



import argparse

import requests
import torch
from PIL import Image

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





def 						__lowerCamelCase       (      __magic_name__	:  str						):
							a__:       int											=CLIPSegTextConfig()
							a__:       Tuple											=CLIPSegVisionConfig(patch_size=16						)

							a__:       Optional[int]											=True if '''refined''' in model_name else False
							a__:       Any											=16 if '''rd16''' in model_name else 64

							a__:       List[str]											=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       (      __magic_name__	:  Optional[Any]						):


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

							return name





def 						__lowerCamelCase       (      __magic_name__	:  Optional[int]     , __magic_name__	:  Any						):


							for key in orig_state_dict.copy().keys():
														a__:       Union[str, Any]											=orig_state_dict.pop(SCREAMING_SNAKE_CASE__						)

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

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

							return orig_state_dict





def 						__lowerCamelCase       (      ):
							a__:       int											='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							a__:       List[Any]											=Image.open(requests.get(SCREAMING_SNAKE_CASE__     , stream=SCREAMING_SNAKE_CASE__						).raw						)
							return image





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  Optional[int]     , __magic_name__	:  Optional[Any]     , __magic_name__	:  List[Any]						):
							a__:       Any											=get_clipseg_config(SCREAMING_SNAKE_CASE__						)
							a__:       int											=CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__						)
							model.eval()

							a__:       List[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__:       List[str]											=convert_state_dict(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__						)
							a__:       Dict											=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__:       Any											=ViTImageProcessor(size=352						)
							a__:       List[Any]											=CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"						)
							a__:       Optional[Any]											=CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__     , tokenizer=SCREAMING_SNAKE_CASE__						)

							a__:       List[Any]											=prepare_img()
							a__:       Union[str, Any]											=['''a glass''', '''something to fill''', '''wood''', '''a jar''']

							a__:       Any											=processor(text=SCREAMING_SNAKE_CASE__     , images=[image] * len(SCREAMING_SNAKE_CASE__						)     , padding="max_length"     , return_tensors="pt"						)

							with torch.no_grad():
														a__:       str											=model(**SCREAMING_SNAKE_CASE__						)

							# verify values
							a__:       Union[str, Any]											=torch.tensor([0.1_110, -0.1_882, 0.1_645]						)
							a__:       Union[str, Any]											=torch.tensor([0.2_692, -0.7_197, -0.1_328]						)
							if model_name == "clipseg-rd64-refined":
														a__:       int											=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":
														a__:       str											=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":
														a__:       Union[str, Any]											=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]     , 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__":
		__UpperCAmelCase						  =     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						  =     parser.parse_args()
		convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
		from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline





