'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      List[str]:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : List[str]			)    ->      Union[str, Any]:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					)    ->      Any:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : List[Any]					,    snake_case_       : Optional[Any]="groupvit-gcc-yfcc"					,    snake_case_       : List[str]=False			)    ->      Any:


     '''simple docstring'''




     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([[13.3523, 6.3629]]			)
     elif model_name == "groupvit-gcc-redcaps":
          UpperCAmelCase_           =   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__":
    SCREAMING_SNAKE_CASE_:     Dict  =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`.',
    )
    SCREAMING_SNAKE_CASE_:     Optional[int]  =parser.parse_args()

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











"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result