



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


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

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





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

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

        __a		 =       weight
    return state_dict





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=None       ):
    # 1. If possible, build the tokenizer.
    if tokenizer_file is None:
        print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
        __a		 =       50277
        __a		 =       AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
    else:
        __a		 =       PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase       )
        __a		 =       len(_UpperCAmelCase       )
    tokenizer.save_pretrained(_UpperCAmelCase       )

    # 2. Build the config
    __a		 =       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		 =       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		 =       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
    __a		 =       hf_hub_download(_UpperCAmelCase     ,				_UpperCAmelCase       )
    __a		 =       torch.load(_UpperCAmelCase     ,				map_location='''cpu'''       )
    __a		 =       convert_state_dict(_UpperCAmelCase       )

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

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

        del state_dict
        del shards
        gc.collect()

        for shard_file in shard_files:
            __a		 =       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.'''       )
        __a		 =       AutoModelForCausalLM.from_pretrained(_UpperCAmelCase       )
        model.push_to_hub(_UpperCAmelCase     ,				max_shard_size='''2GB'''       )
        tokenizer.push_to_hub(_UpperCAmelCase       )


if __name__ == "__main__":
 __snake_case		:Tuple   	=  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.''',
 )

 __snake_case		: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,
 )










import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__snake_case		:List[str]   	=  HfApi()

__snake_case		:str   	=  {}
# fmt: off
__snake_case		:Optional[Any]   	=  torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
__snake_case		:Union[str, Any]   	=  torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
__snake_case		:str   	=  torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
__snake_case		:List[Any]   	=  torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
__snake_case		:Any   	=  torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
__snake_case		:List[str]   	=  torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
__snake_case		:Optional[int]   	=  torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
__snake_case		:Tuple   	=  torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
__snake_case		:List[Any]   	=  torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
__snake_case		:Optional[Any]   	=  torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
__snake_case		:Optional[Any]   	=  torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
__snake_case		:List[str]   	=  torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
__snake_case		:Any   	=  torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
__snake_case		:List[str]   	=  torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
__snake_case		:Union[str, Any]   	=  torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

__snake_case		:List[Any]   	=  api.list_models(filter='''diffusers''')
for mod in models:
 if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
  __snake_case		:List[str]   	=  '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

  print(f'Started running {mod.modelId}!!!')

  if mod.modelId.startswith('''CompVis'''):
   __snake_case		:Optional[int]   	=  UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
  else:
   __snake_case		:str   	=  UNetaDModel.from_pretrained(local_checkpoint)

  torch.manual_seed(0)
  random.seed(0)

  __snake_case		:List[Any]   	=  torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
  __snake_case		:List[Any]   	=  torch.tensor([10] * noise.shape[0])
  with torch.no_grad():
   __snake_case		:Any   	=  model(noise, time_step).sample

  assert torch.allclose(
      logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
  )
  print(f'{mod.modelId} has passed successfully!!!')





