
import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		super().__init__()
		lowercase	    =       value_function
		lowercase	    =       unet
		lowercase	    =       scheduler
		lowercase	    =       env
		lowercase	    =       env.get_dataset()
		lowercase	    =       {}
		for key in self.data.keys():
			try:
				lowercase	    =       self.data[key].mean()
			except:  # noqa: E722
				pass
		lowercase	    =       {}
		for key in self.data.keys():
			try:
				lowercase	    =       self.data[key].std()
			except:  # noqa: E722
				pass
		lowercase	    =       env.observation_space.shape[0]
		lowercase	    =       env.action_space.shape[0]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		return (x_in - self.means[key]) / self.stds[key]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		return x_in * self.stds[key] + self.means[key]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if type(snake_case			) is dict:
			return {k: self.to_torch(snake_case			) for k, v in x_in.items()}
		elif torch.is_tensor(snake_case			):
			return x_in.to(self.unet.device			)
		return torch.tensor(snake_case						,       device=self.unet.device			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		for key, val in cond.items():
			lowercase	    =       val.clone()
		return x_in





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       x.shape[0]
		lowercase	    =       None
		for i in tqdm.tqdm(self.scheduler.timesteps			):
			# create batch of timesteps to pass into model
			lowercase	    =       torch.full((batch_size,)						,       snake_case						,       device=self.unet.device						,       dtype=torch.long			)
			for _ in range(snake_case			):
				with torch.enable_grad():
					x.requires_grad_()

					# permute to match dimension for pre-trained models
					lowercase	    =       self.value_function(x.permute(0						,       2						,       1			)						,       snake_case			).sample
					lowercase	    =       torch.autograd.grad([y.sum()]						,       [x]			)[0]

					lowercase	    =       self.scheduler._get_variance(snake_case			)
					lowercase	    =       torch.exp(0.5 * posterior_variance			)
					lowercase	    =       model_std * grad

				lowercase	    =       0
				lowercase	    =       x.detach()
				lowercase	    =       x + scale * grad
				lowercase	    =       self.reset_xa(snake_case						,       snake_case						,       self.action_dim			)

			lowercase	    =       self.unet(x.permute(0						,       2						,       1			)						,       snake_case			).sample.permute(0						,       2						,       1			)

			# TODO: verify deprecation of this kwarg
			lowercase	    =       self.scheduler.step(snake_case						,       snake_case						,       snake_case						,       predict_epsilon=snake_case			)['prev_sample']

			# apply conditions to the trajectory (set the initial state)
			lowercase	    =       self.reset_xa(snake_case						,       snake_case						,       self.action_dim			)
			lowercase	    =       self.to_torch(snake_case			)
		return x, y





	def __call__(			self						,       snake_case						,       snake_case=64						,       snake_case=32						,       snake_case=2						,       snake_case=0.1			):
		# normalize the observations and create  batch dimension
		lowercase	    =       self.normalize(snake_case						,       'observations'			)
		lowercase	    =       obs[None].repeat(snake_case						,       axis=0			)

		lowercase	    =       {0: self.to_torch(snake_case			)}
		lowercase	    =       (batch_size, planning_horizon, self.state_dim + self.action_dim)

		# generate initial noise and apply our conditions (to make the trajectories start at current state)
		lowercase	    =       randn_tensor(snake_case						,       device=self.unet.device			)
		lowercase	    =       self.reset_xa(snake_case						,       snake_case						,       self.action_dim			)
		lowercase	    =       self.to_torch(snake_case			)

		# run the diffusion process
		lowercase    ,   lowercase	    =       self.run_diffusion(snake_case						,       snake_case						,       snake_case						,       snake_case			)

		# sort output trajectories by value
		lowercase	    =       y.argsort(0						,       descending=snake_case			).squeeze()
		lowercase	    =       x[sorted_idx]
		lowercase	    =       sorted_values[:, :, : self.action_dim]
		lowercase	    =       actions.detach().cpu().numpy()
		lowercase	    =       self.de_normalize(snake_case						,       key='actions'			)

		# select the action with the highest value
		if y is not None:
			lowercase	    =       0
		else:
			# if we didn't run value guiding, select a random action
			lowercase	    =       np.random.randint(0						,       snake_case			)

		lowercase	    =       denorm_actions[selected_index, 0]
		return denorm_actions






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_								=       {
    """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 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     {}
   state_dict.pop('''pixel_mean'''			,_UpperCamelCase				)
   state_dict.pop('''pixel_std'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     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:
            __lowerCamelCase    				=     key.replace(_UpperCamelCase			,_UpperCamelCase				)

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

      __lowerCamelCase    				=     value

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

   return model_state_dict





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]="ybelkada/segment-anything"				):
   __lowerCamelCase    				=     hf_hub_download(_UpperCamelCase			,F"""checkpoints/{model_name}.pth"""				)

   if "sam_vit_b" in model_name:
      __lowerCamelCase    				=     SamConfig()
   elif "sam_vit_l" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=10_24			,num_hidden_layers=24			,num_attention_heads=16			,global_attn_indexes=[5, 11, 17, 23]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)
   elif "sam_vit_h" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=12_80			,num_hidden_layers=32			,num_attention_heads=16			,global_attn_indexes=[7, 15, 23, 31]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)

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

   __lowerCamelCase    				=     SamImageProcessor()

   __lowerCamelCase    				=     SamProcessor(image_processor=_UpperCamelCase				)
   __lowerCamelCase    				=     SamModel(_UpperCamelCase				)

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

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

   __lowerCamelCase    				=     [[[4_00, 6_50]]]
   __lowerCamelCase    				=     [[1]]

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

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

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

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

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

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

      __lowerCamelCase    				=     ((75, 2_75, 17_25, 8_50),)

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

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

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

      # Test with 2 points and 1 image.
      __lowerCamelCase    				=     [[[4_00, 6_50], [8_00, 6_50]]]
      __lowerCamelCase    				=     [[1, 1]]

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

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

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


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  a_								=       ["""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_								=       parser.parse_args()

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






