import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


_lowerCAmelCase       =		{
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
    "image_encoder": "vision_encoder",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",
    "patch_embed.proj": "patch_embed.projection",
    ".norm": ".layer_norm",
    "blocks": "layers",
}
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     {}
					state_dict.pop('''pixel_mean'''  ,     __snake_case						)
					state_dict.pop('''pixel_std'''  ,     __snake_case						)

					_UpperCamelCase			     =     R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

					for key, value in state_dict.items():
										for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
															if key_to_modify in key:
																				_UpperCamelCase			     =     key.replace(__snake_case  ,     __snake_case						)

										if re.match(__snake_case  ,     __snake_case						):
															_UpperCamelCase			     =     int(re.match(__snake_case  ,     __snake_case						).group(2						)						)
															if layer_nb == 0:
																				_UpperCamelCase			     =     key.replace('''layers.0'''  ,     '''proj_in'''						)
															elif layer_nb == 1:
																				_UpperCamelCase			     =     key.replace('''layers.1'''  ,     '''layers.0'''						)
															elif layer_nb == 2:
																				_UpperCamelCase			     =     key.replace('''layers.2'''  ,     '''proj_out'''						)

										_UpperCamelCase			     =     value

					_UpperCamelCase			     =     model_state_dict[
					    '''prompt_encoder.shared_embedding.positional_embedding'''
					]

					return model_state_dict
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case="ybelkada/segment-anything"						):
					_UpperCamelCase			     =     hf_hub_download(__snake_case  ,     f"""checkpoints/{model_name}.pth"""						)

					if "sam_vit_b" in model_name:
										_UpperCamelCase			     =     SamConfig()
					elif "sam_vit_l" in model_name:
										_UpperCamelCase			     =     SamVisionConfig(
										    hidden_size=1024  ,     num_hidden_layers=24  ,     num_attention_heads=16  ,     global_attn_indexes=[5, 11, 17, 23]  ,     )

										_UpperCamelCase			     =     SamConfig(
										    vision_config=__snake_case  ,     )
					elif "sam_vit_h" in model_name:
										_UpperCamelCase			     =     SamVisionConfig(
										    hidden_size=1280  ,     num_hidden_layers=32  ,     num_attention_heads=16  ,     global_attn_indexes=[7, 15, 23, 31]  ,     )

										_UpperCamelCase			     =     SamConfig(
										    vision_config=__snake_case  ,     )

					_UpperCamelCase			     =     torch.load(__snake_case  ,     map_location='''cpu'''						)
					_UpperCamelCase			     =     replace_keys(__snake_case						)

					_UpperCamelCase			     =     SamImageProcessor()

					_UpperCamelCase			     =     SamProcessor(image_processor=__snake_case						)
					_UpperCamelCase			     =     SamModel(__snake_case						)

					hf_model.load_state_dict(__snake_case						)
					_UpperCamelCase			     =     hf_model.to('''cuda'''						)

					_UpperCamelCase			     =     '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
					_UpperCamelCase			     =     Image.open(requests.get(__snake_case  ,     stream=__snake_case						).raw						).convert('''RGB'''						)

					_UpperCamelCase			     =     [[[400, 650]]]
					_UpperCamelCase			     =     [[1]]

					_UpperCamelCase			     =     processor(images=np.array(__snake_case						)  ,     return_tensors='''pt'''						).to('''cuda'''						)

					with torch.no_grad():
										_UpperCamelCase			     =     hf_model(**__snake_case						)
					_UpperCamelCase			     =     output.iou_scores.squeeze()

					if model_name == "sam_vit_h_4b8939":
										assert scores[-1].item() == 0.579890251159668

										_UpperCamelCase			     =     processor(
										    images=np.array(__snake_case						)  ,     input_points=__snake_case  ,     input_labels=__snake_case  ,     return_tensors='''pt'''						).to('''cuda'''						)

										with torch.no_grad():
															_UpperCamelCase			     =     hf_model(**__snake_case						)
										_UpperCamelCase			     =     output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9712603092193604

										_UpperCamelCase			     =     ((75, 275, 1725, 850),)

										_UpperCamelCase			     =     processor(images=np.array(__snake_case						)  ,     input_boxes=__snake_case  ,     return_tensors='''pt'''						).to('''cuda'''						)

										with torch.no_grad():
															_UpperCamelCase			     =     hf_model(**__snake_case						)
										_UpperCamelCase			     =     output.iou_scores.squeeze()

										assert scores[-1].item() == 0.8686015605926514

										# Test with 2 points and 1 image.
										_UpperCamelCase			     =     [[[400, 650], [800, 650]]]
										_UpperCamelCase			     =     [[1, 1]]

