
import argparse

import requests
import torch
from PIL import Image

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






def 						lowerCamelCase__							(      a   )   ->     int:
   _A:						Any	    = CLIPSegTextConfig()
   _A:						Optional[Any]	    = CLIPSegVisionConfig(patch_size=16   )

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

   _A:						Union[str, Any]	    = CLIPSegConfig.from_text_vision_configs(
       a	,	a	,	use_complex_transposed_convolution=a	,	reduce_dim=a	,	)
   return config






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

   return name






def 						lowerCamelCase__							(      a	,	a   )   ->     List[str]:
   for key in orig_state_dict.copy().keys():
      _A:						Tuple	    = orig_state_dict.pop(a   )

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

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

   return orig_state_dict






def 						lowerCamelCase__							(      )   ->     str:
   _A:						Dict	    = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A:						Dict	    = Image.open(requests.get(a	,	stream=a   ).raw   )
   return image






def 						lowerCamelCase__							(      a	,	a	,	a	,	a   )   ->     Union[str, Any]:
   _A:						Tuple	    = get_clipseg_config(a   )
   _A:						Union[str, Any]	    = CLIPSegForImageSegmentation(a   )
   model.eval()

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

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

    # rename some keys
   _A:						str	    = convert_state_dict(a	,	a   )
   _A    ,						_A:						List[str]	    = model.load_state_dict(a	,	strict=a   )

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

   _A:						Any	    = ViTImageProcessor(size=3_52   )
   _A:						Optional[int]	    = CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''   )
   _A:						Optional[Any]	    = CLIPSegProcessor(image_processor=a	,	tokenizer=a   )

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

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

   with torch.no_grad():
      _A:						Optional[int]	    = model(**a   )

   # verify values
   _A:						Dict	    = torch.tensor([0.1110, -0.1882, 0.1645]   )
   _A:						Optional[int]	    = torch.tensor([0.2692, -0.7197, -0.1328]   )
   if model_name == "clipseg-rd64-refined":
      _A:						int	    = 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":
      _A:						Optional[int]	    = 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":
      _A:						Optional[Any]	    = 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]	,	a	,	atol=1E-3   )
   assert torch.allclose(outputs.conditional_embeddings[0, :3]	,	a	,	atol=1E-3   )
   assert torch.allclose(outputs.pooled_output[0, :3]	,	a	,	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(a   )
      processor.save_pretrained(a   )

   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__	:  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.'
   )

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





import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


UpperCAmelCase__	:  Optional[int]      =   logging.get_logger(__name__)






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''


   def __init__(      self  :							str    ,    *lowerCAmelCase_  :							Union[str, Any]    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      warnings.warn(
          '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
          ''' Please use ConvNextImageProcessor instead.'''    ,    lowerCAmelCase_    ,    )
      super().__init__(*lowerCAmelCase_    ,    **lowerCAmelCase_  )



