




import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output






import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    CLIPSegTextConfig()
  _lowercase: Optional[Any]						     =    CLIPSegVisionConfig(patch_size=16							)

  _lowercase: Union[str, Any]						     =    True if 'refined' in model_name else False
  _lowercase: str						     =    16 if 'rd16' in model_name else 64

  _lowercase: Any						     =    CLIPSegConfig.from_text_vision_configs(
      UpperCamelCase					,     UpperCamelCase					,     use_complex_transposed_convolution=UpperCamelCase					,     reduce_dim=UpperCamelCase					,     )
  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  if "clip_model" in name:
    _lowercase: Tuple						     =    name.replace('clip_model'					,     'clip'							)
  if "transformer" in name:
    if "visual" in name:
      _lowercase: Union[str, Any]						     =    name.replace('visual.transformer'					,     'vision_model'							)
    else:
      _lowercase: Optional[int]						     =    name.replace('transformer'					,     'text_model'							)
  if "resblocks" in name:
    _lowercase: List[Any]						     =    name.replace('resblocks'					,     'encoder.layers'							)
  if "ln_1" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_1'					,     'layer_norm1'							)
  if "ln_2" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_2'					,     'layer_norm2'							)
  if "c_fc" in name:
    _lowercase: str						     =    name.replace('c_fc'					,     'fc1'							)
  if "c_proj" in name:
    _lowercase: List[Any]						     =    name.replace('c_proj'					,     'fc2'							)
  if "attn" in name and "self" not in name:
    _lowercase: Dict						     =    name.replace('attn'					,     'self_attn'							)
  # text encoder
  if "token_embedding" in name:
    _lowercase: Optional[int]						     =    name.replace('token_embedding'					,     'text_model.embeddings.token_embedding'							)
  if "positional_embedding" in name and "visual" not in name:
    _lowercase: int						     =    name.replace('positional_embedding'					,     'text_model.embeddings.position_embedding.weight'							)
  if "ln_final" in name:
    _lowercase: Optional[Any]						     =    name.replace('ln_final'					,     'text_model.final_layer_norm'							)
  # vision encoder
  if "visual.class_embedding" in name:
    _lowercase: Union[str, Any]						     =    name.replace('visual.class_embedding'					,     'vision_model.embeddings.class_embedding'							)
  if "visual.conv1" in name:
    _lowercase: int						     =    name.replace('visual.conv1'					,     'vision_model.embeddings.patch_embedding'							)
  if "visual.positional_embedding" in name:
    _lowercase: List[Any]						     =    name.replace('visual.positional_embedding'					,     'vision_model.embeddings.position_embedding.weight'							)
  if "visual.ln_pre" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_pre'					,     'vision_model.pre_layrnorm'							)
  if "visual.ln_post" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_post'					,     'vision_model.post_layernorm'							)
  # projection layers
  if "visual.proj" in name:
    _lowercase: List[str]						     =    name.replace('visual.proj'					,     'visual_projection.weight'							)
  if "text_projection" in name:
    _lowercase: Any						     =    name.replace('text_projection'					,     'text_projection.weight'							)
  # decoder
  if "trans_conv" in name:
    _lowercase: List[Any]						     =    name.replace('trans_conv'					,     'transposed_convolution'							)
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    _lowercase: Any						     =    'decoder.' + name
  if "blocks" in name:
    _lowercase: Optional[int]						     =    name.replace('blocks'					,     'decoder.layers'							)
  if "linear1" in name:
    _lowercase: Optional[Any]						     =    name.replace('linear1'					,     'mlp.fc1'							)
  if "linear2" in name:
    _lowercase: int						     =    name.replace('linear2'					,     'mlp.fc2'							)
  if "norm1" in name and "layer_" not in name:
    _lowercase: Optional[int]						     =    name.replace('norm1'					,     'layer_norm1'							)
  if "norm2" in name and "layer_" not in name:
    _lowercase: Tuple						     =    name.replace('norm2'					,     'layer_norm2'							)

  return name


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    _lowercase: int						     =    orig_state_dict.pop(UpperCamelCase							)

