"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

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


def   a__      (		UpperCAmelCase__       :			int			)->	Optional[Any]:
						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(
						    UpperCAmelCase__		,			UpperCAmelCase__		,			use_complex_transposed_convolution=UpperCAmelCase__		,			reduce_dim=UpperCAmelCase__		,			)
						return config


def   a__      (		UpperCAmelCase__       :			int			)->	Union[str, Any]:
						# update prefixes
						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__      (		UpperCAmelCase__       :			Optional[int]		,			UpperCAmelCase__       :			Tuple			)->	str:
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase__						=		orig_state_dict.pop(UpperCAmelCase__			)

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

						return orig_state_dict


def   a__      (		)->	List[str]:
						UpperCAmelCase__						=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase__						=		Image.open(requests.get(UpperCAmelCase__		,			stream=UpperCAmelCase__			).raw			)
						return image


def   a__      (		UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			int			)->	List[str]:
						UpperCAmelCase__						=		get_clipseg_config(UpperCAmelCase__			)
						UpperCAmelCase__						=		CLIPSegForImageSegmentation(UpperCAmelCase__			)
						model.eval()

						UpperCAmelCase__						=		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__						=		convert_state_dict(UpperCAmelCase__		,			UpperCAmelCase__			)
						UpperCAmelCase__	,      UpperCAmelCase__						=		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__						=		ViTImageProcessor(size=3_5_2			)
						UpperCAmelCase__						=		CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			)
						UpperCAmelCase__						=		CLIPSegProcessor(image_processor=UpperCAmelCase__		,			tokenizer=UpperCAmelCase__			)

						UpperCAmelCase__						=		prepare_img()
						UpperCAmelCase__						=		["""a glass""", """something to fill""", """wood""", """a jar"""]

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

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

						# verify values
						UpperCAmelCase__						=		torch.tensor([0.11_10, -0.18_82, 0.16_45]			)
						UpperCAmelCase__						=		torch.tensor([0.26_92, -0.71_97, -0.13_28]			)
						if model_name == "clipseg-rd64-refined":
												UpperCAmelCase__						=		torch.tensor(
												    [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]			)
						elif model_name == "clipseg-rd64":
												UpperCAmelCase__						=		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":
												UpperCAmelCase__						=		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]		,			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__":
						UpperCamelCase_       :       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."""
						)

						UpperCamelCase_       :       Union[str, Any]							   =		parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""







from ..utils import DummyObject, requires_backends


class 			_lowercase		(   metaclass=__lowerCamelCase  ):
				lowerCamelCase__						=       ['torch', 'transformers', 'invisible_watermark']

				def __init__(     self       :    Tuple       ,    *_lowercase       :    int       ,    **_lowercase       :    List[Any]     ):


										'''simple docstring'''
										requires_backends(self       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    Tuple       ,    *_lowercase       :    Dict       ,    **_lowercase       :    Union[str, Any]     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    Tuple       ,    *_lowercase       :    Tuple       ,    **_lowercase       :    Any     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )


class 			_lowercase		(   metaclass=__lowerCamelCase  ):
				lowerCamelCase__						=       ['torch', 'transformers', 'invisible_watermark']

				def __init__(     self       :    Union[str, Any]       ,    *_lowercase       :    List[Any]       ,    **_lowercase       :    List[Any]     ):


										'''simple docstring'''
										requires_backends(self       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    List[str]       ,    *_lowercase       :    Any       ,    **_lowercase       :    Tuple     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    List[Any]       ,    *_lowercase       :    Optional[Any]       ,    **_lowercase       :    Union[str, Any]     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

