
"""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,
)


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    __lowerCAmelCase    (_UpperCamelCase		):
		__lowerCAmelCase      :						Union[str, Any]						      =				{}
		state_dict.pop('pixel_mean'	,  __lowerCamelCase		)
		state_dict.pop('pixel_std'	,  __lowerCamelCase		)

		__lowerCAmelCase      :						Union[str, 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:
								__lowerCAmelCase      :						List[str]						      =				key.replace(__lowerCamelCase	,  __lowerCamelCase		)

				if re.match(__lowerCamelCase	,  __lowerCamelCase		):
						__lowerCAmelCase      :						Dict						      =				int(re.match(__lowerCamelCase	,  __lowerCamelCase		).group(2		)		)
						if layer_nb == 0:
								__lowerCAmelCase      :						Tuple						      =				key.replace('layers.0'	,  'proj_in'		)
						elif layer_nb == 1:
								__lowerCAmelCase      :						Optional[Any]						      =				key.replace('layers.1'	,  'layers.0'		)
						elif layer_nb == 2:
								__lowerCAmelCase      :						str						      =				key.replace('layers.2'	,  'proj_out'		)

				__lowerCAmelCase      :						Dict						      =				value

		__lowerCAmelCase      :						List[str]						      =				model_state_dict[
		    "prompt_encoder.shared_embedding.positional_embedding"
		]

		return model_state_dict
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase="ybelkada/segment-anything"		):
		__lowerCAmelCase      :						List[str]						      =				hf_hub_download(__lowerCamelCase	,  F"checkpoints/{model_name}.pth"		)

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

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

				__lowerCAmelCase      :						Dict						      =				SamConfig(
				    vision_config=__lowerCamelCase	,  )

		__lowerCAmelCase      :						Optional[int]						      =				torch.load(__lowerCamelCase	,  map_location='cpu'		)
		__lowerCAmelCase      :						Union[str, Any]						      =				replace_keys(__lowerCamelCase		)

		__lowerCAmelCase      :						Optional[int]						      =				SamImageProcessor()

		__lowerCAmelCase      :						List[Any]						      =				SamProcessor(image_processor=__lowerCamelCase		)
		__lowerCAmelCase      :						List[Any]						      =				SamModel(__lowerCamelCase		)

		hf_model.load_state_dict(__lowerCamelCase		)
		__lowerCAmelCase      :						Any						      =				hf_model.to('cuda'		)

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

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

		__lowerCAmelCase      :						Optional[int]						      =				processor(images=np.array(__lowerCamelCase		)	,  return_tensors='pt'		).to('cuda'		)

		with torch.no_grad():
				__lowerCAmelCase      :						Dict						      =				hf_model(**__lowerCamelCase		)
		__lowerCAmelCase      :						Tuple						      =				output.iou_scores.squeeze()

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

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

				with torch.no_grad():
						__lowerCAmelCase      :						Union[str, Any]						      =				hf_model(**__lowerCamelCase		)
				__lowerCAmelCase      :						Dict						      =				output.iou_scores.squeeze()

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

				__lowerCAmelCase      :						Tuple						      =				((75, 275, 1725, 850),)

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

				with torch.no_grad():
						__lowerCAmelCase      :						Dict						      =				hf_model(**__lowerCamelCase		)
				__lowerCAmelCase      :						Union[str, Any]						      =				output.iou_scores.squeeze()

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

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

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

				with torch.no_grad():
						__lowerCAmelCase      :						Optional[int]						      =				hf_model(**__lowerCamelCase		)
				__lowerCAmelCase      :						Optional[int]						      =				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"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)