






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

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




def       snake_case  (A_				:Tuple				):







  '''simple docstring'''

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

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

    a					:      Optional[int]    =       weight
  return state_dict




def       snake_case  (A_				:Union[str, Any]     ,      A_				:Union[str, Any]     ,      A_				:Optional[int]     ,      A_				:Optional[int]=None     ,      A_				:str=None     ,      A_				:List[Any]=False     ,      A_				:Optional[int]=None				):







  '''simple docstring'''

  if tokenizer_file is None:
    print('No `--tokenizer_file` provided, we will use the default tokenizer.'				)
    a					:      Any    =       5_0_2_7_7
    a					:      str    =       AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'				)
  else:
    a					:      List[Any]    =       PreTrainedTokenizerFast(tokenizer_file=A_				)
    a					:      List[str]    =       len(A_				)
  tokenizer.save_pretrained(A_				)

  # 2. Build the config
  a					:      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					:      int    =       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					:      int    =       RwkvConfig(
      vocab_size=A_     ,      num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,      hidden_size=HIDEN_SIZE_MAPPING[size]     ,      )
  config.save_pretrained(A_				)

  # 3. Download model file then convert state_dict
  a					:      Dict    =       hf_hub_download(A_     ,      A_				)
  a					:      int    =       torch.load(A_     ,      map_location='cpu'				)
  a					:      Optional[int]    =       convert_state_dict(A_				)

  # 4. Split in shards and save
  a,					a					:      List[str]    =       shard_checkpoint(A_				)
  for shard_file, shard in shards.items():
    torch.save(A_     ,      os.path.join(A_     ,      A_				)				)

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

    # 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					:      Optional[int]    =       list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      a					:      Tuple    =       torch.load(os.path.join(A_     ,      A_				)				)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,      os.path.join(A_     ,      A_				)				)

  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					:      List[str]    =       AutoModelForCausalLM.from_pretrained(A_				)
    model.push_to_hub(A_     ,      max_shard_size='2GB'				)
    tokenizer.push_to_hub(A_				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						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    :   Tuple   			=						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 argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




