




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

lowerCamelCase							:						Tuple					       =  {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[Any]	):


      '''simple docstring'''



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

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

            lowerCamelCase_							=       weight
      return state_dict

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict      ,				lowercase      :			Optional[int]      ,				lowercase      :			Union[str, Any]      ,				lowercase      :			int=None      ,				lowercase      :			str=None      ,				lowercase      :			Any=False      ,				lowercase      :			Tuple=None	):


      '''simple docstring'''



      if tokenizer_file is None:
            print('No `--tokenizer_file` provided, we will use the default tokenizer.'	)
            lowerCamelCase_							=       5_02_77
            lowerCamelCase_							=       AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'	)
      else:
            lowerCamelCase_							=       PreTrainedTokenizerFast(tokenizer_file=lowercase	)
            lowerCamelCase_							=       len(lowercase	)
      tokenizer.save_pretrained(lowercase	)

      # 2. Build the config
      lowerCamelCase_							=       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_							=       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_							=       RwkvConfig(
          vocab_size=lowercase      ,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]      ,				hidden_size=HIDEN_SIZE_MAPPING[size]      ,				)
      config.save_pretrained(lowercase	)

      # 3. Download model file then convert state_dict
      lowerCamelCase_							=       hf_hub_download(lowercase      ,				lowercase	)
      lowerCamelCase_							=       torch.load(lowercase      ,				map_location='cpu'	)
      lowerCamelCase_							=       convert_state_dict(lowercase	)

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

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

            # 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_							=       list(shards.keys()	)

            del state_dict
            del shards
            gc.collect()

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

      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_							=       AutoModelForCausalLM.from_pretrained(lowercase	)
            model.push_to_hub(lowercase      ,				max_shard_size='2GB'	)
            tokenizer.push_to_hub(lowercase	)


if __name__ == "__main__":
 lowerCamelCase							:						List[str]					       =  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							:						Optional[Any]					       =  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_tensorflow_text_available, is_torch_available


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

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




