


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				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     # 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				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     __UpperCamelCase    							={}

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

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

          __UpperCamelCase    							=value.float()

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

     return upgrade

@torch.no_grad()
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]=None				):
     if config_path is not None:
          __UpperCamelCase    							=FlavaConfig.from_pretrained(lowercase__				)
     else:
          __UpperCamelCase    							=FlavaConfig()

     __UpperCamelCase    							=FlavaForPreTraining(lowercase__				).eval()

     __UpperCamelCase    							=convert_dalle_checkpoint(lowercase__			,     lowercase__			,     save_checkpoint=lowercase__				)

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

     __UpperCamelCase    							=upgrade_state_dict(lowercase__			,     lowercase__				)
     hf_model.load_state_dict(lowercase__				)
     __UpperCamelCase    							=hf_model.state_dict()
     __UpperCamelCase    							=count_parameters(lowercase__				)
     __UpperCamelCase    							=count_parameters(lowercase__				) + count_parameters(lowercase__				)

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

     hf_model.save_pretrained(lowercase__				)


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"""






lowercase__     						=						{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowercase__     						=						{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    from_type.lower().strip('s'		)
				_lowerCamelCase     :			List[Any]    =    to_type.lower().strip('s'		)

				_lowerCamelCase     :			Optional[int]    =    UNIT_SYMBOL.get(lowercase__							,    lowercase__		)
				_lowerCamelCase     :			Any    =    UNIT_SYMBOL.get(lowercase__							,    lowercase__		)

				if from_sanitized not in METRIC_CONVERSION:
								_lowerCamelCase     :			Tuple    =    (
								    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    f'''Conversion abbreviations are: {', '.join(lowercase__		)}'''
								)
								raise ValueError(lowercase__		)
				if to_sanitized not in METRIC_CONVERSION:
								_lowerCamelCase     :			Any    =    (
								    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    f'''Conversion abbreviations are: {', '.join(lowercase__		)}'''
								)
								raise ValueError(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    METRIC_CONVERSION[from_sanitized]
				_lowerCamelCase     :			int    =    METRIC_CONVERSION[to_sanitized]
				_lowerCamelCase     :			List[str]    =    1

				if from_exponent > to_exponent:
								_lowerCamelCase     :			List[str]    =    from_exponent - to_exponent
				else:
								_lowerCamelCase     :			List[Any]    =    -(to_exponent - from_exponent)

				return value * pow(10							,    lowercase__		)


if __name__ == "__main__":
			from doctest import testmod

			testmod()