

"""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  (  UpperCamelCase_					:	Dict)     ->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	List[str])     ->						Dict:
   '''simple docstring'''


   __lowercase								=						{}

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

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

      __lowercase								=						value.float()

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

   return upgrade




@torch.no_grad()
def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Any=None)     ->						Dict:
   '''simple docstring'''


   if config_path is not None:
      __lowercase								=						FlavaConfig.from_pretrained(UpperCamelCase_)
   else:
      __lowercase								=						FlavaConfig()

   __lowercase								=						FlavaForPreTraining(UpperCamelCase_).eval()

   __lowercase								=						convert_dalle_checkpoint(UpperCamelCase_,      UpperCamelCase_,      save_checkpoint=UpperCamelCase_)

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

   __lowercase								=						upgrade_state_dict(UpperCamelCase_,      UpperCamelCase_)
   hf_model.load_state_dict(UpperCamelCase_)
   __lowercase								=						hf_model.state_dict()
   __lowercase								=						count_parameters(UpperCamelCase_)
   __lowercase								=						count_parameters(UpperCamelCase_) + count_parameters(UpperCamelCase_)

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

   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a							=							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')
   _a							=							parser.parse_args()

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







"""simple docstring"""





from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor




