
'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def 				__magic_name__(					UpperCAmelCase__   :					Tuple  ):
						UpperCamelCase_    :  Dict				    =    CLIPSegTextConfig()
						UpperCamelCase_    :  List[str]				    =    CLIPSegVisionConfig(patch_size=16  )

						UpperCamelCase_    :  str				    =    True if """refined""" in model_name else False
						UpperCamelCase_    :  Any				    =    16 if """rd16""" in model_name else 64

						UpperCamelCase_    :  Optional[Any]				    =    CLIPSegConfig.from_text_vision_configs(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							use_complex_transposed_convolution=UpperCAmelCase__       ,							reduce_dim=UpperCAmelCase__       ,							)
						return config
def 				__magic_name__(					UpperCAmelCase__   :					Tuple  ):
						# update prefixes
						if "clip_model" in name:
												UpperCamelCase_    :  int				    =    name.replace("""clip_model"""       ,							"""clip"""  )
						if "transformer" in name:
												if "visual" in name:
																		UpperCamelCase_    :  str				    =    name.replace("""visual.transformer"""       ,							"""vision_model"""  )
												else:
																		UpperCamelCase_    :  Union[str, Any]				    =    name.replace("""transformer"""       ,							"""text_model"""  )
						if "resblocks" in name:
												UpperCamelCase_    :  Optional[int]				    =    name.replace("""resblocks"""       ,							"""encoder.layers"""  )
						if "ln_1" in name:
												UpperCamelCase_    :  List[Any]				    =    name.replace("""ln_1"""       ,							"""layer_norm1"""  )
						if "ln_2" in name:
												UpperCamelCase_    :  Dict				    =    name.replace("""ln_2"""       ,							"""layer_norm2"""  )
						if "c_fc" in name:
												UpperCamelCase_    :  Optional[Any]				    =    name.replace("""c_fc"""       ,							"""fc1"""  )
						if "c_proj" in name:
												UpperCamelCase_    :  Any				    =    name.replace("""c_proj"""       ,							"""fc2"""  )
						if "attn" in name and "self" not in name:
												UpperCamelCase_    :  Any				    =    name.replace("""attn"""       ,							"""self_attn"""  )
						# text encoder
						if "token_embedding" in name:
												UpperCamelCase_    :  int				    =    name.replace("""token_embedding"""       ,							"""text_model.embeddings.token_embedding"""  )
						if "positional_embedding" in name and "visual" not in name:
												UpperCamelCase_    :  Any				    =    name.replace("""positional_embedding"""       ,							"""text_model.embeddings.position_embedding.weight"""  )
						if "ln_final" in name:
												UpperCamelCase_    :  List[str]				    =    name.replace("""ln_final"""       ,							"""text_model.final_layer_norm"""  )
						# vision encoder
						if "visual.class_embedding" in name:
												UpperCamelCase_    :  Dict				    =    name.replace("""visual.class_embedding"""       ,							"""vision_model.embeddings.class_embedding"""  )
						if "visual.conv1" in name:
												UpperCamelCase_    :  Dict				    =    name.replace("""visual.conv1"""       ,							"""vision_model.embeddings.patch_embedding"""  )
						if "visual.positional_embedding" in name:
												UpperCamelCase_    :  Optional[Any]				    =    name.replace("""visual.positional_embedding"""       ,							"""vision_model.embeddings.position_embedding.weight"""  )
						if "visual.ln_pre" in name:
												UpperCamelCase_    :  List[Any]				    =    name.replace("""visual.ln_pre"""       ,							"""vision_model.pre_layrnorm"""  )
						if "visual.ln_post" in name:
												UpperCamelCase_    :  Dict				    =    name.replace("""visual.ln_post"""       ,							"""vision_model.post_layernorm"""  )
						# projection layers
						if "visual.proj" in name:
												UpperCamelCase_    :  List[Any]				    =    name.replace("""visual.proj"""       ,							"""visual_projection.weight"""  )
						if "text_projection" in name:
												UpperCamelCase_    :  str				    =    name.replace("""text_projection"""       ,							"""text_projection.weight"""  )
						# decoder
						if "trans_conv" in name:
												UpperCamelCase_    :  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:
												UpperCamelCase_    :  List[str]				    =    """decoder.""" + name
						if "blocks" in name:
												UpperCamelCase_    :  int				    =    name.replace("""blocks"""       ,							"""decoder.layers"""  )
						if "linear1" in name:
												UpperCamelCase_    :  Union[str, Any]				    =    name.replace("""linear1"""       ,							"""mlp.fc1"""  )
						if "linear2" in name:
												UpperCamelCase_    :  Dict				    =    name.replace("""linear2"""       ,							"""mlp.fc2"""  )
						if "norm1" in name and "layer_" not in name:
												UpperCamelCase_    :  List[str]				    =    name.replace("""norm1"""       ,							"""layer_norm1"""  )
						if "norm2" in name and "layer_" not in name:
												UpperCamelCase_    :  Union[str, Any]				    =    name.replace("""norm2"""       ,							"""layer_norm2"""  )

						return name
def 				__magic_name__(					UpperCAmelCase__   :					Tuple       ,							UpperCAmelCase__   :					Any  ):
						for key in orig_state_dict.copy().keys():
												UpperCamelCase_    :  List[str]				    =    orig_state_dict.pop(UpperCAmelCase__  )

												if key.startswith("""clip_model"""  ) and "attn.in_proj" in key:
																		UpperCamelCase_    :  int				    =    key.split("""."""  )
																		if "visual" in key:
																								UpperCamelCase_    :  List[str]				    =    int(key_split[4]  )
																								UpperCamelCase_    :  int				    =    config.vision_config.hidden_size
																								UpperCamelCase_    :  List[str]				    =    """vision_model"""
																		else:
																								UpperCamelCase_    :  str				    =    int(key_split[3]  )
																								UpperCamelCase_    :  Dict				    =    config.text_config.hidden_size
																								UpperCamelCase_    :  Dict				    =    """text_model"""

																		if "weight" in key:
																								UpperCamelCase_    :  List[Any]				    =    val[:dim, :]
																								UpperCamelCase_    :  Union[str, Any]				    =    val[
																								    dim : dim * 2, :
																								]
																								UpperCamelCase_    :  str				    =    val[-dim:, :]
																		else:
																								UpperCamelCase_    :  List[Any]				    =    val[:dim]
																								UpperCamelCase_    :  Dict				    =    val[dim : dim * 2]
																								UpperCamelCase_    :  Any				    =    val[-dim:]
												elif "self_attn" in key and "out_proj" not in key:
																		UpperCamelCase_    :  List[str]				    =    key.split("""."""  )
																		UpperCamelCase_    :  Union[str, Any]				    =    int(key_split[1]  )
																		UpperCamelCase_    :  Optional[int]				    =    config.reduce_dim
																		if "weight" in key:
																								UpperCamelCase_    :  Optional[Any]				    =    val[:dim, :]
																								UpperCamelCase_    :  List[Any]				    =    val[dim : dim * 2, :]
																								UpperCamelCase_    :  Union[str, Any]				    =    val[-dim:, :]
																		else:
																								UpperCamelCase_    :  Optional[Any]				    =    val[:dim]
																								UpperCamelCase_    :  Dict				    =    val[dim : dim * 2]
																								UpperCamelCase_    :  int				    =    val[-dim:]
												else:
																		UpperCamelCase_    :  List[Any]				    =    rename_key(UpperCAmelCase__  )
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								UpperCamelCase_    :  str				    =    val.T
																		UpperCamelCase_    :  str				    =    val

						return orig_state_dict
def 				__magic_name__(					):
						UpperCamelCase_    :  List[Any]				    =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase_    :  Optional[int]				    =    Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__  ).raw  )
						return image
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]       ,							UpperCAmelCase__   :					Dict       ,							UpperCAmelCase__   :					int       ,							UpperCAmelCase__   :					Optional[Any]  ):
						UpperCamelCase_    :  Tuple				    =    get_clipseg_config(UpperCAmelCase__  )
						UpperCamelCase_    :  Optional[Any]				    =    CLIPSegForImageSegmentation(UpperCAmelCase__  )
						model.eval()

