


'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

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






def    lowerCAmelCase__ (       lowerCamelCase  :    Optional[Any]     ):
       _A  :			Union[str, Any]											=      CLIPSegTextConfig()
       _A  :			List[Any]											=      CLIPSegVisionConfig(patch_size=16     )

       _A  :			int											=      True if 'refined' in model_name else False
       _A  :			str											=      16 if 'rd16' in model_name else 64

       _A  :			str											=      CLIPSegConfig.from_text_vision_configs(
           lowerCamelCase						,lowerCamelCase						,use_complex_transposed_convolution=lowerCamelCase						,reduce_dim=lowerCamelCase						,)
       return config






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

       return name






def    lowerCAmelCase__ (       lowerCamelCase  :    Any						,lowerCamelCase  :    Any     ):
       for key in orig_state_dict.copy().keys():
              _A  :			List[str]											=      orig_state_dict.pop(lowerCamelCase     )

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

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

       return orig_state_dict






def    lowerCAmelCase__ (       ):
       _A  :			Dict											=      'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A  :			List[str]											=      Image.open(requests.get(lowerCamelCase						,stream=lowerCamelCase     ).raw     )
       return image






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    List[Any]						,lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    Dict     ):
       _A  :			Tuple											=      get_clipseg_config(lowerCamelCase     )
       _A  :			Any											=      CLIPSegForImageSegmentation(lowerCamelCase     )
       model.eval()

       _A  :			List[str]											=      torch.load(lowerCamelCase						,map_location='cpu'     )

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

    # rename some keys
       _A  :			Dict											=      convert_state_dict(lowerCamelCase						,lowerCamelCase     )
       _A						,				_A  :			int											=      model.load_state_dict(lowerCamelCase						,strict=lowerCamelCase     )

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

       _A  :			List[Any]											=      ViTImageProcessor(size=352     )
       _A  :			Optional[int]											=      CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     )
       _A  :			Tuple											=      CLIPSegProcessor(image_processor=lowerCamelCase						,tokenizer=lowerCamelCase     )

       _A  :			Optional[Any]											=      prepare_img()
       _A  :			Dict											=      ['a glass', 'something to fill', 'wood', 'a jar']

       _A  :			Any											=      processor(text=lowerCamelCase						,images=[image] * len(lowerCamelCase     )						,padding='max_length'						,return_tensors='pt'     )

       with torch.no_grad():
              _A  :			Any											=      model(**lowerCamelCase     )

       # verify values
       _A  :			Union[str, Any]											=      torch.tensor([0.1_110, -0.1_882, 0.1_645]     )
       _A  :			List[Any]											=      torch.tensor([0.2_692, -0.7_197, -0.1_328]     )
       if model_name == "clipseg-rd64-refined":
              _A  :			int											=      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":
              _A  :			Optional[Any]											=      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":
              _A  :			Union[str, Any]											=      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]						,lowerCamelCase						,atol=1E-3     )
       assert torch.allclose(outputs.conditional_embeddings[0, :3]						,lowerCamelCase						,atol=1E-3     )
       assert torch.allclose(outputs.pooled_output[0, :3]						,lowerCamelCase						,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(lowerCamelCase     )
              processor.save_pretrained(lowerCamelCase     )

       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__":
   A   :  Dict       		=				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.'''
   )

   A   :  List[str]       		=				parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''







A   :  Optional[Any]       		=				{
    '''0000''': '''0''',
    '''0001''': '''1''',
    '''0010''': '''2''',
    '''0011''': '''3''',
    '''0100''': '''4''',
    '''0101''': '''5''',
    '''0110''': '''6''',
    '''0111''': '''7''',
    '''1000''': '''8''',
    '''1001''': '''9''',
    '''1010''': '''a''',
    '''1011''': '''b''',
    '''1100''': '''c''',
    '''1101''': '''d''',
    '''1110''': '''e''',
    '''1111''': '''f''',
}






def    lowerCAmelCase__ (       lowerCamelCase  :    str     ):
       _A  :			Union[str, Any]											=      str(lowerCamelCase     ).strip()

       # Exceptions
       if not binary_str:
              raise ValueError('Empty string was passed to the function'     )
       _A  :			Any											=      binary_str[0] == '-'
       _A  :			str											=      binary_str[1:] if is_negative else binary_str
       if not all(char in '01' for char in binary_str     ):
              raise ValueError('Non-binary value was passed to the function'     )

       _A  :			Optional[Any]											=      (
           '0' * (4 * (divmod(len(lowerCamelCase     )						,4     )[0] + 1) - len(lowerCamelCase     )) + binary_str
       )

       _A  :			Optional[int]											=      []
       for x in range(0						,len(lowerCamelCase     )						,4     ):
              hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]     )
       _A  :			int											=      '0x' + ''.join(lowerCamelCase     )

       return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
   from doctest import testmod

   testmod()






