"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

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





def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[Any]       )       ->					List[str]:





						"""simple docstring"""






						snake_case             =      CLIPSegTextConfig()
						snake_case             =      CLIPSegVisionConfig(patch_size=1_6       )

						snake_case             =      True if '''refined''' in model_name else False
						snake_case             =      1_6 if '''rd16''' in model_name else 6_4

						snake_case             =      CLIPSegConfig.from_text_vision_configs(
						    __lowercase			,      __lowercase			,      use_complex_transposed_convolution=__lowercase			,      reduce_dim=__lowercase			,      )
						return config





def  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[int]       )       ->					Dict:





						"""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  lowerCAmelCase__     (      _UpperCamelCase							:					Any			,      _UpperCamelCase							:					Dict       )       ->					Optional[int]:





						"""simple docstring"""






						for key in orig_state_dict.copy().keys():
												snake_case             =      orig_state_dict.pop(__lowercase       )

												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(__lowercase       )
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								snake_case             =      val.T
																		snake_case             =      val

						return orig_state_dict





def  lowerCAmelCase__     (      )       ->					Optional[int]:





						"""simple docstring"""






						snake_case             =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case             =      Image.open(requests.get(__lowercase			,      stream=__lowercase       ).raw       )
						return image





def  lowerCAmelCase__     (      _UpperCamelCase							:					Tuple			,      _UpperCamelCase							:					Optional[Any]			,      _UpperCamelCase							:					str			,      _UpperCamelCase							:					Union[str, Any]       )       ->					Union[str, Any]:





						"""simple docstring"""






						snake_case             =      get_clipseg_config(__lowercase       )
						snake_case             =      CLIPSegForImageSegmentation(__lowercase       )
						model.eval()

						snake_case             =      torch.load(__lowercase			,      map_location='cpu'       )

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

    # rename some keys
						snake_case             =      convert_state_dict(__lowercase			,      __lowercase       )
						snake_case             =      model.load_state_dict(__lowercase			,      strict=__lowercase       )

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

						snake_case             =      ViTImageProcessor(size=3_5_2       )
						snake_case             =      CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'       )
						snake_case             =      CLIPSegProcessor(image_processor=__lowercase			,      tokenizer=__lowercase       )

						snake_case             =      prepare_img()
						snake_case             =      ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

						snake_case             =      processor(text=__lowercase			,      images=[image] * len(__lowercase       )			,      padding='max_length'			,      return_tensors='pt'       )

						with torch.no_grad():
												snake_case             =      model(**__lowercase       )

						# verify values
						snake_case             =      torch.tensor([0.11_10, -0.18_82, 0.16_45]       )
						snake_case             =      torch.tensor([0.26_92, -0.71_97, -0.13_28]       )
						if model_name == "clipseg-rd64-refined":
												snake_case             =      torch.tensor(
												    [[-1_0.0_4_0_7, -9.94_31, -1_0.2_6_4_6], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]       )
						elif model_name == "clipseg-rd64":
												snake_case             =      torch.tensor(
												    [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]       )
						elif model_name == "clipseg-rd16":
												snake_case             =      torch.tensor(
												    [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]       )
						else:
												raise ValueError(f"""Model name {model_name} not supported."""       )

						assert torch.allclose(outputs.logits[0, :3, :3]			,      __lowercase			,      atol=1e-3       )
						assert torch.allclose(outputs.conditional_embeddings[0, :3]			,      __lowercase			,      atol=1e-3       )
						assert torch.allclose(outputs.pooled_output[0, :3]			,      __lowercase			,      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(__lowercase       )
												processor.save_pretrained(__lowercase       )

						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__       =	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__       =	parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase			     =					TypeVar('''T''')
UpperCamelCase			     =					TypeVar('''U''')






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							T | None					,  SCREAMING_SNAKE_CASE_				:							U | None	)			->		Any:







       '''simple docstring'''



       A:		Union[str, Any]          =		key
       A:		Union[str, Any]          =		val
       A:		int          =		0
       A:		DoubleLinkedListNode[T, U] | None          =		None
       A:		DoubleLinkedListNode[T, U] | None          =		None




  def __repr__( self				:							Any	)			->		str:







       '''simple docstring'''



       return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
           self.key					,  self.val					,  self.freq					,  self.next is not None					,  self.prev is not None	)






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							int	)			->		None:







       '''simple docstring'''



       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A     ,    A:		Any          =		self.rear, self.head