						UpperCamelCase_    :  List[str]				    =    torch.load(UpperCAmelCase__       ,							map_location="""cpu"""  )

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

    # rename some keys
						UpperCamelCase_    :  List[str]				    =    convert_state_dict(UpperCAmelCase__       ,							UpperCAmelCase__  )
						UpperCamelCase_    ,UpperCamelCase_    :  Union[str, Any]				    =    model.load_state_dict(UpperCAmelCase__       ,							strict=UpperCAmelCase__  )

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

						UpperCamelCase_    :  Tuple				    =    ViTImageProcessor(size=352  )
						UpperCamelCase_    :  int				    =    CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""  )
						UpperCamelCase_    :  int				    =    CLIPSegProcessor(image_processor=UpperCAmelCase__       ,							tokenizer=UpperCAmelCase__  )

						UpperCamelCase_    :  Union[str, Any]				    =    prepare_img()
						UpperCamelCase_    :  Optional[int]				    =    ["""a glass""", """something to fill""", """wood""", """a jar"""]

						UpperCamelCase_    :  List[str]				    =    processor(text=UpperCAmelCase__       ,							images=[image] * len(UpperCAmelCase__  )       ,							padding="""max_length"""       ,							return_tensors="""pt"""  )

						with torch.no_grad():
												UpperCamelCase_    :  Tuple				    =    model(**UpperCAmelCase__  )

						# verify values
						UpperCamelCase_    :  str				    =    torch.tensor([0.1_110, -0.1_882, 0.1_645]  )
						UpperCamelCase_    :  List[Any]				    =    torch.tensor([0.2_692, -0.7_197, -0.1_328]  )
						if model_name == "clipseg-rd64-refined":
												UpperCamelCase_    :  Any				    =    torch.tensor(
												    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]  )
						elif model_name == "clipseg-rd64":
												UpperCamelCase_    :  List[Any]				    =    torch.tensor(
												    [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]  )
						elif model_name == "clipseg-rd16":
												UpperCamelCase_    :  List[str]				    =    torch.tensor(
												    [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]  )
						else:
												raise ValueError(f"""Model name {model_name} not supported."""  )

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

						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 typing import Any




class 						__snake_case :




		def __init__(     self				:int ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:  # noqa: A002






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    item
								UpperCamelCase_    :  int				    =    next







class 						__snake_case :




		def __init__(     self				:Optional[Any])				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  Node | None				    =    None
								UpperCamelCase_    :  Tuple				    =    0




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    Node(_SCREAMING_SNAKE_CASE ,						self.head)
								self.size += 1




		def   _snake_case  (     self				:int)				-> Any:






								'''simple docstring'''






								if self.head is None:
														return None
								else:
														UpperCamelCase_    :  Any				    =    self.head.item
														UpperCamelCase_    :  List[str]				    =    self.head.next
														self.size -= 1
														return item




		def   _snake_case  (     self				:int)				-> bool:






								'''simple docstring'''






								return self.head is None




		def __str__(     self				:Any)				-> str:






								'''simple docstring'''






								if self.is_empty():
														return ""
								else:
														UpperCamelCase_    :  List[str]				    =    self.head
														UpperCamelCase_    :  Tuple				    =    """"""
														UpperCamelCase_    :  list[str]				    =    []
														while iterate:
																				item_list.append(str(iterate.item))
																				UpperCamelCase_    :  Tuple				    =    iterate.next

														UpperCamelCase_    :  Optional[int]				    =    """ --> """.join(_SCREAMING_SNAKE_CASE)

														return item_str





		def __len__(     self				:List[Any])				-> int:






								'''simple docstring'''






								return self.size




