
"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

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







def 			__a  (	__lowerCamelCase						):
   UpperCAmelCase_      :      Optional[int]      =					CLIPSegTextConfig()
   UpperCAmelCase_      :      List[Any]      =					CLIPSegVisionConfig(patch_size=16						)

   UpperCAmelCase_      :      List[Any]      =					True if "refined" in model_name else False
   UpperCAmelCase_      :      Union[str, Any]      =					16 if "rd16" in model_name else 64

   UpperCAmelCase_      :      Optional[Any]      =					CLIPSegConfig.from_text_vision_configs(
       __lowerCamelCase,	__lowerCamelCase,	use_complex_transposed_convolution=__lowerCamelCase,	reduce_dim=__lowerCamelCase,	)
   return config







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

   return name







def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
   for key in orig_state_dict.copy().keys():
      UpperCAmelCase_      :      Dict      =					orig_state_dict.pop(__lowerCamelCase						)

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

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

   return orig_state_dict







def 			__a  (	):
   UpperCAmelCase_      :      List[str]      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_      :      List[str]      =					Image.open(requests.get(__lowerCamelCase,	stream=__lowerCamelCase						).raw						)
   return image







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   UpperCAmelCase_      :      Union[str, Any]      =					get_clipseg_config(__lowerCamelCase						)
   UpperCAmelCase_      :      Optional[Any]      =					CLIPSegForImageSegmentation(__lowerCamelCase						)
   model.eval()

   UpperCAmelCase_      :      Optional[int]      =					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
   UpperCAmelCase_      :      Optional[int]      =					convert_state_dict(__lowerCamelCase,	__lowerCamelCase						)
   UpperCAmelCase_      :      Optional[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}"""						)

   UpperCAmelCase_      :      Union[str, Any]      =					ViTImageProcessor(size=352						)
   UpperCAmelCase_      :      str      =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"						)
   UpperCAmelCase_      :      Tuple      =					CLIPSegProcessor(image_processor=__lowerCamelCase,	tokenizer=__lowerCamelCase						)

   UpperCAmelCase_      :      Any      =					prepare_img()
   UpperCAmelCase_      :      Optional[int]      =					["a glass", "something to fill", "wood", "a jar"]

   UpperCAmelCase_      :      List[Any]      =					processor(text=__lowerCamelCase,	images=[image] * len(__lowerCamelCase						),	padding="max_length",	return_tensors="pt"						)

   with torch.no_grad():
      UpperCAmelCase_      :      int      =					model(**__lowerCamelCase						)

   # verify values
   UpperCAmelCase_      :      Tuple      =					torch.tensor([0.1110, -0.1882, 0.1645]						)
   UpperCAmelCase_      :      Tuple      =					torch.tensor([0.2692, -0.7197, -0.1328]						)
   if model_name == "clipseg-rd64-refined":
      UpperCAmelCase_      :      Any      =					torch.tensor(
          [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]						)
   elif model_name == "clipseg-rd64":
      UpperCAmelCase_      :      Dict      =					torch.tensor(
          [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]						)
   elif model_name == "clipseg-rd16":
      UpperCAmelCase_      :      Optional[int]      =					torch.tensor(
          [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]						)
   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	 =							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	 =							parser.parse_args()
    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""





from hashlib import mda, shaaaa

_a	 =							(shaaaa, mda)


class A_			:




  '''simple docstring'''







  def __init__(	self       ,     lowercase_ = 8						):

     """simple docstring"""
     UpperCAmelCase_      :      List[str]      =					0B0
     UpperCAmelCase_      :      Union[str, Any]      =					size







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self.hash_(lowercase_						)
     self.bitarray |= h







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					self.hash_(lowercase_						)
     return (h & self.bitarray) == h







  def __contains__(	self       ,     lowercase_						):

     """simple docstring"""
     return self.exists(lowercase_						)







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      str      =					bin(lowercase_						)[2:]
     return res.zfill(self.size						)







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     return self.format_bin(self.bitarray						)







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      List[str]      =					0B0
     for func in HASH_FUNCTIONS:
        UpperCAmelCase_      :      str      =					(
            int.from_bytes(func(value.encode()						).digest()       ,     "little"						) % self.size
        )
        res |= 2**position
     return res







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     return self.format_bin(self.hash_(lowercase_						)						)







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					bin(self.bitarray						).count("1"						)
     return (n_ones / self.size) ** len(lowercase_						)


