


'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

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






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]     ):
					__a    : Optional[int]      					=						CLIPSegTextConfig()
					__a    : Dict      					=						CLIPSegVisionConfig(patch_size=16     )

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

					__a    : Optional[int]      					=						CLIPSegConfig.from_text_vision_configs(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   use_complex_transposed_convolution=_SCREAMING_SNAKE_CASE		,   reduce_dim=_SCREAMING_SNAKE_CASE		,   )
					return config






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

					return name






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
					for key in orig_state_dict.copy().keys():
										__a    : int      					=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE     )

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

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

					return orig_state_dict






def 						lowerCamelCase							():
					__a    : Optional[int]      					=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__a    : Dict      					=						Image.open(requests.get(_SCREAMING_SNAKE_CASE		,   stream=_SCREAMING_SNAKE_CASE     ).raw     )
					return image






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[Any]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[Any]     ):
					__a    : List[Any]      					=						get_clipseg_config(_SCREAMING_SNAKE_CASE     )
					__a    : Optional[Any]      					=						CLIPSegForImageSegmentation(_SCREAMING_SNAKE_CASE     )
					model.eval()

					__a    : str      					=						torch.load(_SCREAMING_SNAKE_CASE		,   map_location='cpu'     )

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

    # rename some keys
					__a    : Optional[int]      					=						convert_state_dict(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
					__a    : List[str]      					=						model.load_state_dict(_SCREAMING_SNAKE_CASE		,   strict=_SCREAMING_SNAKE_CASE     )

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

					__a    : str      					=						ViTImageProcessor(size=352     )
					__a    : List[str]      					=						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     )
					__a    : Union[str, Any]      					=						CLIPSegProcessor(image_processor=_SCREAMING_SNAKE_CASE		,   tokenizer=_SCREAMING_SNAKE_CASE     )

					__a    : Any      					=						prepare_img()
					__a    : Tuple      					=						['''a glass''', '''something to fill''', '''wood''', '''a jar''']

					__a    : List[Any]      					=						processor(text=_SCREAMING_SNAKE_CASE		,   images=[image] * len(_SCREAMING_SNAKE_CASE     )		,   padding='max_length'		,   return_tensors='pt'     )

					with torch.no_grad():
										__a    : Optional[int]      					=						model(**_SCREAMING_SNAKE_CASE     )

					# verify values
					__a    : Optional[Any]      					=						torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]     )
					__a    : List[str]      					=						torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]     )
					if model_name == "clipseg-rd64-refined":
										__a    : Optional[Any]      					=						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":
										__a    : Optional[int]      					=						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":
										__a    : int      					=						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]		,   _SCREAMING_SNAKE_CASE		,   atol=1e-3     )
					assert torch.allclose(outputs.conditional_embeddings[0, :3]		,   _SCREAMING_SNAKE_CASE		,   atol=1e-3     )
					assert torch.allclose(outputs.pooled_output[0, :3]		,   _SCREAMING_SNAKE_CASE		,   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(_SCREAMING_SNAKE_CASE     )
										processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

					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__":
		__lowercase			:		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.'
		)

		__lowercase			:		Tuple =			parser.parse_args()
		convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''



def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
					__a    : Dict      					=						exponent + 2
					__a    : List[Any]      					=						(1 + 5**0.5) / 2
					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
	print(f'''{solution() = }''')



