code: 
  '''simple docstring'''
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					int     =	CLIPSegTextConfig()
  		snake_case     :					Tuple     =	CLIPSegVisionConfig(patch_size=16 )
  
  		snake_case     :					Optional[int]     =	True if '''refined''' in model_name else False
  		snake_case     :					Any     =	16 if '''rd16''' in model_name else 64
  
  		snake_case     :					List[str]     =	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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		if "clip_model" in name:
  				snake_case     :					int     =	name.replace('''clip_model'''   ,					'''clip''' )
  		if "transformer" in name:
  				if "visual" in name:
  						snake_case     :					List[Any]     =	name.replace('''visual.transformer'''   ,					'''vision_model''' )
  				else:
  						snake_case     :					List[str]     =	name.replace('''transformer'''   ,					'''text_model''' )
  		if "resblocks" in name:
  				snake_case     :					str     =	name.replace('''resblocks'''   ,					'''encoder.layers''' )
  		if "ln_1" in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''ln_1'''   ,					'''layer_norm1''' )
  		if "ln_2" in name:
  				snake_case     :					Optional[Any]     =	name.replace('''ln_2'''   ,					'''layer_norm2''' )
  		if "c_fc" in name:
  				snake_case     :					Dict     =	name.replace('''c_fc'''   ,					'''fc1''' )
  		if "c_proj" in name:
  				snake_case     :					Optional[int]     =	name.replace('''c_proj'''   ,					'''fc2''' )
  		if "attn" in name and "self" not in name:
  				snake_case     :					Tuple     =	name.replace('''attn'''   ,					'''self_attn''' )
  		# text encoder
  		if "token_embedding" in name:
  				snake_case     :					Optional[int]     =	name.replace('''token_embedding'''   ,					'''text_model.embeddings.token_embedding''' )
  		if "positional_embedding" in name and "visual" not in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''positional_embedding'''   ,					'''text_model.embeddings.position_embedding.weight''' )
  		if "ln_final" in name:
  				snake_case     :					Tuple     =	name.replace('''ln_final'''   ,					'''text_model.final_layer_norm''' )
  		# vision encoder
  		if "visual.class_embedding" in name:
  				snake_case     :					Tuple     =	name.replace('''visual.class_embedding'''   ,					'''vision_model.embeddings.class_embedding''' )
  		if "visual.conv1" in name:
  				snake_case     :					Tuple     =	name.replace('''visual.conv1'''   ,					'''vision_model.embeddings.patch_embedding''' )
  		if "visual.positional_embedding" in name:
  				snake_case     :					Optional[Any]     =	name.replace('''visual.positional_embedding'''   ,					'''vision_model.embeddings.position_embedding.weight''' )
  		if "visual.ln_pre" in name:
  				snake_case     :					Any     =	name.replace('''visual.ln_pre'''   ,					'''vision_model.pre_layrnorm''' )
  		if "visual.ln_post" in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''visual.ln_post'''   ,					'''vision_model.post_layernorm''' )
  		# projection layers
  		if "visual.proj" in name:
  				snake_case     :					Any     =	name.replace('''visual.proj'''   ,					'''visual_projection.weight''' )
  		if "text_projection" in name:
  				snake_case     :					List[Any]     =	name.replace('''text_projection'''   ,					'''text_projection.weight''' )
  		# decoder
  		if "trans_conv" in name:
  				snake_case     :					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:
  				snake_case     :					Dict     =	'''decoder.''' + name
  		if "blocks" in name:
  				snake_case     :					Any     =	name.replace('''blocks'''   ,					'''decoder.layers''' )
  		if "linear1" in name:
  				snake_case     :					List[Any]     =	name.replace('''linear1'''   ,					'''mlp.fc1''' )
  		if "linear2" in name:
  				snake_case     :					int     =	name.replace('''linear2'''   ,					'''mlp.fc2''' )
  		if "norm1" in name and "layer_" not in name:
  				snake_case     :					Any     =	name.replace('''norm1'''   ,					'''layer_norm1''' )
  		if "norm2" in name and "layer_" not in name:
  				snake_case     :					Union[str, Any]     =	name.replace('''norm2'''   ,					'''layer_norm2''' )
  
  		return name
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		for key in orig_state_dict.copy().keys():
  				snake_case     :					Union[str, Any]     =	orig_state_dict.pop(SCREAMING_SNAKE_CASE__ )
  
  				if key.startswith('''clip_model''' ) and "attn.in_proj" in key:
  						snake_case     :					Tuple     =	key.split('''.''' )
  						if "visual" in key:
  								snake_case     :					Optional[int]     =	int(key_split[4] )
  								snake_case     :					Dict     =	config.vision_config.hidden_size
  								snake_case     :					Any     =	'''vision_model'''
  						else:
  								snake_case     :					Dict     =	int(key_split[3] )
  								snake_case     :					int     =	config.text_config.hidden_size
  								snake_case     :					Dict     =	'''text_model'''
  
