

'''simple docstring'''





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,
)


UpperCamelCase_							=					{
    '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 							_lowerCAmelCase (      __magic_name__	:    Tuple	)							->     List[Any]:
      lowercase   :       Union[str, Any]								={}
      state_dict.pop('''pixel_mean'''      ,   A_	)
      state_dict.pop('''pixel_std'''      ,   A_	)

      lowercase   :       List[str]								=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:
                        lowercase   :       int								=key.replace(A_      ,   A_	)

            if re.match(A_      ,   A_	):
                  lowercase   :       str								=int(re.match(A_      ,   A_	).group(2	)	)
                  if layer_nb == 0:
                        lowercase   :       Optional[int]								=key.replace('''layers.0'''      ,   '''proj_in'''	)
                  elif layer_nb == 1:
                        lowercase   :       Optional[int]								=key.replace('''layers.1'''      ,   '''layers.0'''	)
                  elif layer_nb == 2:
                        lowercase   :       List[str]								=key.replace('''layers.2'''      ,   '''proj_out'''	)

            lowercase   :       Dict								=value

      lowercase   :       Dict								=model_state_dict[
          '''prompt_encoder.shared_embedding.positional_embedding'''
      ]

      return model_state_dict






def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    Tuple="ybelkada/segment-anything"	)							->     Optional[Any]:
      lowercase   :       List[Any]								=hf_hub_download(A_      ,   f'''checkpoints/{model_name}.pth'''	)

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

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

            lowercase   :       Optional[Any]								=SamConfig(
                vision_config=A_      ,   )

      lowercase   :       List[str]								=torch.load(A_      ,   map_location='''cpu'''	)
      lowercase   :       Optional[int]								=replace_keys(A_	)

      lowercase   :       List[str]								=SamImageProcessor()

      lowercase   :       Dict								=SamProcessor(image_processor=A_	)
      lowercase   :       int								=SamModel(A_	)

      hf_model.load_state_dict(A_	)
      lowercase   :       Optional[Any]								=hf_model.to('''cuda'''	)

      lowercase   :       Optional[int]								='''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
      lowercase   :       Dict								=Image.open(requests.get(A_      ,   stream=A_	).raw	).convert('''RGB'''	)

      lowercase   :       Tuple								=[[[400, 650]]]
      lowercase   :       List[Any]								=[[1]]

      lowercase   :       Dict								=processor(images=np.array(A_	)      ,   return_tensors='''pt'''	).to('''cuda'''	)

      with torch.no_grad():
            lowercase   :       List[Any]								=hf_model(**A_	)
      lowercase   :       List[Any]								=output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

            lowercase   :       str								=processor(
                images=np.array(A_	)      ,   input_points=A_      ,   input_labels=A_      ,   return_tensors='''pt'''	).to('''cuda'''	)

            with torch.no_grad():
                  lowercase   :       int								=hf_model(**A_	)
            lowercase   :       Dict								=output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

            lowercase   :       str								=((75, 275, 1725, 850),)

            lowercase   :       List[Any]								=processor(images=np.array(A_	)      ,   input_boxes=A_      ,   return_tensors='''pt'''	).to('''cuda'''	)

            with torch.no_grad():
                  lowercase   :       Tuple								=hf_model(**A_	)
            lowercase   :       Optional[Any]								=output.iou_scores.squeeze()

            assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

            # Test with 2 points and 1 image.
            lowercase   :       Union[str, Any]								=[[[400, 650], [800, 650]]]
            lowercase   :       int								=[[1, 1]]

            lowercase   :       Tuple								=processor(
                images=np.array(A_	)      ,   input_points=A_      ,   input_labels=A_      ,   return_tensors='''pt'''	).to('''cuda'''	)

            with torch.no_grad():
                  lowercase   :       Tuple								=hf_model(**A_	)
            lowercase   :       List[Any]								=output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
       UpperCamelCase_							=					argparse.ArgumentParser()
       UpperCamelCase_							=					['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""",
       )

       UpperCamelCase_							=					parser.parse_args()

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








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





