






"""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,
)


A__   :					Dict							=   {
    '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		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     {}
  state_dict.pop('''pixel_mean''' ,				_UpperCamelCase						)
  state_dict.pop('''pixel_std''' ,				_UpperCamelCase						)

  _lowercase:			Any       =     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:			str       =     key.replace(_UpperCamelCase ,				_UpperCamelCase						)

    if re.match(_UpperCamelCase ,				_UpperCamelCase						):
      _lowercase:			List[Any]       =     int(re.match(_UpperCamelCase ,				_UpperCamelCase						).group(2						)						)
      if layer_nb == 0:
        _lowercase:			Union[str, Any]       =     key.replace('''layers.0''' ,				'''proj_in'''						)
      elif layer_nb == 1:
        _lowercase:			Tuple       =     key.replace('''layers.1''' ,				'''layers.0'''						)
      elif layer_nb == 2:
        _lowercase:			Union[str, Any]       =     key.replace('''layers.2''' ,				'''proj_out'''						)

    _lowercase:			Any       =     value

  _lowercase:			Optional[int]       =     model_state_dict[
      '''prompt_encoder.shared_embedding.positional_embedding'''
  ]

  return model_state_dict




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase="ybelkada/segment-anything"						):






  """simple docstring"""



  _lowercase:			str       =     hf_hub_download(_UpperCamelCase ,				f'''checkpoints/{model_name}.pth'''						)

  if "sam_vit_b" in model_name:
    _lowercase:			Tuple       =     SamConfig()
  elif "sam_vit_l" in model_name:
    _lowercase:			Optional[Any]       =     SamVisionConfig(
        hidden_size=1_024 ,				num_hidden_layers=24 ,				num_attention_heads=16 ,				global_attn_indexes=[5, 11, 17, 23] ,				)

    _lowercase:			Optional[Any]       =     SamConfig(
        vision_config=_UpperCamelCase ,				)
  elif "sam_vit_h" in model_name:
    _lowercase:			Dict       =     SamVisionConfig(
        hidden_size=1_280 ,				num_hidden_layers=32 ,				num_attention_heads=16 ,				global_attn_indexes=[7, 15, 23, 31] ,				)

    _lowercase:			int       =     SamConfig(
        vision_config=_UpperCamelCase ,				)

  _lowercase:			List[str]       =     torch.load(_UpperCamelCase ,				map_location='''cpu'''						)
  _lowercase:			str       =     replace_keys(_UpperCamelCase						)

  _lowercase:			List[Any]       =     SamImageProcessor()

  _lowercase:			Dict       =     SamProcessor(image_processor=_UpperCamelCase						)
  _lowercase:			str       =     SamModel(_UpperCamelCase						)

  hf_model.load_state_dict(_UpperCamelCase						)
  _lowercase:			int       =     hf_model.to('''cuda'''						)

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

  _lowercase:			Union[str, Any]       =     [[[400, 650]]]
  _lowercase:			int       =     [[1]]

  _lowercase:			str       =     processor(images=np.array(_UpperCamelCase						) ,				return_tensors='''pt'''						).to('''cuda'''						)

  with torch.no_grad():
    _lowercase:			str       =     hf_model(**_UpperCamelCase						)
  _lowercase:			Tuple       =     output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.579_890_251_159_668

    _lowercase:			Optional[int]       =     processor(
        images=np.array(_UpperCamelCase						) ,				input_points=_UpperCamelCase ,				input_labels=_UpperCamelCase ,				return_tensors='''pt'''						).to('''cuda'''						)

    with torch.no_grad():
      _lowercase:			str       =     hf_model(**_UpperCamelCase						)
    _lowercase:			Optional[int]       =     output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_712_603_092_193_604

    _lowercase:			List[Any]       =     ((75, 275, 1_725, 850),)

    _lowercase:			Union[str, Any]       =     processor(images=np.array(_UpperCamelCase						) ,				input_boxes=_UpperCamelCase ,				return_tensors='''pt'''						).to('''cuda'''						)

    with torch.no_grad():
      _lowercase:			Optional[Any]       =     hf_model(**_UpperCamelCase						)
    _lowercase:			List[Any]       =     output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_686_015_605_926_514

    # Test with 2 points and 1 image.
    _lowercase:			str       =     [[[400, 650], [800, 650]]]
    _lowercase:			Optional[Any]       =     [[1, 1]]

    _lowercase:			List[Any]       =     processor(
        images=np.array(_UpperCamelCase						) ,				input_points=_UpperCamelCase ,				input_labels=_UpperCamelCase ,				return_tensors='''pt'''						).to('''cuda'''						)

    with torch.no_grad():
      _lowercase:			Optional[int]       =     hf_model(**_UpperCamelCase						)
    _lowercase:			int       =     output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
  A__   :					Union[str, Any]							=   argparse.ArgumentParser()
  A__   :					Any							=   ['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',
  )

  A__   :					str							=   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 torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
