

'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def    a__       (  a__						):




     """simple docstring"""


     return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()						)







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.cmd.mim_head.cls.predictions"""      ,    """mmm_image_head"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.cmd.mlm_head.cls.predictions"""      ,    """mmm_text_head"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.cmd.itm_head.cls"""      ,    """itm_head"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.cmd.itm_head.pooler"""      ,    """itm_head.pooler"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.cmd.clip_head.logit_scale"""      ,    """flava.logit_scale"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.fairseq_mlm.cls.predictions"""      ,    """mlm_head"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""heads.imagenet.mim_head.cls.predictions"""      ,    """mim_head"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""mm_text_projection"""      ,    """flava.text_to_mm_projection"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""mm_image_projection"""      ,    """flava.image_to_mm_projection"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""image_encoder.module"""      ,    """flava.image_model"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""text_encoder.module"""      ,    """flava.text_model"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""mm_encoder.module.encoder.cls_token"""      ,    """flava.multimodal_model.cls_token"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""mm_encoder.module"""      ,    """flava.multimodal_model"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""text_projection"""      ,    """flava.text_projection"""						)
          __SCREAMING_SNAKE_CASE     =      key.replace("""image_projection"""      ,    """flava.image_projection"""						)

          __SCREAMING_SNAKE_CASE     =      value.float()

     for key, value in codebook_state_dict.items():
          __SCREAMING_SNAKE_CASE     =      value

     return upgrade







@torch.no_grad()
def    a__       (  a__      ,    a__      ,    a__      ,    a__=None						):




     """simple docstring"""


     if config_path is not None:
          __SCREAMING_SNAKE_CASE     =      FlavaConfig.from_pretrained(a__						)
     else:
          __SCREAMING_SNAKE_CASE     =      FlavaConfig()

     __SCREAMING_SNAKE_CASE     =      FlavaForPreTraining(a__						).eval()

     __SCREAMING_SNAKE_CASE     =      convert_dalle_checkpoint(a__      ,    a__      ,    save_checkpoint=a__						)

     if os.path.exists(a__						):
          __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    map_location="""cpu"""						)
     else:
          __SCREAMING_SNAKE_CASE     =      torch.hub.load_state_dict_from_url(a__      ,    map_location="""cpu"""						)

     __SCREAMING_SNAKE_CASE     =      upgrade_state_dict(a__      ,    a__						)
     hf_model.load_state_dict(a__						)
     __SCREAMING_SNAKE_CASE     =      hf_model.state_dict()
     __SCREAMING_SNAKE_CASE     =      count_parameters(a__						)
     __SCREAMING_SNAKE_CASE     =      count_parameters(a__						) + count_parameters(a__						)

     assert torch.allclose(a__      ,    a__      ,    atol=1E-3						)

     hf_model.save_pretrained(a__						)


if __name__ == "__main__":
  UpperCAmelCase :       Optional[Any]			   =     argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
  parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  UpperCAmelCase :       Any			   =     parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)









'''simple docstring'''



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
  import jax







@require_flax
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      FlaxAutoencoderKL
 @property
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      4
      __SCREAMING_SNAKE_CASE     =      3
      __SCREAMING_SNAKE_CASE     =      (32, 32)

      __SCREAMING_SNAKE_CASE     =      jax.random.PRNGKey(0			)
      __SCREAMING_SNAKE_CASE     =      jax.random.uniform(__SCREAMING_SNAKE_CASE					,							((batch_size, num_channels) + sizes)			)

      return {"sample": image, "prng_key": prng_key}


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


      """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""": 4,
      }
      __SCREAMING_SNAKE_CASE     =      self.dummy_input
      return init_dict, inputs_dict






