
import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def        __lowercase(							__snake_case				:		Optional[int]					)       ->   Union[str, Any]:
       __snake_case						       =   CLIPSegTextConfig()
       __snake_case						       =   CLIPSegVisionConfig(patch_size=16					)

       __snake_case						       =   True if 'refined' in model_name else False
       __snake_case						       =   16 if 'rd16' in model_name else 64

       __snake_case						       =   CLIPSegConfig.from_text_vision_configs(
           __snake_case    ,__snake_case    ,use_complex_transposed_convolution=__snake_case    ,reduce_dim=__snake_case    ,)
       return config
def        __lowercase(							__snake_case				:		Tuple					)       ->   Tuple:







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

       return name
def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		Tuple					)       ->   List[str]:







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

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

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

       return orig_state_dict
def        __lowercase(							)       ->   Dict:
       __snake_case						       =   'http://images.cocodataset.org/val2017/000000039769.jpg'
       __snake_case						       =   Image.open(requests.get(__snake_case    ,stream=__snake_case					).raw					)
       return image
def        __lowercase(							__snake_case				:		List[Any]    ,__snake_case				:		List[Any]    ,__snake_case				:		Union[str, Any]    ,__snake_case				:		Optional[Any]					)       ->   Union[str, Any]:
       __snake_case						       =   get_clipseg_config(__snake_case					)
       __snake_case						       =   CLIPSegForImageSegmentation(__snake_case					)
       model.eval()

       __snake_case						       =   torch.load(__snake_case    ,map_location='cpu'					)

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

    # rename some keys
       __snake_case						       =   convert_state_dict(__snake_case    ,__snake_case					)
       __snake_case       ,       __snake_case						       =   model.load_state_dict(__snake_case    ,strict=__snake_case					)

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

       __snake_case						       =   ViTImageProcessor(size=3_52					)
       __snake_case						       =   CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'					)
       __snake_case						       =   CLIPSegProcessor(image_processor=__snake_case    ,tokenizer=__snake_case					)

       __snake_case						       =   prepare_img()
       __snake_case						       =   ['a glass', 'something to fill', 'wood', 'a jar']

       __snake_case						       =   processor(text=__snake_case    ,images=[image] * len(__snake_case					)    ,padding='max_length'    ,return_tensors='pt'					)

       with torch.no_grad():
              __snake_case						       =   model(**__snake_case					)

       # verify values
       __snake_case						       =   torch.tensor([0.11_10, -0.18_82, 0.16_45]					)
       __snake_case						       =   torch.tensor([0.26_92, -0.71_97, -0.13_28]					)
       if model_name == "clipseg-rd64-refined":
              __snake_case						       =   torch.tensor(
                  [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]					)
       elif model_name == "clipseg-rd64":
              __snake_case						       =   torch.tensor(
                  [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]					)
       elif model_name == "clipseg-rd16":
              __snake_case						       =   torch.tensor(
                  [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]					)
       else:
              raise ValueError(f'''Model name {model_name} not supported.'''					)

       assert torch.allclose(outputs.logits[0, :3, :3]    ,__snake_case    ,atol=1e-3					)
       assert torch.allclose(outputs.conditional_embeddings[0, :3]    ,__snake_case    ,atol=1e-3					)
       assert torch.allclose(outputs.pooled_output[0, :3]    ,__snake_case    ,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(__snake_case					)
              processor.save_pretrained(__snake_case					)

       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__":
 lowerCamelCase_				:							List[str]       =    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."
 )

 lowerCamelCase_				:							Tuple       =    parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self      )->   None:
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A    =  {}
     # indexmap keys are an index and it's values are the number at that index
     __A    =  {}

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == item:
           array.pop(UpperCAmelCase      )
           return array
        elif array[mid] < item:
           __A    =  mid + 1
        else:
           __A    =  mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == index:
           # If the item already exists in the array,
           # insert it after the existing item
           array.insert(mid + 1    ,							UpperCAmelCase      )
           return array
        elif array[mid] < index:
           __A    =  mid + 1
        else:
           __A    =  mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(UpperCAmelCase    ,							UpperCAmelCase      )
     return array

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   None:
     # Remove previous index
     if index in self.indexmap:
        __A    =  self.indexmap[index]
        if len(self.numbermap[n]      ) == 1:
           del self.numbermap[n]
        else:
           __A    =  self.binary_search_delete(self.numbermap[n]    ,							UpperCAmelCase      )

        # Set new index
     __A    =  number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
        __A    =  [index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
        __A    =  self.binary_search_insert(
            self.numbermap[number]    ,							UpperCAmelCase      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   int:
     return self.numbermap.get(UpperCAmelCase    ,							[-1]      )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

