





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def     lowerCamelCase__			(__lowerCamelCase	):




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

     return name






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):




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

     return orig_state_dict






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
     _SCREAMING_SNAKE_CASE       :	Any						    =							Image.open(requests.get(_lowerCAmelCase,       stream=_lowerCAmelCase	).raw	)
     return im






@torch.no_grad()
def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase="groupvit-gcc-yfcc",       __lowerCamelCase=False	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							GroupViTConfig()
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							GroupViTModel(_lowerCAmelCase	).eval()

     _SCREAMING_SNAKE_CASE       :	Any						    =							torch.load(_lowerCAmelCase,       map_location="cpu"	)["model"]
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							convert_state_dict(_lowerCAmelCase,       _lowerCAmelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							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
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							prepare_img()
     _SCREAMING_SNAKE_CASE       :	Any						    =							processor(text=["a photo of a cat", "a photo of a dog"],       images=_lowerCAmelCase,       padding=_lowerCAmelCase,       return_tensors="pt"	)

     with torch.no_grad():
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(**_lowerCAmelCase	)

     if model_name == "groupvit-gcc-yfcc":
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							torch.tensor([[13.3523, 6.3629]]	)
     elif model_name == "groupvit-gcc-redcaps":
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.tensor([[16.1873, 8.6230]]	)
     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__":
       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)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)