code: 
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       snake_case					=       Image.open(requests.get(__snake_case							,stream=__snake_case       ).raw       )
       return image
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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([0.1110, -0.1882, 0.1645]       )
       snake_case					=       torch.tensor([0.2692, -0.7197, -0.1328]       )
       if model_name == "clipseg-rd64-refined":
            snake_case					=       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":
            snake_case					=       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":
            snake_case					=       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]							,__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__":
   _SCREAMING_SNAKE_CASE    :				List[Any]       =					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."
   )
  
   _SCREAMING_SNAKE_CASE    :				str       =					parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
style_context: 
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)
  
  @dataclass
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"
  
  
  
  
  
  
  @dataclass(frozen=snake_case__			)
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)					) + "\n"
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           if len(self.get_labels()					) > 1:
                snake_case					=       self.get_labels()[int(example.label					)]
           return example
  
  
  
  
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
           with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
                return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)
  
  
  
  
  
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
           snake_case					=       [] if labels is None else labels
           snake_case					=       [] if examples is None else examples
           snake_case					=       mode
           snake_case					=       verbose
      def __len__( self					):
           return len(self.examples					)
      def __getitem__( self    ,      __snake_case					):
           if isinstance(__snake_case    ,      __snake_case					):
                return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
           return self.examples[idx]
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples_from_csv(
               __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
           return processor
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples(__snake_case    ,      labels=__snake_case					)
           return processor
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
           snake_case					=       self._read_tsv(__snake_case					)
           if skip_first_row:
                snake_case					=       lines[1:]
           snake_case					=       []
           snake_case					=       []
           snake_case					=       []
           for i, line in enumerate(__snake_case					):
                texts.append(line[column_text]					)
                labels.append(line[column_label]					)
                if column_id is not None:
                     ids.append(line[column_id]					)
                else:
                     snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                     ids.append(__snake_case					)
  
           return self.add_examples(
               __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
           if labels is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(
                    F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is None:
                snake_case					=       [None] * len(__snake_case					)
           if labels is None:
                snake_case					=       [None] * len(__snake_case					)
           snake_case					=       []
           snake_case					=       set()
           for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
                if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                     snake_case				,						snake_case					=       text_or_text_and_label
                else:
                     snake_case					=       text_or_text_and_label
                added_labels.add(__snake_case					)
                examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)
  
           # Update examples
           if overwrite_examples:
                snake_case					=       examples
           else:
                self.examples.extend(__snake_case					)
  
           # Update labels
           if overwrite_labels:
                snake_case					=       list(__snake_case					)
           else:
                snake_case					=       list(set(self.labels					).union(__snake_case					)					)
  
           return self.examples
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
           if max_length is None:
                snake_case					=       tokenizer.max_len
  
           snake_case					=       {label: i for i, label in enumerate(self.labels					)}
  
           snake_case					=       []
           for ex_index, example in enumerate(self.examples					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Tokenizing example {ex_index}'''					)
  
                snake_case					=       tokenizer.encode(
                    example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
                all_input_ids.append(__snake_case					)
  
           snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)
  
           snake_case					=       []
           for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)
  
                # Zero-pad up to the sequence length.
                snake_case					=       batch_length - len(__snake_case					)
                if pad_on_left:
                     snake_case					=       ([pad_token] * padding_length) + input_ids
                     snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                else:
                     snake_case					=       input_ids + ([pad_token] * padding_length)
                     snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
  
                if self.mode == "classification":
                     snake_case					=       label_map[example.label]
                elif self.mode == "regression":
                     snake_case					=       float(example.label					)
                else:
                     raise ValueError(self.mode					)
  
                if ex_index < 5 and self.verbose:
                     logger.info('''*** Example ***'''					)
                     logger.info(F'''guid: {example.guid}'''					)
                     logger.info(F'''input_ids: {" ".join([str(__snake_case					) for x in input_ids]					)}'''					)
                     logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                     logger.info(F'''label: {example.label} (id = {label})'''					)
  
                features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)
  
           if return_tensors is None:
                return features
           elif return_tensors == "tf":
                if not is_tf_available():
                     raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
                import tensorflow as tf
  
                def gen():
                     for ex in features:
                          yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
                snake_case					=       tf.data.Dataset.from_generator(
                    __snake_case    ,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,      ({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))    ,      )
                return dataset
           elif return_tensors == "pt":
                if not is_torch_available():
                     raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
                import torch
                from torch.utils.data import TensorDataset
  
                snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
                snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
                if self.mode == "classification":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
                elif self.mode == "regression":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)
  
                snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
                return dataset
           else:
                raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)
  
  
  
  
  