    if key.startswith('clip_model'							) and "attn.in_proj" in key:
      _lowercase: Union[str, Any]						     =    key.split('.'							)
      if "visual" in key:
        _lowercase: List[str]						     =    int(key_split[4]							)
        _lowercase: Dict						     =    config.vision_config.hidden_size
        _lowercase: Union[str, Any]						     =    'vision_model'
      else:
        _lowercase: str						     =    int(key_split[3]							)
        _lowercase: Optional[Any]						     =    config.text_config.hidden_size
        _lowercase: List[str]						     =    'text_model'

      if "weight" in key:
        _lowercase: Optional[int]						     =    val[:dim, :]
        _lowercase: List[Any]						     =    val[
            dim : dim * 2, :
        ]
        _lowercase: Tuple						     =    val[-dim:, :]
      else:
        _lowercase: Any						     =    val[:dim]
        _lowercase: List[str]						     =    val[dim : dim * 2]
        _lowercase: Optional[int]						     =    val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      _lowercase: str						     =    key.split('.'							)
      _lowercase: Union[str, Any]						     =    int(key_split[1]							)
      _lowercase: List[str]						     =    config.reduce_dim
      if "weight" in key:
        _lowercase: Union[str, Any]						     =    val[:dim, :]
        _lowercase: Tuple						     =    val[dim : dim * 2, :]
        _lowercase: Union[str, Any]						     =    val[-dim:, :]
      else:
        _lowercase: Union[str, Any]						     =    val[:dim]
        _lowercase: Dict						     =    val[dim : dim * 2]
        _lowercase: Any						     =    val[-dim:]
    else:
      _lowercase: Dict						     =    rename_key(UpperCamelCase							)
      if "visual_projection" in new_name or "text_projection" in new_name:
        _lowercase: str						     =    val.T
      _lowercase: Tuple						     =    val

  return orig_state_dict


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: str						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return image


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    get_clipseg_config(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    CLIPSegForImageSegmentation(UpperCamelCase							)
  model.eval()

  _lowercase: Dict						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith('model'							):
      state_dict.pop(UpperCamelCase					,     UpperCamelCase							)

    # rename some keys
  _lowercase: List[str]						     =    convert_state_dict(UpperCamelCase					,     UpperCamelCase							)
  _lowercase     ,					_lowercase: List[Any]						     =    model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase							)							)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(F'Unexpected keys: {unexpected_keys}'							)

  _lowercase: Tuple						     =    ViTImageProcessor(size=352							)
  _lowercase: Union[str, Any]						     =    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
  _lowercase: Optional[Any]						     =    CLIPSegProcessor(image_processor=UpperCamelCase					,     tokenizer=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    prepare_img()
  _lowercase: List[Any]						     =    ['a glass', 'something to fill', 'wood', 'a jar']

  _lowercase: str						     =    processor(text=UpperCamelCase					,     images=[image] * len(UpperCamelCase							)					,     padding='max_length'					,     return_tensors='pt'							)

  with torch.no_grad():
    _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)

  # verify values
  _lowercase: int						     =    torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
  _lowercase: Tuple						     =    torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
  if model_name == "clipseg-rd64-refined":
    _lowercase: str						     =    torch.tensor(
        [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]							)
  elif model_name == "clipseg-rd64":
    _lowercase: Dict						     =    torch.tensor(
        [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]							)
  elif model_name == "clipseg-rd16":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]							)
  else:
    raise ValueError(F'Model name {model_name} not supported.'							)

  assert torch.allclose(outputs.logits[0, :3, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.pooled_output[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  print('Looks ok!'							)

  if pytorch_dump_folder_path is not None:
    print(F'Saving model and processor to {pytorch_dump_folder_path}'							)
    model.save_pretrained(UpperCamelCase							)
    processor.save_pretrained(UpperCamelCase							)

  if push_to_hub:
    print(F'Pushing model and processor for {model_name} to the hub'							)
    model.push_to_hub(F'CIDAS/{model_name}'							)
    processor.push_to_hub(F'CIDAS/{model_name}'							)


if __name__ == "__main__":
   UpperCAmelCase__						:       int							=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='clipseg-rd64',
       type=str,
       choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
       help=(
           'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
           ' reduce dimension)'
       ),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
       type=str,
       help=(
           'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
           ' the decoder weights.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   UpperCAmelCase__						:       str							=    parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
