'''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_      (       SCREAMING_SNAKE_CASE_						)			->Tuple:
  # 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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[Any]:
  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_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None						)			->Union[str, Any]:
  if config_path is not None:
    lowercase_     =       FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       FlavaConfig()

  lowercase_     =       FlavaForPreTraining(SCREAMING_SNAKE_CASE_						).eval()

  lowercase_     =       convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	save_checkpoint=SCREAMING_SNAKE_CASE_						)

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

  lowercase_     =       upgrade_state_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       hf_model.state_dict()
  lowercase_     =       count_parameters(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       count_parameters(SCREAMING_SNAKE_CASE_						) + count_parameters(SCREAMING_SNAKE_CASE_						)

  assert torch.allclose(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	atol=1e-3						)

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		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""")
       __snake_case						  =		parser.parse_args()

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


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GitForCausalLM""",
           """GitModel""",
           """GitPreTrainedModel""",
           """GitVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
       from .processing_git import GitProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_git import (
                  GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GitForCausalLM,
                  GitModel,
                  GitPreTrainedModel,
                  GitVisionModel,
              )

else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

