




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]     ):


      '''simple docstring'''



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

      return name

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]	,							lowercase	:       Any     ):


      '''simple docstring'''



      for key in orig_state_dict.copy().keys():
            lowerCamelCase_		     =    orig_state_dict.pop(lowercase     )

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

      return orig_state_dict

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      lowerCamelCase_		     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_		     =    Image.open(requests.get(lowercase	,							stream=lowercase     ).raw     )
      return im

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


      '''simple docstring'''



      lowerCamelCase_		     =    GroupViTConfig()
      lowerCamelCase_		     =    GroupViTModel(lowercase     ).eval()

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

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

      with torch.no_grad():
            lowerCamelCase_		     =    model(**lowercase     )

      if model_name == "groupvit-gcc-yfcc":
            lowerCamelCase_		     =    torch.tensor([[13.3523, 6.3629]]     )
      elif model_name == "groupvit-gcc-redcaps":
            lowerCamelCase_		     =    torch.tensor([[16.1873, 8.6230]]     )
      else:
            raise ValueError(f"""Model name {model_name} not supported."""     )
      assert torch.allclose(outputs.logits_per_image	,							lowercase	,							atol=1e-3     )

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

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


if __name__ == "__main__":
 lowerCamelCase				:		int     =   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`.",
 )
 lowerCamelCase				:		Optional[int]     =   parser.parse_args()

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









from collections.abc import Callable

import numpy as np

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Callable	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float     ):


      '''simple docstring'''



      lowerCamelCase_		     =    int(np.ceil((x_end - xa) / step_size     )     )
      lowerCamelCase_		     =    np.zeros((n + 1,)     )
      lowerCamelCase_		     =    ya
      lowerCamelCase_		     =    xa

      for k in range(lowercase     ):
            lowerCamelCase_		     =    y[k] + step_size * ode_func(lowercase	,							y[k]     )
            lowerCamelCase_		     =    y[k] + (
                (step_size / 2) * (ode_func(lowercase	,							y[k]     ) + ode_func(x + step_size	,							lowercase     ))
            )
            x += step_size

      return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()



