






"""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


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

lowerCAmelCase_	:    int            =      {
    """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       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			list(state_dict.keys()		)
       for name in state_dict_keys:
              UpperCAmelCase	    =			state_dict.pop(UpperCamelCase__		)
              # 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"""	,      UpperCamelCase__		)
              # ffn -> feed_forward
              UpperCAmelCase	    =			re.sub(r"""blocks\.(\d+)\.ffn"""	,      r"""blocks.\1.feed_forward"""	,      UpperCamelCase__		)
              # 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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=None		):







       '''simple docstring'''






       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=UpperCamelCase__		)
              UpperCAmelCase	    =			len(UpperCamelCase__		)
       tokenizer.save_pretrained(UpperCamelCase__		)

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

       # 3. Download model file then convert state_dict
       UpperCAmelCase	    =			hf_hub_download(UpperCamelCase__	,      UpperCamelCase__		)
       UpperCAmelCase	    =			torch.load(UpperCamelCase__	,      map_location="""cpu"""		)
       UpperCAmelCase	    =			convert_state_dict(UpperCamelCase__		)

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

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

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

       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(UpperCamelCase__		)
              model.push_to_hub(UpperCamelCase__	,      max_shard_size="""2GB"""		)
              tokenizer.push_to_hub(UpperCamelCase__		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Union[str, 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.''',
 )

 lowerCAmelCase_	:    int            =      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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class   UpperCamelCase_ (       a_  ):
  _A       :					Optional[int]				=	['image_processor', 'tokenizer']
  _A       :					Optional[Any]				=	'ViTImageProcessor'
  _A       :					Tuple				=	('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(      self ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead.""" ,		snake_case__ ,		)
                UpperCAmelCase	    =			kwargs.pop("""feature_extractor"""				)

         UpperCAmelCase	    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""				)
         if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""				)

         super().__init__(snake_case__ ,		snake_case__				)



  def __call__(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			List[str]:



         """simple docstring"""





         if text is None and visual_prompt is None and images is None:
                raise ValueError("""You have to specify either text, visual prompt or images."""				)

         if text is not None and visual_prompt is not None:
                raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""				)

         if text is not None:
                UpperCAmelCase	    =			self.tokenizer(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if images is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None and images is not None:
                UpperCAmelCase	    =			{
                    """pixel_values""": image_features.pixel_values,
                    """conditional_pixel_values""": prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                UpperCAmelCase	    =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                UpperCAmelCase	    =			{
                    """conditional_pixel_values""": prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**snake_case__				) ,		tensor_type=snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Tuple:



         """simple docstring"""





         return self.tokenizer.batch_decode(*snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Optional[int]:



         """simple docstring"""





         return self.tokenizer.decode(*snake_case__ ,		**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,		snake_case__ ,		)
         return self.image_processor_class



  @property
  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,		snake_case__ ,		)
         return self.image_processor