										_UpperCamelCase			     =     processor(
										    images=np.array(__snake_case						)  ,     input_points=__snake_case  ,     input_labels=__snake_case  ,     return_tensors='''pt'''						).to('''cuda'''						)

										with torch.no_grad():
															_UpperCamelCase			     =     hf_model(**__snake_case						)
										_UpperCamelCase			     =     output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
					_lowerCAmelCase       =		argparse.ArgumentParser()
					_lowerCAmelCase       =		["sam_vit_b_01ec64", "sam_vit_h_4b8939", "sam_vit_l_0b3195"]
					parser.add_argument(
					    "--model_name",
					    default="sam_vit_h_4b8939",
					    choices=choices,
					    type=str,
					    help="Path to hf config.json of model to convert",
					)
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether to push the model and processor to the hub after converting",
					)
					parser.add_argument(
					    "--model_hub_id",
					    default="ybelkada/segment-anything",
					    choices=choices,
					    type=str,
					    help="Path to hf config.json of model to convert",
					)

					_lowerCAmelCase       =		parser.parse_args()

					convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)


"""simple docstring"""

import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ = "cpu" ,							lowercase_ = "openai/clip-vit-large-patch14"				)    ->       None:
          UpperCAmelCase          =       device
          UpperCAmelCase          =       CLIPTokenizerFast.from_pretrained(lowercase_				)
          UpperCAmelCase          =       [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
          UpperCAmelCase          =       [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
          UpperCAmelCase          =       torchvision.transforms.Normalize(self.image_mean ,							self.image_std				)
          UpperCAmelCase          =       torchvision.transforms.Resize(2_2_4				)
          UpperCAmelCase          =       torchvision.transforms.CenterCrop(2_2_4				)




      def      a_   (				self ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       self.resize(lowercase_				)
          UpperCAmelCase          =       self.center_crop(lowercase_				)
          UpperCAmelCase          =       self.normalize(lowercase_				)
          return images




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.tokenizer(text=lowercase_ ,							**lowercase_				)
          UpperCAmelCase          =       self.preprocess_img(lowercase_				)
          UpperCAmelCase          =       {key: value.to(self.device				) for (key, value) in encoding.items()}
          return encoding

class   _UpperCAmelCase				(		nn.Module				):




      def __init__(				self ,							lowercase_=1_0 ,							lowercase_=0.0_1 ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=False ,							lowercase_=True ,							lowercase_="image" ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							)    ->       None:
          super().__init__()
          UpperCAmelCase          =       None
          UpperCAmelCase          =       device if device else get_device()
          if vqgan:
              UpperCAmelCase          =       vqgan
          else:
              UpperCAmelCase          =       load_vqgan(self.device ,							conf_path=lowercase_ ,							ckpt_path=lowercase_				)
          self.vqgan.eval()
          if clip:
              UpperCAmelCase          =       clip
          else:
              UpperCAmelCase          =       CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
          self.clip.to(self.device				)
          UpperCAmelCase          =       ProcessorGradientFlow(device=self.device				)

          UpperCAmelCase          =       iterations
          UpperCAmelCase          =       lr
          UpperCAmelCase          =       log
          UpperCAmelCase          =       make_grid
          UpperCAmelCase          =       return_val
          UpperCAmelCase          =       quantize
          UpperCAmelCase          =       self.vqgan.decoder.z_shape




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=5 ,							lowercase_=True				)    ->       Dict:
          UpperCAmelCase          =       []
          if output_path is None:
              UpperCAmelCase          =       './animation.gif'
          if input_path is None:
              UpperCAmelCase          =       self.save_path
          UpperCAmelCase          =       sorted(glob(input_path + '/*'				)				)
          if not len(lowercase_				):
              raise ValueError(
                  'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                  ' function?)'				)
          if len(lowercase_				) == 1:
              print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
          UpperCAmelCase          =       total_duration / len(lowercase_				)
          UpperCAmelCase          =       [frame_duration] * len(lowercase_				)
          if extend_frames:
              UpperCAmelCase          =       1.5
              UpperCAmelCase          =       3
          for file_name in paths:
              if file_name.endswith('.png'				):
                  images.append(imageio.imread(lowercase_				)				)
          imageio.mimsave(lowercase_ ,							lowercase_ ,							duration=lowercase_				)
          print(F"gif saved to {output_path}"				)




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None				)    ->       List[Any]:
          if not (path or img):
              raise ValueError('Input either path or tensor'				)
          if img is not None:
              raise NotImplementedError
          UpperCAmelCase          =       preprocess(Image.open(lowercase_				) ,							target_image_size=2_5_6				).to(self.device				)
          UpperCAmelCase          =       preprocess_vqgan(lowercase_				)
          UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.encode(lowercase_				)
          return z




      def      a_   (				self ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       self.latent.detach().requires_grad_()
          UpperCAmelCase          =       base_latent + transform_vector
          if self.quantize:
              UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.quantize(lowercase_				)
          else:
              UpperCAmelCase          =       trans_latent
          return self.vqgan.decode(lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=None				)    ->       str:
          UpperCAmelCase          =       self.clip_preprocessor(text=lowercase_ ,							images=lowercase_ ,							return_tensors='pt' ,							padding=lowercase_				)
          UpperCAmelCase          =       self.clip(**lowercase_				)
          UpperCAmelCase          =       clip_outputs.logits_per_image
          if weights is not None:
              UpperCAmelCase          =       similarity_logits * weights
          return similarity_logits.sum()




