__     : Union[str, Any]  =weight
  return state_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       List[str]=None	,	SCREAMING_SNAKE_CASE					:       Optional[Any]=None	,	SCREAMING_SNAKE_CASE					:       Optional[Any]=False	,	SCREAMING_SNAKE_CASE					:       Any=None						):





  """simple docstring"""



  if tokenizer_file is None:
    print("No `--tokenizer_file` provided, we will use the default tokenizer."						)
    a__     : Dict  =50_277
    a__     : Any  =AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"						)
  else:
    a__     : Optional[int]  =PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE						)
    a__     : Optional[Any]  =len(SCREAMING_SNAKE_CASE						)
  tokenizer.save_pretrained(SCREAMING_SNAKE_CASE						)

  # 2. Build the config
  a__     : 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:
        a__     : List[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}.'''						)

  a__     : str  =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
  a__     : Any  =hf_hub_download(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =torch.load(SCREAMING_SNAKE_CASE	,	map_location="cpu"						)
  a__     : List[Any]  =convert_state_dict(SCREAMING_SNAKE_CASE						)

  # 4. Split in shards and save
  a__     : 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:
    a__     : Dict  =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:
      a__     : Dict  =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."						)
    a__     : Tuple  =list(shards.keys()						)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      a__     : int  =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."						)
    a__     : Optional[int]  =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     :     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.""",
     )

     UpperCAmelCase     :     str							=	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 argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : Tuple  =set()
  a__     : Optional[Any]  =[]

  def parse_line(SCREAMING_SNAKE_CASE					:       Optional[int]						):
    for line in fp:
      if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
        a__     : str  =line.decode("UTF-8"						)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(" "						):
        # process a single warning and move it to `selected_warnings`.
        if len(SCREAMING_SNAKE_CASE						) > 0:
          a__     : Union[str, Any]  ="\n".join(SCREAMING_SNAKE_CASE						)
          # Only keep the warnings specified in `targets`
          if any(f''': {x}: ''' in warning for x in targets						):
            selected_warnings.add(SCREAMING_SNAKE_CASE						)
          buffer.clear()
        continue
      else:
        a__     : Optional[Any]  =line.strip()
        buffer.append(SCREAMING_SNAKE_CASE						)

  if from_gh:
    for filename in os.listdir(SCREAMING_SNAKE_CASE						):
      a__     : str  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(SCREAMING_SNAKE_CASE						) as fp:
          parse_line(SCREAMING_SNAKE_CASE						)
  else:
    try:
      with zipfile.ZipFile(SCREAMING_SNAKE_CASE						) as z:
        for filename in z.namelist():
          if not os.path.isdir(SCREAMING_SNAKE_CASE						):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(SCREAMING_SNAKE_CASE						) as fp:
              parse_line(SCREAMING_SNAKE_CASE						)
    except Exception:
      logger.warning(
          f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''						)

  return selected_warnings
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Optional[int]  =set()

  a__     : Any  =[os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for p in os.listdir(SCREAMING_SNAKE_CASE						) if (p.endswith(".zip"						) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)

  return selected_warnings


if __name__ == "__main__":
     def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





       """simple docstring"""



       return values.split(","						)

     UpperCAmelCase     :     Any							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
     parser.add_argument(
         """--output_dir""",
         type=str,
         required=True,
         help="""Where to store the downloaded artifacts and other result files.""",
     )
     parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
     # optional parameters
     parser.add_argument(
         """--targets""",
         default="""DeprecationWarning,UserWarning,FutureWarning""",
         type=list_str,
         help="""Comma-separated list of target warning(s) which we want to extract.""",
     )
     parser.add_argument(
         """--from_gh""",
         action="""store_true""",
         help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
     )

     UpperCAmelCase     :     List[Any]							=	parser.parse_args()

     UpperCAmelCase     :     str							=	args.from_gh
     if from_gh:
          # The artifacts have to be downloaded using `actions/download-artifact@v3`
          pass
     else:
          os.makedirs(args.output_dir, exist_ok=True)

          # get download links
          UpperCAmelCase     :     Dict							=	get_artifacts_links(args.workflow_run_id, token=args.token)
          with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
               json.dump(artifacts, fp, ensure_ascii=False, indent=4)

          # download artifacts
          for idx, (name, url) in enumerate(artifacts.items()):
               print(name)
               print(url)
               print("""=""" * 80)
               download_artifact(name, url, args.output_dir, args.token)
               # Be gentle to GitHub
               time.sleep(1)

    # extract warnings from artifacts
     UpperCAmelCase     :     Tuple							=	extract_warnings(args.output_dir, args.targets)
     UpperCAmelCase     :     Optional[Any]							=	sorted(selected_warnings)
     with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
          json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)






