code: 
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Any:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			CLIPSegTextConfig()
      __UpperCAmelCase									=			CLIPSegVisionConfig(patch_size=1_6      )
  
      __UpperCAmelCase									=			True if '''refined''' in model_name else False
      __UpperCAmelCase									=			1_6 if '''rd16''' in model_name else 6_4
  
      __UpperCAmelCase									=			CLIPSegConfig.from_text_vision_configs(
          SCREAMING_SNAKE_CASE__       ,   SCREAMING_SNAKE_CASE__       ,   use_complex_transposed_convolution=SCREAMING_SNAKE_CASE__       ,   reduce_dim=SCREAMING_SNAKE_CASE__       ,   )
      return config
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     int:
  
  
  
  
  
  
      '''simple docstring'''
  
  
      if "clip_model" in name:
          __UpperCAmelCase									=			name.replace('''clip_model'''       ,   '''clip'''      )
      if "transformer" in name:
          if "visual" in name:
              __UpperCAmelCase									=			name.replace('''visual.transformer'''       ,   '''vision_model'''      )
          else:
              __UpperCAmelCase									=			name.replace('''transformer'''       ,   '''text_model'''      )
      if "resblocks" in name:
          __UpperCAmelCase									=			name.replace('''resblocks'''       ,   '''encoder.layers'''      )
      if "ln_1" in name:
          __UpperCAmelCase									=			name.replace('''ln_1'''       ,   '''layer_norm1'''      )
      if "ln_2" in name:
          __UpperCAmelCase									=			name.replace('''ln_2'''       ,   '''layer_norm2'''      )
      if "c_fc" in name:
          __UpperCAmelCase									=			name.replace('''c_fc'''       ,   '''fc1'''      )
      if "c_proj" in name:
          __UpperCAmelCase									=			name.replace('''c_proj'''       ,   '''fc2'''      )
      if "attn" in name and "self" not in name:
          __UpperCAmelCase									=			name.replace('''attn'''       ,   '''self_attn'''      )
      # text encoder
      if "token_embedding" in name:
          __UpperCAmelCase									=			name.replace('''token_embedding'''       ,   '''text_model.embeddings.token_embedding'''      )
      if "positional_embedding" in name and "visual" not in name:
          __UpperCAmelCase									=			name.replace('''positional_embedding'''       ,   '''text_model.embeddings.position_embedding.weight'''      )
      if "ln_final" in name:
          __UpperCAmelCase									=			name.replace('''ln_final'''       ,   '''text_model.final_layer_norm'''      )
      # vision encoder
      if "visual.class_embedding" in name:
          __UpperCAmelCase									=			name.replace('''visual.class_embedding'''       ,   '''vision_model.embeddings.class_embedding'''      )
      if "visual.conv1" in name:
          __UpperCAmelCase									=			name.replace('''visual.conv1'''       ,   '''vision_model.embeddings.patch_embedding'''      )
      if "visual.positional_embedding" in name:
          __UpperCAmelCase									=			name.replace('''visual.positional_embedding'''       ,   '''vision_model.embeddings.position_embedding.weight'''      )
      if "visual.ln_pre" in name:
          __UpperCAmelCase									=			name.replace('''visual.ln_pre'''       ,   '''vision_model.pre_layrnorm'''      )
      if "visual.ln_post" in name:
          __UpperCAmelCase									=			name.replace('''visual.ln_post'''       ,   '''vision_model.post_layernorm'''      )
      # projection layers
      if "visual.proj" in name:
          __UpperCAmelCase									=			name.replace('''visual.proj'''       ,   '''visual_projection.weight'''      )
      if "text_projection" in name:
          __UpperCAmelCase									=			name.replace('''text_projection'''       ,   '''text_projection.weight'''      )
      # decoder
      if "trans_conv" in name:
          __UpperCAmelCase									=			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									=			'''decoder.''' + name
      if "blocks" in name:
          __UpperCAmelCase									=			name.replace('''blocks'''       ,   '''decoder.layers'''      )
      if "linear1" in name:
          __UpperCAmelCase									=			name.replace('''linear1'''       ,   '''mlp.fc1'''      )
      if "linear2" in name:
          __UpperCAmelCase									=			name.replace('''linear2'''       ,   '''mlp.fc2'''      )
      if "norm1" in name and "layer_" not in name:
          __UpperCAmelCase									=			name.replace('''norm1'''       ,   '''layer_norm1'''      )
      if "norm2" in name and "layer_" not in name:
          __UpperCAmelCase									=			name.replace('''norm2'''       ,   '''layer_norm2'''      )
  
      return name
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Any:
  
