



'''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


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

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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=list(state_dict.keys()       )
     for name in state_dict_keys:
          a_       		=state_dict.pop(lowercase__       )
          # 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"       ,	lowercase__       )
          # ffn -> feed_forward
          a_       		=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"       ):
               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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=None       ,	lowercase__=None       ,	lowercase__=False       ,	lowercase__=None       ):


     '''simple docstring'''







     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."       )
          a_       		=5_0_2_7_7
          a_       		=AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"       )
     else:
          a_       		=PreTrainedTokenizerFast(tokenizer_file=lowercase__       )
          a_       		=len(lowercase__       )
     tokenizer.save_pretrained(lowercase__       )

     # 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=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
     a_       		=hf_hub_download(lowercase__       ,	lowercase__       )
     a_       		=torch.load(lowercase__       ,	map_location="cpu"       )
     a_       		=convert_state_dict(lowercase__       )

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

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

          del state_dict
          del shards
          gc.collect()

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


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

  lowercase							       =			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 torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


