
"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






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

 return name






def 							A						(    snake_case				:Optional[int]    , snake_case				:Tuple		)				->     Tuple:
 for key in orig_state_dict.copy().keys():
  __UpperCamelCase      =	orig_state_dict.pop(snake_case		)

  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
   __UpperCamelCase      =	key.split('.'		)
   __UpperCamelCase  ,     __UpperCamelCase      =	int(key_split[2]		), int(key_split[4]		)
   __UpperCamelCase      =	config.vision_config.hidden_size
   if "weight" in key:
    __UpperCamelCase      =	val[:dim, :]
    __UpperCamelCase      =	val[dim : dim * 2, :]
    __UpperCamelCase      =	val[-dim:, :]
   else:
    __UpperCamelCase      =	val[:dim]
    __UpperCamelCase      =	val[dim : dim * 2]
    __UpperCamelCase      =	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
   __UpperCamelCase      =	key.split('.'		)
   __UpperCamelCase      =	int(key_split[3]		)
   __UpperCamelCase      =	config.text_config.hidden_size
   if "weight" in key:
    __UpperCamelCase      =	val[:dim, :]
    __UpperCamelCase      =	val[
        dim : dim * 2, :
    ]
    __UpperCamelCase      =	val[-dim:, :]
   else:
    __UpperCamelCase      =	val[:dim]
    __UpperCamelCase      =	val[dim : dim * 2]
    __UpperCamelCase      =	val[-dim:]
  else:
   __UpperCamelCase      =	rename_key(snake_case		)
   # 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
   ):
    __UpperCamelCase      =	val.squeeze_()
   else:
    __UpperCamelCase      =	val

 return orig_state_dict






def 							A						(    )				->     Dict:
 __UpperCamelCase      =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase      =	Image.open(requests.get(snake_case    , stream=snake_case		).raw		)
 return im






@torch.no_grad()
def 							A						(    snake_case				:Tuple    , snake_case				:Optional[int]    , snake_case				:str="groupvit-gcc-yfcc"    , snake_case				:Optional[int]=False		)				->     List[str]:
 __UpperCamelCase      =	GroupViTConfig()
 __UpperCamelCase      =	GroupViTModel(snake_case		).eval()

 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	convert_state_dict(snake_case    , snake_case		)
 __UpperCamelCase  ,     __UpperCamelCase      =	model.load_state_dict(snake_case    , strict=snake_case		)
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(snake_case		) == 0)

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

 with torch.no_grad():
  __UpperCamelCase      =	model(**snake_case		)

 if model_name == "groupvit-gcc-yfcc":
  __UpperCamelCase      =	torch.tensor([[13.3_523, 6.3_629]]		)
 elif model_name == "groupvit-gcc-redcaps":
  __UpperCamelCase      =	torch.tensor([[16.1_873, 8.6_230]]		)
 else:
  raise ValueError(f'Model name {model_name} not supported.'		)
 assert torch.allclose(outputs.logits_per_image    , snake_case    , atol=1e-3		)

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

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


if __name__ == "__main__":
      UpperCamelCase     :    List[str]       =			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`.",
      )
      UpperCamelCase     :    List[str]       =			parser.parse_args()

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





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 __UpperCamelCase      =	0
 while n > 0:
  res += n % 1_0
  n //= 1_0
 return res






def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0		)






def 							A						(    snake_case				:int		)				->     int:
 return sum(int(snake_case		) for c in str(abs(snake_case		)		)		)






def 							A						(    )				->     None:
 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(snake_case				:Callable    , snake_case				:int		) -> None:
  __UpperCamelCase      =	f'{func.__name__}({value})'
  __UpperCamelCase      =	timeit(f'__main__.{call}'    , setup='import __main__'		)
  print(f'{call:56} = {func(snake_case		)} -- {timing:.4f} seconds'		)

 for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(snake_case    , snake_case		)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



