





'''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,
)


_a   :					Tuple           =       {
    """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		(		lowercase      )   ->   int:
			__lowerCAmelCase   		=							{}
			state_dict.pop("""pixel_mean"""   ,      _snake_case      )
			state_dict.pop("""pixel_std"""   ,      _snake_case      )

			__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(_snake_case   ,      _snake_case      )

						if re.match(_snake_case   ,      _snake_case      ):
									__lowerCAmelCase   		=							int(re.match(_snake_case   ,      _snake_case      ).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   _lowerCAmelCase		(		lowercase   ,      lowercase   ,      lowercase   ,      lowercase="ybelkada/segment-anything"      )   ->   Dict:
			__lowerCAmelCase   		=							hf_hub_download(_snake_case   ,      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=1024   ,      num_hidden_layers=24   ,      num_attention_heads=16   ,      global_attn_indexes=[5, 11, 17, 23]   ,      )

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

						__lowerCAmelCase   		=							SamConfig(
						    vision_config=_snake_case   ,      )

			__lowerCAmelCase   		=							torch.load(_snake_case   ,      map_location="""cpu"""      )
			__lowerCAmelCase   		=							replace_keys(_snake_case      )

			__lowerCAmelCase   		=							SamImageProcessor()

			__lowerCAmelCase   		=							SamProcessor(image_processor=_snake_case      )
			__lowerCAmelCase   		=							SamModel(_snake_case      )

			hf_model.load_state_dict(_snake_case      )
			__lowerCAmelCase   		=							hf_model.to("""cuda"""      )

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

			__lowerCAmelCase   		=							[[[400, 650]]]
			__lowerCAmelCase   		=							[[1]]

			__lowerCAmelCase   		=							processor(images=np.array(_snake_case      )   ,      return_tensors="""pt"""      ).to("""cuda"""      )

			with torch.no_grad():
						__lowerCAmelCase   		=							hf_model(**_snake_case      )
			__lowerCAmelCase   		=							output.iou_scores.squeeze()

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

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

						with torch.no_grad():
									__lowerCAmelCase   		=							hf_model(**_snake_case      )
						__lowerCAmelCase   		=							output.iou_scores.squeeze()

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

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

						__lowerCAmelCase   		=							processor(images=np.array(_snake_case      )   ,      input_boxes=_snake_case   ,      return_tensors="""pt"""      ).to("""cuda"""      )

						with torch.no_grad():
									__lowerCAmelCase   		=							hf_model(**_snake_case      )
						__lowerCAmelCase   		=							output.iou_scores.squeeze()

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

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

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

						with torch.no_grad():
									__lowerCAmelCase   		=							hf_model(**_snake_case      )
						__lowerCAmelCase   		=							output.iou_scores.squeeze()

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


if __name__ == "__main__":
	_a   :					str           =       argparse.ArgumentParser()
	_a   :					Optional[Any]           =       ["""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   :					Optional[Any]           =       parser.parse_args()

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






'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    if len(_snake_case							) == 1:
        return True
    lowercase__									=    series[1] - series[0]
    for index in range(len(_snake_case							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True




def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    lowercase__									=    0
    for val in series:
        answer += val
    return answer / len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




