





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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   {}
		state_dict.pop("""pixel_mean"""       , UpperCamelCase__     )
		state_dict.pop("""pixel_std"""       , UpperCamelCase__     )

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

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

				SCREAMING_SNAKE_CASE__								=   value

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

		return model_state_dict






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				int       , UpperCamelCase__:				Tuple="ybelkada/segment-anything"     ):
		SCREAMING_SNAKE_CASE__								=   hf_hub_download(UpperCamelCase__       , f'''checkpoints/{model_name}.pth'''     )

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

				SCREAMING_SNAKE_CASE__								=   SamConfig(
				    vision_config=UpperCamelCase__       , )
		elif "sam_vit_h" in model_name:
				SCREAMING_SNAKE_CASE__								=   SamVisionConfig(
				    hidden_size=1_280       , num_hidden_layers=32       , num_attention_heads=16       , global_attn_indexes=[7, 15, 23, 31]       , )

				SCREAMING_SNAKE_CASE__								=   SamConfig(
				    vision_config=UpperCamelCase__       , )

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

		SCREAMING_SNAKE_CASE__								=   SamImageProcessor()

		SCREAMING_SNAKE_CASE__								=   SamProcessor(image_processor=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   SamModel(UpperCamelCase__     )

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

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

		SCREAMING_SNAKE_CASE__								=   [[[400, 650]]]
		SCREAMING_SNAKE_CASE__								=   [[1]]

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

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

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

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

				assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

				SCREAMING_SNAKE_CASE__								=   ((75, 275, 1_725, 850),)

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

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

				assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

				# Test with 2 points and 1 image.
				SCREAMING_SNAKE_CASE__								=   [[[400, 650], [800, 650]]]
				SCREAMING_SNAKE_CASE__								=   [[1, 1]]

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

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

				assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


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"""



from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState







def     __lowerCAmelCase		(		lowercase    :    bool = True		,				*lowercase    :    str		,				**lowercase    :    List[Any]					)      ->		List[Any]:


    """simple docstring"""





    if not is_tqdm_available():
        raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."					)
    snake_case       : Dict    =       False
    if main_process_only:
        snake_case       : int    =       PartialState().local_process_index == 0
    return _tqdm(*lowercase		,				**lowercase		,				disable=lowercase					)





