




'''simple docstring'''





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Union[List[PIL.Image.Image], np.ndarray]
     SCREAMING_SNAKE_CASE_							:   Optional[List[bool]]


if is_transformers_available() and is_torch_available():
   from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline








'''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_						:      int       =						{
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

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





def      __a     (       _UpperCamelCase:   str   )						->			Any:

     """simple docstring"""




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

          if name != "head.weight":
               _snake_case							=				"rwkv." + name

          _snake_case							=				weight
     return state_dict





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   str=None ,						_UpperCamelCase:   Optional[Any]=None ,						_UpperCamelCase:   List[str]=False ,						_UpperCamelCase:   Dict=None   )						->			Dict:

     """simple docstring"""




     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
          _snake_case							=				50_277
          _snake_case							=				AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
     else:
          _snake_case							=				PreTrainedTokenizerFast(tokenizer_file=_UpperCamelCase   )
          _snake_case							=				len(_UpperCamelCase   )
     tokenizer.save_pretrained(_UpperCamelCase   )

     # 2. Build the config
     _snake_case							=				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:
                    _snake_case							=				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}."""   )

     _snake_case							=				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
     _snake_case							=				hf_hub_download(_UpperCamelCase ,						_UpperCamelCase   )
     _snake_case							=				torch.load(_UpperCamelCase ,						map_location="cpu"   )
     _snake_case							=				convert_state_dict(_UpperCamelCase   )

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

     if index is not None:
          _snake_case							=				os.path.join(_UpperCamelCase ,						_UpperCamelCase   )
          # Save the index as well
          with open(_UpperCamelCase ,						"w" ,						encoding="utf-8"   ) as f:
               _snake_case							=				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."   )
          _snake_case							=				list(shards.keys()   )

          del state_dict
          del shards
          gc.collect()

          for shard_file in shard_files:
               _snake_case							=				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."   )
          _snake_case							=				AutoModelForCausalLM.from_pretrained(_UpperCamelCase   )
          model.push_to_hub(_UpperCamelCase ,						max_shard_size="2GB"   )
          tokenizer.push_to_hub(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      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.''',
   )

   UpperCamelCase_						:      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,
   )


