





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

snake_case 				=       {
    """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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     ):
  _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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :Optional[int]			,							snake_case__  :List[str]			,							snake_case__  :Any=None			,							snake_case__  :Any=None			,							snake_case__  :Dict=False			,							snake_case__  :Optional[int]=None     ):
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print('No `--tokenizer_file` provided, we will use the default tokenizer.'     )
    _lowercase							= 5_0277
    _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__":
      snake_case 				=       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 				=       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 warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,*__A	:	Optional[int]						,**__A	:	List[Any]  )						->					None:
         warnings.warn(
             'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DonutImageProcessor instead.'						,__A						,)
         super().__init__(*__A						,**__A  )