  
  
  
  
  
      '''simple docstring'''
  
  
      for key in orig_state_dict.copy().keys():
          __UpperCAmelCase									=			orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )
  
          if key.startswith('''clip_model'''      ) and "attn.in_proj" in key:
              __UpperCAmelCase									=			key.split('''.'''      )
              if "visual" in key:
                  __UpperCAmelCase									=			int(key_split[4]      )
                  __UpperCAmelCase									=			config.vision_config.hidden_size
                  __UpperCAmelCase									=			'''vision_model'''
              else:
                  __UpperCAmelCase									=			int(key_split[3]      )
                  __UpperCAmelCase									=			config.text_config.hidden_size
                  __UpperCAmelCase									=			'''text_model'''
  
              if "weight" in key:
                  __UpperCAmelCase									=			val[:dim, :]
                  __UpperCAmelCase									=			val[
                      dim : dim * 2, :
                  ]
                  __UpperCAmelCase									=			val[-dim:, :]
              else:
                  __UpperCAmelCase									=			val[:dim]
                  __UpperCAmelCase									=			val[dim : dim * 2]
                  __UpperCAmelCase									=			val[-dim:]
          elif "self_attn" in key and "out_proj" not in key:
              __UpperCAmelCase									=			key.split('''.'''      )
              __UpperCAmelCase									=			int(key_split[1]      )
              __UpperCAmelCase									=			config.reduce_dim
              if "weight" in key:
                  __UpperCAmelCase									=			val[:dim, :]
                  __UpperCAmelCase									=			val[dim : dim * 2, :]
                  __UpperCAmelCase									=			val[-dim:, :]
              else:
                  __UpperCAmelCase									=			val[:dim]
                  __UpperCAmelCase									=			val[dim : dim * 2]
                  __UpperCAmelCase									=			val[-dim:]
          else:
              __UpperCAmelCase									=			rename_key(SCREAMING_SNAKE_CASE__      )
              if "visual_projection" in new_name or "text_projection" in new_name:
                  __UpperCAmelCase									=			val.T
              __UpperCAmelCase									=			val
  
      return orig_state_dict
  
  
  def       __a       (   )      ->     int:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __UpperCAmelCase									=			Image.open(requests.get(SCREAMING_SNAKE_CASE__       ,   stream=SCREAMING_SNAKE_CASE__      ).raw      )
      return image
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Dict:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			get_clipseg_config(SCREAMING_SNAKE_CASE__      )
      __UpperCAmelCase									=			CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__      )
      model.eval()
  
      __UpperCAmelCase									=			torch.load(SCREAMING_SNAKE_CASE__       ,   map_location='''cpu'''      )
  
      # remove some keys
      for key in state_dict.copy().keys():
          if key.startswith('''model'''      ):
              state_dict.pop(SCREAMING_SNAKE_CASE__       ,   SCREAMING_SNAKE_CASE__      )
  
      # rename some keys
      __UpperCAmelCase									=			convert_state_dict(SCREAMING_SNAKE_CASE__       ,   SCREAMING_SNAKE_CASE__      )
      __UpperCAmelCase									=			model.load_state_dict(SCREAMING_SNAKE_CASE__       ,   strict=SCREAMING_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(SCREAMING_SNAKE_CASE__      )      )
      if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
          raise ValueError(f'''Unexpected keys: {unexpected_keys}'''      )
  
      __UpperCAmelCase									=			ViTImageProcessor(size=3_5_2      )
      __UpperCAmelCase									=			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''      )
      __UpperCAmelCase									=			CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__       ,   tokenizer=SCREAMING_SNAKE_CASE__      )
  
