
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        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=None		):
					'''simple docstring'''
					if config_path is not None:
										lowercase							=						FlavaConfig.from_pretrained(lowerCAmelCase__		)
					else:
										lowercase							=						FlavaConfig()

					lowercase							=						FlavaForPreTraining(lowerCAmelCase__		).eval()

					lowercase							=						convert_dalle_checkpoint(lowerCAmelCase__ ,			lowerCAmelCase__ ,			save_checkpoint=lowerCAmelCase__		)

					if os.path.exists(lowerCAmelCase__		):
										lowercase							=						torch.load(lowerCAmelCase__ ,			map_location='''cpu'''		)
					else:
										lowercase							=						torch.hub.load_state_dict_from_url(lowerCAmelCase__ ,			map_location='''cpu'''		)

					lowercase							=						upgrade_state_dict(lowerCAmelCase__ ,			lowerCAmelCase__		)
					hf_model.load_state_dict(lowerCAmelCase__		)
					lowercase							=						hf_model.state_dict()
					lowercase							=						count_parameters(lowerCAmelCase__		)
					lowercase							=						count_parameters(lowerCAmelCase__		) + count_parameters(lowerCAmelCase__		)

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

					hf_model.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
							lowercase__		:List[str]   =    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")
							lowercase__		:str   =    parser.parse_args()

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase		       =							{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EncodecModel''',
     '''EncodecPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


