





"""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        lowercase__     (       lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

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




def        lowercase__     (       lowercase_		,lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							{}

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

            _UpperCamelCase   :						Optional[Any]    =							key.replace("heads.cmd.mim_head.cls.predictions"		,"mmm_image_head"      )
            _UpperCamelCase   :						Optional[Any]    =							key.replace("heads.cmd.mlm_head.cls.predictions"		,"mmm_text_head"      )
            _UpperCamelCase   :						Optional[Any]    =							key.replace("heads.cmd.itm_head.cls"		,"itm_head"      )
            _UpperCamelCase   :						Tuple    =							key.replace("heads.cmd.itm_head.pooler"		,"itm_head.pooler"      )
            _UpperCamelCase   :						Optional[Any]    =							key.replace("heads.cmd.clip_head.logit_scale"		,"flava.logit_scale"      )
            _UpperCamelCase   :						Optional[int]    =							key.replace("heads.fairseq_mlm.cls.predictions"		,"mlm_head"      )
            _UpperCamelCase   :						List[Any]    =							key.replace("heads.imagenet.mim_head.cls.predictions"		,"mim_head"      )
            _UpperCamelCase   :						int    =							key.replace("mm_text_projection"		,"flava.text_to_mm_projection"      )
            _UpperCamelCase   :						Optional[Any]    =							key.replace("mm_image_projection"		,"flava.image_to_mm_projection"      )
            _UpperCamelCase   :						Optional[Any]    =							key.replace("image_encoder.module"		,"flava.image_model"      )
            _UpperCamelCase   :						Any    =							key.replace("text_encoder.module"		,"flava.text_model"      )
            _UpperCamelCase   :						Optional[Any]    =							key.replace("mm_encoder.module.encoder.cls_token"		,"flava.multimodal_model.cls_token"      )
            _UpperCamelCase   :						Tuple    =							key.replace("mm_encoder.module"		,"flava.multimodal_model"      )
            _UpperCamelCase   :						Any    =							key.replace("text_projection"		,"flava.text_projection"      )
            _UpperCamelCase   :						List[Any]    =							key.replace("image_projection"		,"flava.image_projection"      )

            _UpperCamelCase   :						str    =							value.float()

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

      return upgrade




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=None      )       ->		Union[str, Any]:




      """simple docstring"""

      if config_path is not None:
            _UpperCamelCase   :						int    =							FlavaConfig.from_pretrained(lowercase_      )
      else:
            _UpperCamelCase   :						Optional[int]    =							FlavaConfig()

      _UpperCamelCase   :						List[Any]    =							FlavaForPreTraining(lowercase_      ).eval()

      _UpperCamelCase   :						Dict    =							convert_dalle_checkpoint(lowercase_		,lowercase_		,save_checkpoint=lowercase_      )

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

      _UpperCamelCase   :						int    =							upgrade_state_dict(lowercase_		,lowercase_      )
      hf_model.load_state_dict(lowercase_      )
      _UpperCamelCase   :						Optional[int]    =							hf_model.state_dict()
      _UpperCamelCase   :						Optional[int]    =							count_parameters(lowercase_      )
      _UpperCamelCase   :						Any    =							count_parameters(lowercase_      ) + count_parameters(lowercase_      )

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

      hf_model.save_pretrained(lowercase_      )


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

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


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__     :		Tuple        = {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		Optional[int]        = [
         """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MgpstrModel""",
         """MgpstrPreTrainedModel""",
         """MgpstrForSceneTextRecognition""",
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     lowerCamelCase__     :		List[Any]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






