






import argparse

import requests
import torch
from PIL import Image

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






def 				A	(			a_   )	->		Optional[Any]:
					__UpperCamelCase :   Union[str, Any]									=CLIPSegTextConfig()
					__UpperCamelCase :   str									=CLIPSegVisionConfig(patch_size=16   )

					__UpperCamelCase :   Any									=True if 'refined' in model_name else False
					__UpperCamelCase :   List[str]									=16 if 'rd16' in model_name else 64

					__UpperCamelCase :   Union[str, Any]									=CLIPSegConfig.from_text_vision_configs(
					    a_				,a_				,use_complex_transposed_convolution=a_				,reduce_dim=a_				,)
					return config






def 				A	(			a_   )	->		Any:

					if "clip_model" in name:
										__UpperCamelCase :   Optional[int]									=name.replace('clip_model'				,'clip'   )
					if "transformer" in name:
										if "visual" in name:
															__UpperCamelCase :   str									=name.replace('visual.transformer'				,'vision_model'   )
										else:
															__UpperCamelCase :   Optional[Any]									=name.replace('transformer'				,'text_model'   )
					if "resblocks" in name:
										__UpperCamelCase :   Optional[int]									=name.replace('resblocks'				,'encoder.layers'   )
					if "ln_1" in name:
										__UpperCamelCase :   Optional[Any]									=name.replace('ln_1'				,'layer_norm1'   )
					if "ln_2" in name:
										__UpperCamelCase :   List[str]									=name.replace('ln_2'				,'layer_norm2'   )
					if "c_fc" in name:
										__UpperCamelCase :   int									=name.replace('c_fc'				,'fc1'   )
					if "c_proj" in name:
										__UpperCamelCase :   str									=name.replace('c_proj'				,'fc2'   )
					if "attn" in name and "self" not in name:
										__UpperCamelCase :   Optional[int]									=name.replace('attn'				,'self_attn'   )
					# text encoder
					if "token_embedding" in name:
										__UpperCamelCase :   Any									=name.replace('token_embedding'				,'text_model.embeddings.token_embedding'   )
					if "positional_embedding" in name and "visual" not in name:
										__UpperCamelCase :   Tuple									=name.replace('positional_embedding'				,'text_model.embeddings.position_embedding.weight'   )
					if "ln_final" in name:
										__UpperCamelCase :   Optional[int]									=name.replace('ln_final'				,'text_model.final_layer_norm'   )
					# vision encoder
					if "visual.class_embedding" in name:
										__UpperCamelCase :   str									=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 :   Any									=name.replace('visual.positional_embedding'				,'vision_model.embeddings.position_embedding.weight'   )
					if "visual.ln_pre" in name:
										__UpperCamelCase :   Union[str, Any]									=name.replace('visual.ln_pre'				,'vision_model.pre_layrnorm'   )
					if "visual.ln_post" in name:
										__UpperCamelCase :   Tuple									=name.replace('visual.ln_post'				,'vision_model.post_layernorm'   )
					# projection layers
					if "visual.proj" in name:
										__UpperCamelCase :   int									=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 :   Optional[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 :   Tuple									='decoder.' + name
					if "blocks" in name:
										__UpperCamelCase :   Dict									=name.replace('blocks'				,'decoder.layers'   )
					if "linear1" in name:
										__UpperCamelCase :   List[str]									=name.replace('linear1'				,'mlp.fc1'   )
					if "linear2" in name:
										__UpperCamelCase :   Optional[int]									=name.replace('linear2'				,'mlp.fc2'   )
					if "norm1" in name and "layer_" not in name:
										__UpperCamelCase :   Union[str, Any]									=name.replace('norm1'				,'layer_norm1'   )
					if "norm2" in name and "layer_" not in name:
										__UpperCamelCase :   List[str]									=name.replace('norm2'				,'layer_norm2'   )

					return name






def 				A	(			a_				,a_   )	->		Union[str, Any]:

					for key in orig_state_dict.copy().keys():
										__UpperCamelCase :   int									=orig_state_dict.pop(a_   )

										if key.startswith('clip_model'   ) and "attn.in_proj" in key:
															__UpperCamelCase :   str									=key.split('.'   )
															if "visual" in key:
																				__UpperCamelCase :   Tuple									=int(key_split[4]   )
																				__UpperCamelCase :   int									=config.vision_config.hidden_size
																				__UpperCamelCase :   List[Any]									='vision_model'
															else:
																				__UpperCamelCase :   int									=int(key_split[3]   )
																				__UpperCamelCase :   Union[str, Any]									=config.text_config.hidden_size
																				__UpperCamelCase :   Optional[Any]									='text_model'

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

					return orig_state_dict






def 				A	(			)	->		List[Any]:
					__UpperCamelCase :   Optional[Any]									='http://images.cocodataset.org/val2017/000000039769.jpg'
					__UpperCamelCase :   List[Any]									=Image.open(requests.get(a_				,stream=a_   ).raw   )
					return image






def 				A	(			a_				,a_				,a_				,a_   )	->		int:
					__UpperCamelCase :   Union[str, Any]									=get_clipseg_config(a_   )
					__UpperCamelCase :   Dict									=CLIPSegForImageSegmentation(a_   )
					model.eval()

					__UpperCamelCase :   List[Any]									=torch.load(a_				,map_location='cpu'   )

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

    # rename some keys
					__UpperCamelCase :   int									=convert_state_dict(a_				,a_   )
					__UpperCamelCase						,							__UpperCamelCase :   Dict									=model.load_state_dict(a_				,strict=a_   )

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

					__UpperCamelCase :   List[str]									=ViTImageProcessor(size=352   )
					__UpperCamelCase :   Optional[int]									=CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'   )
					__UpperCamelCase :   str									=CLIPSegProcessor(image_processor=a_				,tokenizer=a_   )

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

					__UpperCamelCase :   Dict									=processor(text=a_				,images=[image] * len(a_   )				,padding='max_length'				,return_tensors='pt'   )

					with torch.no_grad():
										__UpperCamelCase :   Optional[Any]									=model(**a_   )

					# verify values
					__UpperCamelCase :   Dict									=torch.tensor([0.1_110, -0.1_882, 0.1_645]   )
					__UpperCamelCase :   Tuple									=torch.tensor([0.2_692, -0.7_197, -0.1_328]   )
					if model_name == "clipseg-rd64-refined":
										__UpperCamelCase :   Union[str, 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 :   Dict									=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 :   Dict									=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]				,a_				,atol=1e-3   )
					assert torch.allclose(outputs.conditional_embeddings[0, :3]				,a_				,atol=1e-3   )
					assert torch.allclose(outputs.pooled_output[0, :3]				,a_				,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(a_   )
										processor.save_pretrained(a_   )

					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__":
							A_			: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.'''
							)

							A_			:Dict	    =		parser.parse_args()
							convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		return int(lowercase					) if x - int(lowercase					) >= 0 else int(lowercase					) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()
