


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def        UpperCAmelCase_      (							__snake_case		)  ->    str:







     """simple docstring"""


     _lowercase							   =CLIPSegTextConfig()
     _lowercase							   =CLIPSegVisionConfig(patch_size=16		)

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

     _lowercase							   =CLIPSegConfig.from_text_vision_configs(
         __snake_case  ,		__snake_case  ,		use_complex_transposed_convolution=__snake_case  ,		reduce_dim=__snake_case  ,		)
     return config
def        UpperCAmelCase_      (							__snake_case		)  ->    Optional[Any]:







     """simple docstring"""


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

     return name
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    str:







     """simple docstring"""


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

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

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

     return orig_state_dict
def        UpperCAmelCase_      (							)  ->    Any:







     """simple docstring"""


     _lowercase							   ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _lowercase							   =Image.open(requests.get(__snake_case  ,		stream=__snake_case		).raw		)
     return image
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)  ->    int:







     """simple docstring"""


     _lowercase							   =get_clipseg_config(__snake_case		)
     _lowercase							   =CLIPSegForImageSegmentation(__snake_case		)
     model.eval()

     _lowercase							   =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
     _lowercase							   =convert_state_dict(__snake_case  ,		__snake_case		)
     _lowercase   ,					_lowercase							   =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}"		)

     _lowercase							   =ViTImageProcessor(size=352		)
     _lowercase							   =CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''		)
     _lowercase							   =CLIPSegProcessor(image_processor=__snake_case  ,		tokenizer=__snake_case		)

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

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

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

     # verify values
     _lowercase							   =torch.tensor([0.11_10, -0.18_82, 0.16_45]		)
     _lowercase							   =torch.tensor([0.26_92, -0.71_97, -0.13_28]		)
     if model_name == "clipseg-rd64-refined":
          _lowercase							   =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":
          _lowercase							   =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":
          _lowercase							   =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__":
  UpperCAmelCase__     			=							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__     			=							parser.parse_args()
  convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import requests

try:
  from django.contrib.auth import authenticate, login
  from django.shortcuts import redirect, render
except ImportError:
  UpperCAmelCase__     			=							UpperCAmelCase__     			=							UpperCAmelCase__     			=							UpperCAmelCase__     			=							print
def        UpperCAmelCase_      (							__snake_case		)  ->    List[str]:







 """simple docstring"""


 _lowercase							   ='''secretKey'''
 _lowercase							   ='''https://www.google.com/recaptcha/api/siteverify'''

 # when method is not POST, direct user to login page
 if request.method != "POST":
  return render(__snake_case  ,		'''login.html'''		)

 # from the frontend, get username, password, and client_key
 _lowercase							   =request.POST.get('''username'''		)
 _lowercase							   =request.POST.get('''password'''		)
 _lowercase							   =request.POST.get('''g-recaptcha-response'''		)

 # post recaptcha response to Google's recaptcha api
 _lowercase							   =requests.post(__snake_case  ,		data={'''secret''': secret_key, '''response''': client_key}		)
 # if the recaptcha api verified our keys
 if response.json().get('''success'''  ,		__snake_case		):
  # authenticate the user
  _lowercase							   =authenticate(__snake_case  ,		username=__snake_case  ,		password=__snake_case		)
  if user_in_database:
   login(__snake_case  ,		__snake_case		)
   return redirect('''/your-webpage'''		)
 return render(__snake_case  ,		'''login.html'''		)

