


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int = 200   ):
					__a			:       Optional[int]					    =  [1, 2, 5, 10, 20, 50, 100, 200]
					__a			:       int					    =  [0] * (pence + 1)
					__a			:       List[str]					    =  1  # base case: 1 way to make 0 pence

					for coin in coins:
										for i in range(_SCREAMING_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





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 # vision encoder
 if "img_encoder.pos_embed" in name:
  __a :  Any						=  name.replace('''img_encoder.pos_embed'''  ,   '''vision_model.embeddings.position_embeddings'''  )
 if "img_encoder.patch_embed.proj" in name:
  __a :  str						=  name.replace('''img_encoder.patch_embed.proj'''  ,   '''vision_model.embeddings.patch_embeddings.projection'''  )
 if "img_encoder.patch_embed.norm" in name:
  __a :  int						=  name.replace('''img_encoder.patch_embed.norm'''  ,   '''vision_model.embeddings.layernorm'''  )
 if "img_encoder.layers" in name:
  __a :  Union[str, Any]						=  name.replace('''img_encoder.layers'''  ,   '''vision_model.encoder.stages'''  )
 if "blocks" in name and "res" not in name:
  __a :  List[Any]						=  name.replace('''blocks'''  ,   '''layers'''  )
 if "attn" in name and "pre_assign" not in name:
  __a :  Tuple						=  name.replace('''attn'''  ,   '''self_attn'''  )
 if "proj" in name and "self_attn" in name and "text" not in name:
  __a :  List[Any]						=  name.replace('''proj'''  ,   '''out_proj'''  )
 if "pre_assign_attn.attn.proj" in name:
  __a :  Any						=  name.replace('''pre_assign_attn.attn.proj'''  ,   '''pre_assign_attn.attn.out_proj'''  )
 if "norm1" in name:
  __a :  Union[str, Any]						=  name.replace('''norm1'''  ,   '''layer_norm1'''  )
 if "norm2" in name and "pre_assign" not in name:
  __a :  Optional[int]						=  name.replace('''norm2'''  ,   '''layer_norm2'''  )
 if "img_encoder.norm" in name:
  __a :  Union[str, Any]						=  name.replace('''img_encoder.norm'''  ,   '''vision_model.layernorm'''  )
 # text encoder
 if "text_encoder.token_embedding" in name:
  __a :  List[Any]						=  name.replace('''text_encoder.token_embedding'''  ,   '''text_model.embeddings.token_embedding'''  )
 if "text_encoder.positional_embedding" in name:
  __a :  Any						=  name.replace('''text_encoder.positional_embedding'''  ,   '''text_model.embeddings.position_embedding.weight'''  )
 if "text_encoder.transformer.resblocks." in name:
  __a :  Any						=  name.replace('''text_encoder.transformer.resblocks.'''  ,   '''text_model.encoder.layers.'''  )
 if "ln_1" in name:
  __a :  str						=  name.replace('''ln_1'''  ,   '''layer_norm1'''  )
 if "ln_2" in name:
  __a :  Union[str, Any]						=  name.replace('''ln_2'''  ,   '''layer_norm2'''  )
 if "c_fc" in name:
  __a :  Union[str, Any]						=  name.replace('''c_fc'''  ,   '''fc1'''  )
 if "c_proj" in name:
  __a :  Union[str, Any]						=  name.replace('''c_proj'''  ,   '''fc2'''  )
 if "text_encoder" in name:
  __a :  Optional[int]						=  name.replace('''text_encoder'''  ,   '''text_model'''  )
 if "ln_final" in name:
  __a :  str						=  name.replace('''ln_final'''  ,   '''final_layer_norm'''  )
 # projection layers
 if "img_projector.linear_hidden." in name:
  __a :  List[str]						=  name.replace('''img_projector.linear_hidden.'''  ,   '''visual_projection.'''  )
 if "img_projector.linear_out." in name:
  __a :  str						=  name.replace('''img_projector.linear_out.'''  ,   '''visual_projection.3.'''  )
 if "text_projector.linear_hidden" in name:
  __a :  int						=  name.replace('''text_projector.linear_hidden'''  ,   '''text_projection'''  )
 if "text_projector.linear_out" in name:
  __a :  List[str]						=  name.replace('''text_projector.linear_out'''  ,   '''text_projection.3'''  )

