





"""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


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

__A											=		{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}
def  __A   (_SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:Dict											=							list(state_dict.keys()			)
  for name in state_dict_keys:
    lowerCAmelCase__			:Optional[int]											=							state_dict.pop(_SCREAMING_SNAKE_CASE			)
    # emb -> embedding
    if name.startswith('emb.'			):
      lowerCAmelCase__			:Tuple											=							name.replace('emb.'     ,   'embeddings.'			)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith('blocks.0.ln0'			):
      lowerCAmelCase__			:int											=							name.replace('blocks.0.ln0'     ,   'blocks.0.pre_ln'			)
    # att -> attention
    lowerCAmelCase__			:List[Any]											=							re.sub(r'blocks\.(\d+)\.att'     ,   r'blocks.\1.attention'     ,   _SCREAMING_SNAKE_CASE			)
    # ffn -> feed_forward
    lowerCAmelCase__			:Optional[Any]											=							re.sub(r'blocks\.(\d+)\.ffn'     ,   r'blocks.\1.feed_forward'     ,   _SCREAMING_SNAKE_CASE			)
    # time_mix_k -> time_mix_key and reshape
    if name.endswith('.time_mix_k'			):
      lowerCAmelCase__			:Optional[int]											=							name.replace('.time_mix_k'     ,   '.time_mix_key'			)
    # time_mix_v -> time_mix_value and reshape
    if name.endswith('.time_mix_v'			):
      lowerCAmelCase__			:Union[str, Any]											=							name.replace('.time_mix_v'     ,   '.time_mix_value'			)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith('.time_mix_r'			):
      lowerCAmelCase__			:Tuple											=							name.replace('.time_mix_r'     ,   '.time_mix_receptance'			)

    if name != "head.weight":
      lowerCAmelCase__			:Union[str, Any]											=							'''rwkv.''' + name

    lowerCAmelCase__			:Dict											=							weight
  return state_dict
def  __A   (_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			)					->List[str]:





  """simple docstring"""


  if tokenizer_file is None:
    print('No `--tokenizer_file` provided, we will use the default tokenizer.'			)
    lowerCAmelCase__			:List[str]											=							5_0277
    lowerCAmelCase__			:str											=							AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'			)
  else:
    lowerCAmelCase__			:Optional[int]											=							PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:str											=							len(_SCREAMING_SNAKE_CASE			)
  tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE			)

  # 2. Build the config
  lowerCAmelCase__			:List[Any]											=							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:
        lowerCAmelCase__			:List[Any]											=							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}."			)

  lowerCAmelCase__			:Optional[int]											=							RwkvConfig(
      vocab_size=_SCREAMING_SNAKE_CASE     ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,   hidden_size=HIDEN_SIZE_MAPPING[size]     ,   )
  config.save_pretrained(_SCREAMING_SNAKE_CASE			)

  # 3. Download model file then convert state_dict
  lowerCAmelCase__			:Optional[Any]											=							hf_hub_download(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Optional[Any]											=							torch.load(_SCREAMING_SNAKE_CASE     ,   map_location='cpu'			)
  lowerCAmelCase__			:Tuple											=							convert_state_dict(_SCREAMING_SNAKE_CASE			)

  # 4. Split in shards and save
  lowerCAmelCase__			:Optional[Any]											=							shard_checkpoint(_SCREAMING_SNAKE_CASE			)
  for shard_file, shard in shards.items():
    torch.save(_SCREAMING_SNAKE_CASE     ,   os.path.join(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)			)

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

    # 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.'			)
    lowerCAmelCase__			:List[Any]											=							list(shards.keys()			)

    del state_dict
    del shards
    gc.collect()

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

  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.'			)
    lowerCAmelCase__			:Optional[int]											=							AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE			)
    model.push_to_hub(_SCREAMING_SNAKE_CASE     ,   max_shard_size='2GB'			)
    tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE			)


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

  __A											=		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 numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



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




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[str]		     =					UNetaDModel(
             block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



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




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



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




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







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




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2




