code: import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""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(
  							    _A ,_A ,use_complex_transposed_convolution=_A ,reduce_dim=_A ,)
  							return config
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""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 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							for key in orig_state_dict.copy().keys():
  														_lowercase				=	orig_state_dict.pop(_A					)
  
  														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(_A					)
  																					if "visual_projection" in new_name or "text_projection" in new_name:
  																												_lowercase				=	val.T
  																					_lowercase				=	val
  
  							return orig_state_dict
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  							_lowercase				=	Image.open(requests.get(_A ,stream=_A					).raw					)
  							return image
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	get_clipseg_config(_A					)
  							_lowercase				=	CLIPSegForImageSegmentation(_A					)
  							model.eval()
  
  							_lowercase				=	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
  							_lowercase				=	convert_state_dict(_A ,_A					)
  							_lowercase				,       _lowercase				=	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}'''					)
  
  							_lowercase				=	ViTImageProcessor(size=352					)
  							_lowercase				=	CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""					)
  							_lowercase				=	CLIPSegProcessor(image_processor=_A ,tokenizer=_A					)
  
  							_lowercase				=	prepare_img()
  							_lowercase				=	["""a glass""", """something to fill""", """wood""", """a jar"""]
  
  							_lowercase				=	processor(text=_A ,images=[image] * len(_A					) ,padding="""max_length""" ,return_tensors="""pt"""					)
  
  							with torch.no_grad():
  														_lowercase				=	model(**_A					)
  
  							# verify values
  							_lowercase				=	torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]					)
  							_lowercase				=	torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]					)
  							if model_name == "clipseg-rd64-refined":
  														_lowercase				=	torch.tensor(
  														    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]					)
  							elif model_name == "clipseg-rd64":
  														_lowercase				=	torch.tensor(
  														    [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]					)
  							elif model_name == "clipseg-rd16":
  														_lowercase				=	torch.tensor(
  														    [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]					)
  							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__":
  				A_:      Optional[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 shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  @require_vision
  class _lowercase					(       unittest.TestCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	tempfile.mkdtemp()
  
  														_lowercase				=	CLIPImageProcessor()
  														_lowercase				=	BertTokenizer.from_pretrained(
  														    """hf-internal-testing/tiny-random-BertModel"""     ,   model_input_names=["""input_ids""", """attention_mask"""]  )
  
  														_lowercase				=	GitProcessor(UpperCAmelCase     ,   UpperCAmelCase  )
  
  														processor.save_pretrained(self.tmpdirname  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return AutoProcessor.from_pretrained(self.tmpdirname     ,   **UpperCAmelCase  ).tokenizer
  
  
  
  							def     _UpperCAmelCase       (    self     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return AutoProcessor.from_pretrained(self.tmpdirname     ,   **UpperCAmelCase  ).image_processor
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														shutil.rmtree(self.tmpdirname  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	[np.random.randint(255     ,   size=(3, 30, 400)     ,   dtype=np.uinta  )]
  
  														_lowercase				=	[Image.fromarray(np.moveaxis(UpperCAmelCase     ,   0     ,   -1  )  ) for x in image_inputs]
  
  														return image_inputs
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	GitProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()  )
  														processor.save_pretrained(self.tmpdirname  )
  
  														_lowercase				=	self.get_tokenizer(bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""  )
  														_lowercase				=	self.get_image_processor(do_normalize=UpperCAmelCase     ,   padding_value=1.0  )
  
  														_lowercase				=	GitProcessor.from_pretrained(
  														    self.tmpdirname     ,   bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""     ,   do_normalize=UpperCAmelCase     ,   padding_value=1.0  )
  
  														self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()  )
  														self.assertIsInstance(processor.tokenizer     ,   UpperCAmelCase  )
  
  														self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()  )
  														self.assertIsInstance(processor.image_processor     ,   UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	self.prepare_image_inputs()
  
  														_lowercase				=	image_processor(UpperCAmelCase     ,   return_tensors="""np"""  )
  														_lowercase				=	processor(images=UpperCAmelCase     ,   return_tensors="""np"""  )
  
  														for key in input_feat_extract.keys():
  																					self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1e-2  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	"""lower newer"""
  
  														_lowercase				=	processor(text=UpperCAmelCase  )
  
  														_lowercase				=	tokenizer(UpperCAmelCase     ,   return_token_type_ids=UpperCAmelCase  )
  
  														for key in encoded_tok.keys():
  																					self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	"""lower newer"""
  														_lowercase				=	self.prepare_image_inputs()
  
  														_lowercase				=	processor(text=UpperCAmelCase     ,   images=UpperCAmelCase  )
  
  														self.assertListEqual(list(inputs.keys()  )     ,   ["""input_ids""", """attention_mask""", """pixel_values"""]  )
  
  														# test if it raises when no input is passed
  														with pytest.raises(UpperCAmelCase  ):
  																					processor()
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  														_lowercase				=	processor.batch_decode(UpperCAmelCase  )
  														_lowercase				=	tokenizer.batch_decode(UpperCAmelCase  )
  
  														self.assertListEqual(UpperCAmelCase     ,   UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	"""lower newer"""
  														_lowercase				=	self.prepare_image_inputs()
  
  														_lowercase				=	processor(text=UpperCAmelCase     ,   images=UpperCAmelCase  )
  
  														# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
  														self.assertListEqual(list(inputs.keys()  )     ,   ["""input_ids""", """attention_mask""", """pixel_values"""]  )
  
  
  