 return name




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Tuple  ):
 for key in orig_state_dict.copy().keys():
  __a :  List[Any]						=  orig_state_dict.pop(lowerCAmelCase__  )

  if "qkv" in key:
   # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   __a :  Tuple						=  key.split('''.'''  )
   __a				,       __a :  List[Any]						=  int(key_split[2]  ), int(key_split[4]  )
   __a :  List[Any]						=  config.vision_config.hidden_size
   if "weight" in key:
    __a :  int						=  val[:dim, :]
    __a :  List[str]						=  val[dim : dim * 2, :]
    __a :  List[Any]						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  int						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  elif "in_proj" in key:
   # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   __a :  int						=  key.split('''.'''  )
   __a :  str						=  int(key_split[3]  )
   __a :  List[Any]						=  config.text_config.hidden_size
   if "weight" in key:
    __a :  List[str]						=  val[:dim, :]
    __a :  Any						=  val[
        dim : dim * 2, :
    ]
    __a :  Dict						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  Any						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  else:
   __a :  Union[str, Any]						=  rename_key(lowerCAmelCase__  )
   # squeeze if necessary
   if (
       "text_projection.0" in new_name
       or "text_projection.3" in new_name
       or "visual_projection.0" in new_name
       or "visual_projection.3" in new_name
   ):
    __a :  List[Any]						=  val.squeeze_()
   else:
    __a :  Dict						=  val

 return orig_state_dict




def    __UpperCamelCase     (				):
 __a :  Optional[int]						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a :  str						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )
 return im




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Optional[Any]="groupvit-gcc-yfcc"  ,   lowerCAmelCase__							:				int=False  ):
 __a :  Union[str, Any]						=  GroupViTConfig()
 __a :  int						=  GroupViTModel(lowerCAmelCase__  ).eval()

 __a :  Any						=  torch.load(lowerCAmelCase__  ,   map_location='''cpu'''  )['''model''']
 __a :  Optional[Any]						=  convert_state_dict(lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a				,       __a :  Dict						=  model.load_state_dict(lowerCAmelCase__  ,   strict=lowerCAmelCase__  )
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__  ) == 0)

 # verify result
 __a :  Any						=  CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''  )
 __a :  Optional[Any]						=  prepare_img()
 __a :  Optional[int]						=  processor(text=['''a photo of a cat''', '''a photo of a dog''']  ,   images=lowerCAmelCase__  ,   padding=lowerCAmelCase__  ,   return_tensors='''pt'''  )

 with torch.no_grad():
  __a :  Tuple						=  model(**lowerCAmelCase__  )

 if model_name == "groupvit-gcc-yfcc":
  __a :  List[str]						=  torch.tensor([[13.35_23, 6.36_29]]  )
 elif model_name == "groupvit-gcc-redcaps":
  __a :  List[str]						=  torch.tensor([[16.18_73, 8.62_30]]  )
 else:
  raise ValueError(f"Model name {model_name} not supported."  )
 assert torch.allclose(outputs.logits_per_image  ,   lowerCAmelCase__  ,   atol=1e-3  )

 processor.save_pretrained(lowerCAmelCase__  )
 model.save_pretrained(lowerCAmelCase__  )
 print('''Successfully saved processor and model to'''  ,   lowerCAmelCase__  )

 if push_to_hub:
  print('''Pushing to the hub...'''  )
  processor.push_to_hub(lowerCAmelCase__  ,   organization='''nielsr'''  )
  model.push_to_hub(lowerCAmelCase__  ,   organization='''nielsr'''  )


if __name__ == "__main__":
      lowercase__       =argparse.ArgumentParser()
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
      )
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
      parser.add_argument(
          '--model_name',
          default='groupvit-gccy-fcc',
          type=str,
          help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
      )
      lowercase__       =parser.parse_args()

      convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
