'''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 snake_case_      (					__snake_case  :							str)			->					Optional[int]:
   # 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 snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Dict)			->					Optional[Any]:
   lowerCAmelCase_			=      {}

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

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

      lowerCAmelCase_			=      value.float()

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

   return upgrade



@torch.no_grad()
def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict=None)			->					List[Any]:
   if config_path is not None:
      lowerCAmelCase_			=      FlavaConfig.from_pretrained(__snake_case)
   else:
      lowerCAmelCase_			=      FlavaConfig()

   lowerCAmelCase_			=      FlavaForPreTraining(__snake_case).eval()

   lowerCAmelCase_			=      convert_dalle_checkpoint(__snake_case	,   __snake_case	,   save_checkpoint=__snake_case)

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

   lowerCAmelCase_			=      upgrade_state_dict(__snake_case	,   __snake_case)
   hf_model.load_state_dict(__snake_case)
   lowerCAmelCase_			=      hf_model.state_dict()
   lowerCAmelCase_			=      count_parameters(__snake_case)
   lowerCAmelCase_			=      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__":
 A_     :						Union[str, Any]    							=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_     :						Union[str, Any]    							=parser.parse_args()

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






'''simple docstring'''

def snake_case_      (					__snake_case  :							int = 200)			->					int:
   lowerCAmelCase_			=      [1, 2, 5, 10, 20, 50, 100, 200]
   lowerCAmelCase_			=      [0] * (pence + 1)
   lowerCAmelCase_			=      1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(__snake_case	,   pence + 1	,   1):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(2_00) == 7_36_82





