






"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

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




def 						lowerCamelCase_				(  __A  :		Optional[Any]			):
					lowerCamelCase__				=CLIPSegTextConfig()
					lowerCamelCase__				=CLIPSegVisionConfig(patch_size=16			)

					lowerCamelCase__				=True if '''refined''' in model_name else False
					lowerCamelCase__				=16 if '''rd16''' in model_name else 64

					lowerCamelCase__				=CLIPSegConfig.from_text_vision_configs(
					    lowercase_			,	lowercase_			,	use_complex_transposed_convolution=lowercase_			,	reduce_dim=lowercase_			,	)
					return config




def 						lowerCamelCase_				(  __A  :		str			):
					# update prefixes
					if "clip_model" in name:
										lowerCamelCase__				=name.replace('clip_model'			,	'clip'			)
					if "transformer" in name:
										if "visual" in name:
															lowerCamelCase__				=name.replace('visual.transformer'			,	'vision_model'			)
										else:
															lowerCamelCase__				=name.replace('transformer'			,	'text_model'			)
					if "resblocks" in name:
										lowerCamelCase__				=name.replace('resblocks'			,	'encoder.layers'			)
					if "ln_1" in name:
										lowerCamelCase__				=name.replace('ln_1'			,	'layer_norm1'			)
					if "ln_2" in name:
										lowerCamelCase__				=name.replace('ln_2'			,	'layer_norm2'			)
					if "c_fc" in name:
										lowerCamelCase__				=name.replace('c_fc'			,	'fc1'			)
					if "c_proj" in name:
										lowerCamelCase__				=name.replace('c_proj'			,	'fc2'			)
					if "attn" in name and "self" not in name:
										lowerCamelCase__				=name.replace('attn'			,	'self_attn'			)
					# text encoder
					if "token_embedding" in name:
										lowerCamelCase__				=name.replace('token_embedding'			,	'text_model.embeddings.token_embedding'			)
					if "positional_embedding" in name and "visual" not in name:
										lowerCamelCase__				=name.replace('positional_embedding'			,	'text_model.embeddings.position_embedding.weight'			)
					if "ln_final" in name:
										lowerCamelCase__				=name.replace('ln_final'			,	'text_model.final_layer_norm'			)
					# vision encoder
					if "visual.class_embedding" in name:
										lowerCamelCase__				=name.replace('visual.class_embedding'			,	'vision_model.embeddings.class_embedding'			)
					if "visual.conv1" in name:
										lowerCamelCase__				=name.replace('visual.conv1'			,	'vision_model.embeddings.patch_embedding'			)
					if "visual.positional_embedding" in name:
										lowerCamelCase__				=name.replace('visual.positional_embedding'			,	'vision_model.embeddings.position_embedding.weight'			)
					if "visual.ln_pre" in name:
										lowerCamelCase__				=name.replace('visual.ln_pre'			,	'vision_model.pre_layrnorm'			)
					if "visual.ln_post" in name:
										lowerCamelCase__				=name.replace('visual.ln_post'			,	'vision_model.post_layernorm'			)
					# projection layers
					if "visual.proj" in name:
										lowerCamelCase__				=name.replace('visual.proj'			,	'visual_projection.weight'			)
					if "text_projection" in name:
										lowerCamelCase__				=name.replace('text_projection'			,	'text_projection.weight'			)
					# decoder
					if "trans_conv" in name:
										lowerCamelCase__				=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:
										lowerCamelCase__				='''decoder.''' + name
					if "blocks" in name:
										lowerCamelCase__				=name.replace('blocks'			,	'decoder.layers'			)
					if "linear1" in name:
										lowerCamelCase__				=name.replace('linear1'			,	'mlp.fc1'			)
					if "linear2" in name:
										lowerCamelCase__				=name.replace('linear2'			,	'mlp.fc2'			)
					if "norm1" in name and "layer_" not in name:
										lowerCamelCase__				=name.replace('norm1'			,	'layer_norm1'			)
					if "norm2" in name and "layer_" not in name:
										lowerCamelCase__				=name.replace('norm2'			,	'layer_norm2'			)

					return name




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		List[str]			):
					for key in orig_state_dict.copy().keys():
										lowerCamelCase__				=orig_state_dict.pop(lowercase_			)

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

															if "weight" in key:
																				lowerCamelCase__				=val[:dim, :]
																				lowerCamelCase__				=val[
																				    dim : dim * 2, :
																				]
																				lowerCamelCase__				=val[-dim:, :]
															else:
																				lowerCamelCase__				=val[:dim]
																				lowerCamelCase__				=val[dim : dim * 2]
																				lowerCamelCase__				=val[-dim:]
										elif "self_attn" in key and "out_proj" not in key:
															lowerCamelCase__				=key.split('.'			)
															lowerCamelCase__				=int(key_split[1]			)
															lowerCamelCase__				=config.reduce_dim
															if "weight" in key:
																				lowerCamelCase__				=val[:dim, :]
																				lowerCamelCase__				=val[dim : dim * 2, :]
																				lowerCamelCase__				=val[-dim:, :]
															else:
																				lowerCamelCase__				=val[:dim]
																				lowerCamelCase__				=val[dim : dim * 2]
																				lowerCamelCase__				=val[-dim:]
										else:
															lowerCamelCase__				=rename_key(lowercase_			)
															if "visual_projection" in new_name or "text_projection" in new_name:
																				lowerCamelCase__				=val.T
															lowerCamelCase__				=val

					return orig_state_dict




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				='''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCamelCase__				=Image.open(requests.get(lowercase_			,	stream=lowercase_			).raw			)
					return image




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		Any			,	__A  :		Tuple			,	__A  :		int			):
					lowerCamelCase__				=get_clipseg_config(lowercase_			)
					lowerCamelCase__				=CLIPSegForImageSegmentation(lowercase_			)
					model.eval()

					lowerCamelCase__				=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
					lowerCamelCase__				=convert_state_dict(lowercase_			,	lowercase_			)
					lowerCamelCase__				=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}"			)

					lowerCamelCase__				=ViTImageProcessor(size=352			)
					lowerCamelCase__				=CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'			)
					lowerCamelCase__				=CLIPSegProcessor(image_processor=lowercase_			,	tokenizer=lowercase_			)

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

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

					with torch.no_grad():
										lowerCamelCase__				=model(**lowercase_			)

					# verify values
					lowerCamelCase__				=torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]			)
					lowerCamelCase__				=torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]			)
					if model_name == "clipseg-rd64-refined":
										lowerCamelCase__				=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":
										lowerCamelCase__				=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":
										lowerCamelCase__				=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]			,	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__":
						_snake_case       :      List[str]       =					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."
						)

						_snake_case       :      Any       =					parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""



_snake_case       :      Dict       =					[0.485, 0.456, 0.406]
_snake_case       :      int       =					[0.229, 0.224, 0.225]
_snake_case       :      Optional[int]       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.4814_5466, 0.457_8275, 0.4082_1073]
_snake_case       :      str       =					[0.2686_2954, 0.2613_0258, 0.2757_7711]