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       A:		int          =		['''DoubleLinkedList''']
       A:		Dict          =		self.head
       while node.next is not None:
            rep.append(str(SCREAMING_SNAKE_CASE_	)	)
            A:		int          =		node.next
       rep.append(str(self.rear	)	)
       return ",\n    ".join(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       A:		Union[str, Any]          =		self.rear.prev

       # All nodes other than self.head are guaranteed to have non-None previous
       assert previous is not None

       A:		Optional[int]          =		node
       A:		int          =		previous
       A:		Dict          =		node
       A:		int          =		self.rear
       node.freq += 1
       self._position_node(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            A:		Any          =		node.prev

            A:		Optional[Any]          =		previous_node.prev
            A:		int          =		node.prev
            A:		int          =		previous_node
            A:		Optional[int]          =		node




  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		DoubleLinkedListNode[T, U] | None:







       '''simple docstring'''



       if node.prev is None or node.next is None:
            return None

       A:		List[Any]          =		node.next
       A:		Dict          =		node.prev
       A:		str          =		None
       A:		Union[str, Any]          =		None
       return node






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  UpperCamelCase_			:      dict[Callable[[T], U], LFUCache[T, U]]										=				{}



  def __init__( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							int	)			->		int:







       '''simple docstring'''



       A:		DoubleLinkedList[T, U]          =		DoubleLinkedList()
       A:		Any          =		capacity
       A:		List[Any]          =		0
       A:		Dict          =		0
       A:		Dict          =		0
       A:		dict[T, DoubleLinkedListNode[T, U]]          =		{}



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       return (
           f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
           f"""capacity={self.capacity}, current_size={self.num_keys})"""
       )



  def __contains__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							T	)			->		bool:







       '''simple docstring'''



       return key in self.cache



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							T	)			->		U | None:







       '''simple docstring'''



       if key in self.cache:
            self.hits += 1
            A:		DoubleLinkedListNode[T, U]          =		self.cache[key]
            A:		List[str]          =		self.list.remove(self.cache[key]	)
            assert node == value_node

            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(SCREAMING_SNAKE_CASE_	)
            return node.val
       self.miss += 1
       return None



  def 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							T					,  SCREAMING_SNAKE_CASE_				:							U	)			->		None:







       '''simple docstring'''



       if key not in self.cache:
            if self.num_keys >= self.capacity:
                 # delete first node when over capacity
                 A:		List[Any]          =		self.list.head.next

                 # guaranteed to have a non-None first node when num_keys > 0
                 # explain to type checker via assertions
                 assert first_node is not None
                 assert first_node.key is not None
                 assert self.list.remove(SCREAMING_SNAKE_CASE_	) is not None
                 # first_node guaranteed to be in list

                 del self.cache[first_node.key]
                 self.num_keys -= 1
            A:		Tuple          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
            self.list.add(self.cache[key]	)
            self.num_keys += 1

       else:
            A:		Optional[Any]          =		self.list.remove(self.cache[key]	)
            assert node is not None  # node guaranteed to be in list
            A:		List[str]          =		value
            self.list.add(SCREAMING_SNAKE_CASE_	)




  @classmethod
  def 					_snake_case     ( cls				:							type[LFUCache[T, U]]					,  SCREAMING_SNAKE_CASE_				:							int = 1_28	)			->		Callable[[Callable[[T], U]], Callable[..., U]]:







       '''simple docstring'''



       def cache_decorator_inner(SCREAMING_SNAKE_CASE_				:							Callable[[T], U]	) -> Callable[..., U]:
            def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE_				:							T	) -> U:
                 if func not in cls.decorator_function_to_instance_map:
                      A:		Tuple          =		LFUCache(SCREAMING_SNAKE_CASE_	)

                 A:		List[Any]          =		cls.decorator_function_to_instance_map[func].get(args[0]	)
                 if result is None:
                      A:		List[str]          =		func(*SCREAMING_SNAKE_CASE_	)
                      cls.decorator_function_to_instance_map[func].put(args[0]					,  SCREAMING_SNAKE_CASE_	)
                 return result

            def cache_info() -> LFUCache[T, U]:
                 return cls.decorator_function_to_instance_map[func]

            setattr(SCREAMING_SNAKE_CASE_					,  '''cache_info'''					,  SCREAMING_SNAKE_CASE_	)  # noqa: B010

            return cache_decorator_wrapper

       return cache_decorator_inner


if __name__ == "__main__":
     import doctest

     doctest.testmod()