  						if "weight" in key:
  								snake_case     :					Optional[int]     =	val[:dim, :]
  								snake_case     :					str     =	val[
  								    dim : dim * 2, :
  								]
  								snake_case     :					List[Any]     =	val[-dim:, :]
  						else:
  								snake_case     :					Any     =	val[:dim]
  								snake_case     :					str     =	val[dim : dim * 2]
  								snake_case     :					Optional[int]     =	val[-dim:]
  				elif "self_attn" in key and "out_proj" not in key:
  						snake_case     :					List[Any]     =	key.split('''.''' )
  						snake_case     :					Dict     =	int(key_split[1] )
  						snake_case     :					Optional[Any]     =	config.reduce_dim
  						if "weight" in key:
  								snake_case     :					List[Any]     =	val[:dim, :]
  								snake_case     :					Optional[Any]     =	val[dim : dim * 2, :]
  								snake_case     :					List[str]     =	val[-dim:, :]
  						else:
  								snake_case     :					Optional[Any]     =	val[:dim]
  								snake_case     :					Tuple     =	val[dim : dim * 2]
  								snake_case     :					Union[str, Any]     =	val[-dim:]
  				else:
  						snake_case     :					str     =	rename_key(SCREAMING_SNAKE_CASE__ )
  						if "visual_projection" in new_name or "text_projection" in new_name:
  								snake_case     :					Optional[int]     =	val.T
  						snake_case     :					Any     =	val
  
  		return orig_state_dict
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					int     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  		snake_case     :					List[Any]     =	Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,					stream=SCREAMING_SNAKE_CASE__ ).raw )
  		return image
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Any     =	get_clipseg_config(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					int     =	CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__ )
  		model.eval()
  
  		snake_case     :					List[str]     =	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
  		snake_case     :					List[str]     =	convert_state_dict(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  		snake_case ,snake_case     :					Dict     =	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}' )
  
  		snake_case     :					Any     =	ViTImageProcessor(size=352 )
  		snake_case     :					List[Any]     =	CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' )
  		snake_case     :					Optional[Any]     =	CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__   ,					tokenizer=SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					List[Any]     =	prepare_img()
  		snake_case     :					Union[str, Any]     =	['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
  		snake_case     :					Any     =	processor(text=SCREAMING_SNAKE_CASE__   ,					images=[image] * len(SCREAMING_SNAKE_CASE__ )   ,					padding='''max_length'''   ,					return_tensors='''pt''' )
  
  		with torch.no_grad():
  				snake_case     :					str     =	model(**SCREAMING_SNAKE_CASE__ )
  
  		# verify values
  		snake_case     :					Union[str, Any]     =	torch.tensor([0.1110, -0.1882, 0.1645] )
  		snake_case     :					Union[str, Any]     =	torch.tensor([0.2692, -0.7197, -0.1328] )
  		if model_name == "clipseg-rd64-refined":
  				snake_case     :					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":
  				snake_case     :					str     =	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     :					Union[str, 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]   ,					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__":
  						lowercase__						=			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."
  						)
  
  						lowercase__						=			parser.parse_args()
  						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
style_context: 
  '''simple docstring'''
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowercase__						=			{
      "configuration_flava": [
          "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "FlavaConfig",
          "FlavaImageCodebookConfig",
          "FlavaImageConfig",
          "FlavaMultimodalConfig",
          "FlavaTextConfig",
      ],
  }
  
  try:
  						if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			["FlavaFeatureExtractor"]
  						lowercase__						=			["FlavaImageProcessor"]
  						lowercase__						=			["FlavaProcessor"]
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			[
  						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "FlavaForPreTraining",
  						    "FlavaImageCodebook",
  						    "FlavaImageModel",
  						    "FlavaModel",
  						    "FlavaMultimodalModel",
  						    "FlavaPreTrainedModel",
  						    "FlavaTextModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_flava import (
  						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    FlavaConfig,
  						    FlavaImageCodebookConfig,
  						    FlavaImageConfig,
  						    FlavaMultimodalConfig,
  						    FlavaTextConfig,
  						)
  
  						try:
  												if not is_vision_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .feature_extraction_flava import FlavaFeatureExtractor
  												from .image_processing_flava import FlavaImageProcessor
  												from .processing_flava import FlavaProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_flava import (
  												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    FlavaForPreTraining,
  												    FlavaImageCodebook,
  												    FlavaImageModel,
  												    FlavaModel,
  												    FlavaMultimodalModel,
  												    FlavaPreTrainedModel,
  												    FlavaTextModel,
  												)
  
  else:
  						import sys
  
  						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  