      __UpperCAmelCase									=			prepare_img()
      __UpperCAmelCase									=			['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
      __UpperCAmelCase									=			processor(text=SCREAMING_SNAKE_CASE__       ,   images=[image] * len(SCREAMING_SNAKE_CASE__      )       ,   padding='''max_length'''       ,   return_tensors='''pt'''      )
  
      with torch.no_grad():
          __UpperCAmelCase									=			model(**SCREAMING_SNAKE_CASE__      )
  
      # verify values
      __UpperCAmelCase									=			torch.tensor([0.1110, -0.1882, 0.1645]      )
      __UpperCAmelCase									=			torch.tensor([0.2692, -0.7197, -0.1328]      )
      if model_name == "clipseg-rd64-refined":
          __UpperCAmelCase									=			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									=			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									=			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]       ,   SCREAMING_SNAKE_CASE__       ,   atol=1e-3      )
      assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,   SCREAMING_SNAKE_CASE__       ,   atol=1e-3      )
      assert torch.allclose(outputs.pooled_output[0, :3]       ,   SCREAMING_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(SCREAMING_SNAKE_CASE__      )
          processor.save_pretrained(SCREAMING_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__":
         A_		:			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.'
         )
  
         A_		:			int										=   parser.parse_args()
         convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
style_context: 
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin
  
  
  if is_torch_available():
         import torch
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import SwinaSRImageProcessor
         from transformers.image_transforms import get_image_size
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__=7     ,      lowercase__=3     ,      lowercase__=18     ,      lowercase__=30     ,      lowercase__=400     ,      lowercase__=True     ,      lowercase__=1 / 255     ,      lowercase__=True     ,      lowercase__=8     ,      )     ->      List[str]:
          __UpperCAmelCase									=			parent
          __UpperCAmelCase									=			batch_size
          __UpperCAmelCase									=			num_channels
          __UpperCAmelCase									=			image_size
          __UpperCAmelCase									=			min_resolution
          __UpperCAmelCase									=			max_resolution
          __UpperCAmelCase									=			do_rescale
          __UpperCAmelCase									=			rescale_factor
          __UpperCAmelCase									=			do_pad
          __UpperCAmelCase									=			pad_size
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          return {
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
              "pad_size": self.pad_size,
          }
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__=False     ,      lowercase__=False     ,      lowercase__=False  )     ->      Tuple:
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
          if equal_resolution:
              __UpperCAmelCase									=			[]
              for i in range(self.batch_size  ):
                  image_inputs.append(
                      np.random.randint(
                          255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta  )  )
          else:
              __UpperCAmelCase									=			[]
              for i in range(self.batch_size  ):
                  __UpperCAmelCase					,     __UpperCAmelCase									=			np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution  )     ,      2  )
                  image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta  )  )
  
          if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __UpperCAmelCase									=			[Image.fromarray(np.moveaxis(lowercase__     ,      0     ,      -1  )  ) for x in image_inputs]
  
          if torchify:
              __UpperCAmelCase									=			[torch.from_numpy(lowercase__  ) for x in image_inputs]
  
          return image_inputs
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					A_   (      _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       SwinaSRImageProcessor if is_vision_available() else None
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			SwinaSRImageProcessingTester(self  )
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      str:
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_rescale'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''rescale_factor'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_pad'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''pad_size'''  )  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          pass
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )
          __UpperCAmelCase									=			self.image_processor_tester.pad_size
  
          __UpperCAmelCase									=			(old_height // size + 1) * size - old_height
          __UpperCAmelCase									=			(old_width // size + 1) * size - old_width
          return old_height + pad_height, old_width + pad_width
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          # Initialize image_processor
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      Image.Image  )
  
          # Test not batched input
          __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(np.array(image_inputs[0]  )  )
          self.assertEqual(
              encoded_images.shape     ,      (
                  1,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,      )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          # Initialize image_processor
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      numpify=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      np.ndarray  )
  
          # Test not batched input
          __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
          self.assertEqual(
              encoded_images.shape     ,      (
                  1,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,      )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          # Initialize image_processor
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      torchify=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      torch.Tensor  )
  
          # Test not batched input
          __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
          self.assertEqual(
              encoded_images.shape     ,      (
                  1,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,      )
  
  
  
  
  