






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 :      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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]       ):
	snake_case__			:				Union[str, Any]       =  {}
	state_dict.pop("pixel_mean"					,  snake_case_       )
	state_dict.pop("pixel_std"					,  snake_case_       )

	snake_case__			:				Optional[int]       =  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:
				snake_case__			:				Any       =  key.replace(snake_case_					,  snake_case_       )

		if re.match(snake_case_					,  snake_case_       ):
			snake_case__			:				Dict       =  int(re.match(snake_case_					,  snake_case_       ).group(2       )       )
			if layer_nb == 0:
				snake_case__			:				Optional[Any]       =  key.replace("layers.0"					,  "proj_in"       )
			elif layer_nb == 1:
				snake_case__			:				Any       =  key.replace("layers.1"					,  "layers.0"       )
			elif layer_nb == 2:
				snake_case__			:				List[str]       =  key.replace("layers.2"					,  "proj_out"       )

		snake_case__			:				str       =  value

	snake_case__			:				Any       =  model_state_dict[
	    "prompt_encoder.shared_embedding.positional_embedding"
	]

	return model_state_dict







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							List[str]					,  snake_case_ :							Dict					,  snake_case_ :							Tuple="ybelkada/segment-anything"       ):
	snake_case__			:				List[Any]       =  hf_hub_download(snake_case_					,  F'''checkpoints/{model_name}.pth'''       )

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

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

		snake_case__			:				List[str]       =  SamConfig(
		    vision_config=snake_case_					,  )

	snake_case__			:				List[str]       =  torch.load(snake_case_					,  map_location="cpu"       )
	snake_case__			:				Optional[Any]       =  replace_keys(snake_case_       )

	snake_case__			:				Any       =  SamImageProcessor()

	snake_case__			:				Dict       =  SamProcessor(image_processor=snake_case_       )
	snake_case__			:				Optional[Any]       =  SamModel(snake_case_       )

	hf_model.load_state_dict(snake_case_       )
	snake_case__			:				Union[str, Any]       =  hf_model.to("cuda"       )

	snake_case__			:				Any       =  "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
	snake_case__			:				Optional[Any]       =  Image.open(requests.get(snake_case_					,  stream=snake_case_       ).raw       ).convert("RGB"       )

	snake_case__			:				List[Any]       =  [[[400, 650]]]
	snake_case__			:				str       =  [[1]]

	snake_case__			:				str       =  processor(images=np.array(snake_case_       )					,  return_tensors="pt"       ).to("cuda"       )

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

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

		snake_case__			:				Optional[int]       =  processor(
		    images=np.array(snake_case_       )					,  input_points=snake_case_					,  input_labels=snake_case_					,  return_tensors="pt"       ).to("cuda"       )

		with torch.no_grad():
			snake_case__			:				Dict       =  hf_model(**snake_case_       )
		snake_case__			:				List[str]       =  output.iou_scores.squeeze()

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

		snake_case__			:				Optional[Any]       =  ((75, 275, 1725, 850),)

		snake_case__			:				str       =  processor(images=np.array(snake_case_       )					,  input_boxes=snake_case_					,  return_tensors="pt"       ).to("cuda"       )

		with torch.no_grad():
			snake_case__			:				str       =  hf_model(**snake_case_       )
		snake_case__			:				Union[str, Any]       =  output.iou_scores.squeeze()

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

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

		snake_case__			:				List[str]       =  processor(
		    images=np.array(snake_case_       )					,  input_points=snake_case_					,  input_labels=snake_case_					,  return_tensors="pt"       ).to("cuda"       )

		with torch.no_grad():
			snake_case__			:				Optional[Any]       =  hf_model(**snake_case_       )
		snake_case__			:				str       =  output.iou_scores.squeeze()

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


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				argparse.ArgumentParser()
	__lowerCamelCase :      str										=				["""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 :      Dict										=				parser.parse_args()

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











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



