


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,
)


_lowercase       :      Optional[Any]	=	{
    "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__:     Dict						) ->   Optional[int]:





	"""simple docstring"""







	A								=				{}
	state_dict.pop("""pixel_mean"""							,					UpperCamelCase__						)
	state_dict.pop("""pixel_std"""							,					UpperCamelCase__						)

	A								=				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:
				A								=				key.replace(UpperCamelCase__							,					UpperCamelCase__						)

		if re.match(UpperCamelCase__							,					UpperCamelCase__						):
			A								=				int(re.match(UpperCamelCase__							,					UpperCamelCase__						).group(2						)						)
			if layer_nb == 0:
				A								=				key.replace("""layers.0"""							,					"""proj_in"""						)
			elif layer_nb == 1:
				A								=				key.replace("""layers.1"""							,					"""layers.0"""						)
			elif layer_nb == 2:
				A								=				key.replace("""layers.2"""							,					"""proj_out"""						)

		A								=				value

	A								=				model_state_dict[
	    """prompt_encoder.shared_embedding.positional_embedding"""
	]

	return model_state_dict





def   _lowerCAmelCase				(   UpperCamelCase__:     Any							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Dict="ybelkada/segment-anything"						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				hf_hub_download(UpperCamelCase__							,					f'checkpoints/{model_name}.pth'						)

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

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

		A								=				SamConfig(
		    vision_config=UpperCamelCase__							,					)

	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)
	A								=				replace_keys(UpperCamelCase__						)

	A								=				SamImageProcessor()

	A								=				SamProcessor(image_processor=UpperCamelCase__						)
	A								=				SamModel(UpperCamelCase__						)

	hf_model.load_state_dict(UpperCamelCase__						)
	A								=				hf_model.to("""cuda"""						)

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

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

	A								=				processor(images=np.array(UpperCamelCase__						)							,					return_tensors="""pt"""						).to("""cuda"""						)

	with torch.no_grad():
		A								=				hf_model(**UpperCamelCase__						)
	A								=				output.iou_scores.squeeze()

	if model_name == "sam_vit_h_4b8939":
		assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

		A								=				processor(
		    images=np.array(UpperCamelCase__						)							,					input_points=UpperCamelCase__							,					input_labels=UpperCamelCase__							,					return_tensors="""pt"""						).to("""cuda"""						)

		with torch.no_grad():
			A								=				hf_model(**UpperCamelCase__						)
		A								=				output.iou_scores.squeeze()

		assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

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

		A								=				processor(images=np.array(UpperCamelCase__						)							,					input_boxes=UpperCamelCase__							,					return_tensors="""pt"""						).to("""cuda"""						)

		with torch.no_grad():
			A								=				hf_model(**UpperCamelCase__						)
		A								=				output.iou_scores.squeeze()

		assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

		A								=				processor(
		    images=np.array(UpperCamelCase__						)							,					input_points=UpperCamelCase__							,					input_labels=UpperCamelCase__							,					return_tensors="""pt"""						).to("""cuda"""						)

		with torch.no_grad():
			A								=				hf_model(**UpperCamelCase__						)
		A								=				output.iou_scores.squeeze()

		assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
				_lowercase       :      Tuple	=	argparse.ArgumentParser()
				_lowercase       :      Dict	=	["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",
				)

				_lowercase       :      List[Any]	=	parser.parse_args()

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







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



