


"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def 				_snake_case      (   __snake_case							:      str   ):




   """simple docstring"""







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

   return name

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]   ):




   """simple docstring"""







   for key in orig_state_dict.copy().keys():
      _lowerCamelCase	:					Tuple   =		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
         _lowerCamelCase	:					List[str]   =		key.split("""."""   )
         _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		int(key_split[2]   ), int(key_split[4]   )
         _lowerCamelCase	:					Dict   =		config.vision_config.hidden_size
         if "weight" in key:
            _lowerCamelCase	:					str   =		val[:dim, :]
            _lowerCamelCase	:					Dict   =		val[dim : dim * 2, :]
            _lowerCamelCase	:					Optional[Any]   =		val[-dim:, :]
         else:
            _lowerCamelCase	:					Union[str, Any]   =		val[:dim]
            _lowerCamelCase	:					List[str]   =		val[dim : dim * 2]
            _lowerCamelCase	:					Union[str, 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
         _lowerCamelCase	:					Tuple   =		key.split("""."""   )
         _lowerCamelCase	:					int   =		int(key_split[3]   )
         _lowerCamelCase	:					str   =		config.text_config.hidden_size
         if "weight" in key:
            _lowerCamelCase	:					List[str]   =		val[:dim, :]
            _lowerCamelCase	:					List[Any]   =		val[
                dim : dim * 2, :
            ]
            _lowerCamelCase	:					Any   =		val[-dim:, :]
         else:
            _lowerCamelCase	:					Any   =		val[:dim]
            _lowerCamelCase	:					Any   =		val[dim : dim * 2]
            _lowerCamelCase	:					Any   =		val[-dim:]
      else:
         _lowerCamelCase	:					List[str]   =		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
         ):
            _lowerCamelCase	:					List[str]   =		val.squeeze_()
         else:
            _lowerCamelCase	:					Optional[int]   =		val

   return orig_state_dict

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[Any]   =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _lowerCamelCase	:					Union[str, Any]   =		Image.open(requests.get(__snake_case						, stream=__snake_case   ).raw   )
   return im

@torch.no_grad()
def 				_snake_case      (   __snake_case							:      Optional[Any]						, __snake_case							:      Optional[Any]						, __snake_case							:      int="groupvit-gcc-yfcc"						, __snake_case							:      int=False   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		GroupViTConfig()
   _lowerCamelCase	:					Dict   =		GroupViTModel(__snake_case   ).eval()

   _lowerCamelCase	:					Tuple   =		torch.load(__snake_case						, map_location="""cpu"""   )["""model"""]
   _lowerCamelCase	:					int   =		convert_state_dict(__snake_case						, __snake_case   )
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		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
   _lowerCamelCase	:					List[Any]   =		CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""   )
   _lowerCamelCase	:					Any   =		prepare_img()
   _lowerCamelCase	:					Optional[int]   =		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():
      _lowerCamelCase	:					str   =		model(**__snake_case   )

   if model_name == "groupvit-gcc-yfcc":
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor([[13.3523, 6.3629]]   )
   elif model_name == "groupvit-gcc-redcaps":
      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([[16.1873, 8.6230]]   )
   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				=						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				=						parser.parse_args()

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





'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