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[Any]:
          UpperCAmelCase          =       self._get_clip_similarity(pos_prompts['prompts'] ,							lowercase_ ,							weights=(1 / pos_prompts['weights'])				)
          if neg_prompts:
              UpperCAmelCase          =       self._get_clip_similarity(neg_prompts['prompts'] ,							lowercase_ ,							weights=neg_prompts['weights']				)
          else:
              UpperCAmelCase          =       torch.tensor([1] ,							device=self.device				)
          UpperCAmelCase          =       -torch.log(lowercase_				) + torch.log(lowercase_				)
          return loss




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Tuple:
          UpperCAmelCase          =       torch.randn_like(self.latent ,							requires_grad=lowercase_ ,							device=self.device				)
          UpperCAmelCase          =       torch.optim.Adam([vector] ,							lr=self.lr				)

          for i in range(self.iterations				):
              optim.zero_grad()
              UpperCAmelCase          =       self._add_vector(lowercase_				)
              UpperCAmelCase          =       loop_post_process(lowercase_				)
              UpperCAmelCase          =       self._get_CLIP_loss(lowercase_ ,							lowercase_ ,							lowercase_				)
              print('CLIP loss' ,							lowercase_				)
              if self.log:
                  wandb.log({'CLIP Loss': clip_loss}				)
              clip_loss.backward(retain_graph=lowercase_				)
              optim.step()
              if self.return_val == "image":
                  yield custom_to_pil(transformed_img[0]				)
              else:
                  yield vector




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          wandb.init(reinit=lowercase_ ,							project='face-editor'				)
          wandb.config.update({'Positive Prompts': positive_prompts}				)
          wandb.config.update({'Negative Prompts': negative_prompts}				)
          wandb.config.update({'lr': self.lr, 'iterations': self.iterations}				)
          if image_path:
              UpperCAmelCase          =       Image.open(lowercase_				)
              UpperCAmelCase          =       image.resize((2_5_6, 2_5_6)				)
              wandb.log('Original Image' ,							wandb.Image(lowercase_				)				)




      def      a_   (				self ,							lowercase_				)    ->       Tuple:
          if not prompts:
              return []
          UpperCAmelCase          =       []
          UpperCAmelCase          =       []
          if isinstance(lowercase_ ,							lowercase_				):
              UpperCAmelCase          =       [prompt.strip() for prompt in prompts.split('|'				)]
          for prompt in prompts:
              if isinstance(lowercase_ ,							(tuple, list)				):
                  UpperCAmelCase          =       prompt[0]
                  UpperCAmelCase          =       float(prompt[1]				)
              elif ":" in prompt:
                  UpperCAmelCase      ,				UpperCAmelCase          =       prompt.split(':'				)
                  UpperCAmelCase          =       float(lowercase_				)
              else:
                  UpperCAmelCase          =       prompt
                  UpperCAmelCase          =       1.0
              processed_prompts.append(lowercase_				)
              weights.append(lowercase_				)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(lowercase_ ,							device=self.device				),
          }




      def      a_   (				self ,							lowercase_ ,							lowercase_=None ,							lowercase_=None ,							lowercase_=True ,							lowercase_=False ,							lowercase_=True ,							lowercase_=True ,							lowercase_=None ,							)    ->       List[str]:
          if image_path:
              UpperCAmelCase          =       self._get_latent(lowercase_				)
          else:
              UpperCAmelCase          =       torch.randn(self.latent_dim ,							device=self.device				)
          if self.log:
              self._init_logging(lowercase_ ,							lowercase_ ,							lowercase_				)

          assert pos_prompts, "You must provide at least one positive prompt."
          UpperCAmelCase          =       self.process_prompts(lowercase_				)
          UpperCAmelCase          =       self.process_prompts(lowercase_				)
          if save_final and save_path is None:
              UpperCAmelCase          =       os.path.join('./outputs/' ,							'_'.join(pos_prompts['prompts']				)				)
          if not os.path.exists(lowercase_				):
              os.makedirs(lowercase_				)
          else:
              UpperCAmelCase          =       save_path + '_' + get_timestamp()
              os.makedirs(lowercase_				)
          UpperCAmelCase          =       save_path

          UpperCAmelCase          =       self.vqgan.decode(self.latent				)[0]
          if show_intermediate:
              print('Original Image'				)
              show_pil(custom_to_pil(lowercase_				)				)

          UpperCAmelCase          =       loop_post_process(lowercase_				)
          for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase_ ,							lowercase_ ,							lowercase_				)				):
              if show_intermediate:
                  show_pil(lowercase_				)
              if save_intermediate:
                  transformed_img.save(os.path.join(self.save_path ,							F"iter_{iter:03d}.png"				)				)
              if self.log:
                  wandb.log({'Image': wandb.Image(lowercase_				)}				)
          if show_final:
              show_pil(lowercase_				)
          if save_final:
              transformed_img.save(os.path.join(self.save_path ,							F"iter_{iter:03d}_final.png"				)				)



