
'''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 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  str   =		{}

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

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

        A			:  List[str]   =		value.float()

    for key, value in codebook_state_dict.items():
        A			:  Optional[Any]   =		value

    return upgrade







@torch.no_grad()
def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None		):




    '''simple docstring'''



    if config_path is not None:
        A			:  Tuple   =		FlavaConfig.from_pretrained(snake_case__		)
    else:
        A			:  str   =		FlavaConfig()

    A			:  Any   =		FlavaForPreTraining(snake_case__		).eval()

    A			:  Union[str, Any]   =		convert_dalle_checkpoint(snake_case__						,							snake_case__						,							save_checkpoint=snake_case__		)

    if os.path.exists(snake_case__		):
        A			:  List[str]   =		torch.load(snake_case__						,							map_location='''cpu'''		)
    else:
        A			:  int   =		torch.hub.load_state_dict_from_url(snake_case__						,							map_location='''cpu'''		)

    A			:  Tuple   =		upgrade_state_dict(snake_case__						,							snake_case__		)
    hf_model.load_state_dict(snake_case__		)
    A			:  Dict   =		hf_model.state_dict()
    A			:  List[Any]   =		count_parameters(snake_case__		)
    A			:  str   =		count_parameters(snake_case__		) + count_parameters(snake_case__		)

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

    hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
      lowercase     :							Dict       						=     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)






'''simple docstring'''




lowercase     :							dict[str, float]       						=     {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_02_17_66_34e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_58_18,
}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
        A			:  int   =		(
            F'Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'
            F'Valid values are: {", ".join(snake_case__		)}'
        )
        raise ValueError(snake_case__		)
    return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




