





'''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


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

SCREAMING_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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


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

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

            SCREAMING_SNAKE_CASE	=		weight
      return state_dict



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None				,		_SCREAMING_SNAKE_CASE=None				,		_SCREAMING_SNAKE_CASE=False				,		_SCREAMING_SNAKE_CASE=None			)				->     Tuple:


      '''simple docstring'''


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

      # 2. Build the config
      SCREAMING_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:
                        SCREAMING_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}."""			)

      SCREAMING_SNAKE_CASE	=		RwkvConfig(
          vocab_size=__lowerCAmelCase				,		num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,		hidden_size=HIDEN_SIZE_MAPPING[size]				,		)
      config.save_pretrained(__lowerCAmelCase			)

      # 3. Download model file then convert state_dict
      SCREAMING_SNAKE_CASE	=		hf_hub_download(__lowerCAmelCase				,		__lowerCAmelCase			)
      SCREAMING_SNAKE_CASE	=		torch.load(__lowerCAmelCase				,		map_location="""cpu"""			)
      SCREAMING_SNAKE_CASE	=		convert_state_dict(__lowerCAmelCase			)

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

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

            # 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."""			)
            SCREAMING_SNAKE_CASE	=		list(shards.keys()			)

            del state_dict
            del shards
            gc.collect()

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

      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."""			)
            SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(__lowerCAmelCase			)
            model.push_to_hub(__lowerCAmelCase				,		max_shard_size="""2GB"""			)
            tokenizer.push_to_hub(__lowerCAmelCase			)


if __name__ == "__main__":
      SCREAMING_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.""",
      )

      SCREAMING_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 unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			str				=    VQModel
     __snake_case					:			Optional[Any]				=    "sample"






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Optional[Any]=(32, 32) )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		4
           SCREAMING_SNAKE_CASE	=		3

           SCREAMING_SNAKE_CASE	=		floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase__ )

           return {"sample": image}






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{
               """block_out_channels""": [32, 64],
               """in_channels""": 3,
               """out_channels""": 3,
               """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
               """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
               """latent_channels""": 3,
           }
           SCREAMING_SNAKE_CASE	=		self.dummy_input
           return init_dict, inputs_dict






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	str:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,output_loading_info=lowerCamelCase__ )
           self.assertIsNotNone(lowerCamelCase__ )
           self.assertEqual(len(loading_info["""missing_keys"""] )		,0 )

           model.to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(**self.dummy_input )

           assert image is not None, "Make sure output is not None"






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy""" )
           model.to(lowerCamelCase__ ).eval()

           torch.manual_seed(0 )
           if torch.cuda.is_available():
                 torch.cuda.manual_seed_all(0 )

           SCREAMING_SNAKE_CASE	=		torch.randn(1		,model.config.in_channels		,model.config.sample_size		,model.config.sample_size )
           SCREAMING_SNAKE_CASE	=		image.to(lowerCamelCase__ )
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ ).sample

           SCREAMING_SNAKE_CASE	=		output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           SCREAMING_SNAKE_CASE	=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
           # fmt: on
           self.assertTrue(torch.allclose(lowerCamelCase__		,lowerCamelCase__		,atol=1e-3 ) )


