


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

__snake_case   							=						{
    '''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 		_A		(		_lowercase )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			list(state_dict.keys() )
     for name in state_dict_keys:
          __UpperCamelCase =			state_dict.pop(_lowercase )
          # 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'					,  _lowercase )
          # ffn -> feed_forward
          __UpperCamelCase =			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' ):
               __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 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None					,  _lowercase=False					,  _lowercase=None )					-> Any:


     """simple docstring"""


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

     # 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=_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
     __UpperCamelCase =			hf_hub_download(_lowercase					,  _lowercase )
     __UpperCamelCase =			torch.load(_lowercase					,  map_location='cpu' )
     __UpperCamelCase =			convert_state_dict(_lowercase )

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

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

          del state_dict
          del shards
          gc.collect()

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


if __name__ == "__main__":
    __snake_case   							=						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   							=						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 argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()

    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

    parser.add_argument(
        '''--txt2img_unclip''',
        default='''kakaobrain/karlo-v1-alpha''',
        type=str,
        required=False,
        help='''The pretrained txt2img unclip.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)


