





"""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     )   ->List[str]:





  """simple docstring"""


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

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

    lowerCAmelCase__     :Tuple											=				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     )   ->Optional[int]:





  """simple docstring"""


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

  # 2. Build the config
  lowerCAmelCase__     :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__     :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__     :Dict											=				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__     :Union[str, Any]											=				hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :str											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )
  lowerCAmelCase__     :Dict											=				convert_state_dict(_SCREAMING_SNAKE_CASE     )

  # 4. Split in shards and save
  lowerCAmelCase__				,							lowerCAmelCase__     :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__     :Any											=				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__     :Dict											=				list(shards.keys()     )

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      lowerCAmelCase__     :Union[str, Any]											=				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[Any]											=				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,
  )







"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       __magic_name__	:List[str]						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					num_layers=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				DDPMScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='sample'       ,					num_train_timesteps=1_0_0_0       ,					clip_sample=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				UNetaDConditionModel(
             sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,					block_out_channels=(3_2, 6_4)       ,					attention_head_dim=(2, 4)       ,					class_embed_type='projection'       ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,					cross_attention_dim=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				AutoencoderKL()

         lowerCAmelCase__     :Optional[int]											=				{
             # prior components
             'prior_tokenizer': prior_tokenizer,
             'prior_text_encoder': prior_text_encoder,
             'prior': prior,
             'prior_scheduler': prior_scheduler,
             # image noising components
             'image_normalizer': image_normalizer,
             'image_noising_scheduler': image_noising_scheduler,
             # regular denoising components
             'tokenizer': tokenizer,
             'text_encoder': text_encoder,
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
         }

         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'prior_num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






@slow
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )

         lowerCAmelCase__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # stable unclip will oom when integration tests are run on a V100,
         # so turn on memory savings
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :List[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCAmelCase__     :int											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

         lowerCAmelCase__     :str											=				torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9
