





"""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


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

__A											=						{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[str]:





  """simple docstring"""


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

    if name != "head.weight":
      lowerCAmelCase__     :Union[str, Any]											=				'rwkv.' + name

    lowerCAmelCase__     :Tuple											=				weight
  return state_dict
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=False	,  _SCREAMING_SNAKE_CASE=None     )   ->Optional[int]:





  """simple docstring"""


  if tokenizer_file is None:
    print('No `--tokenizer_file` provided, we will use the default tokenizer.'     )
    lowerCAmelCase__     :Optional[int]											=				5_0277
    lowerCAmelCase__     :Union[str, Any]											=				AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'     )
  else:
    lowerCAmelCase__     :Union[str, Any]											=				PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[int]											=				len(_SCREAMING_SNAKE_CASE     )
  tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE     )

  # 2. Build the config
  lowerCAmelCase__     :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:
        lowerCAmelCase__     :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}."     )

  lowerCAmelCase__     :Dict											=				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
  lowerCAmelCase__     :Union[str, Any]											=				hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :str											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )
  lowerCAmelCase__     :Dict											=				convert_state_dict(_SCREAMING_SNAKE_CASE     )

  # 4. Split in shards and save
  lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				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:
    lowerCAmelCase__     :str											=				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:
      lowerCAmelCase__     :Any											=				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.'     )
    lowerCAmelCase__     :Dict											=				list(shards.keys()     )

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      lowerCAmelCase__     :Union[str, Any]											=				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.'     )
    lowerCAmelCase__     :Optional[Any]											=				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__":
  __A											=						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											=						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'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

		__UpperCAmelCase       							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)