
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__							:							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 		A_       (							snake_case							:      List[str]       )					->			List[str]:



						'''simple docstring'''

						__UpperCamelCase        =	{}
						state_dict.pop('''pixel_mean'''       ,					__UpperCAmelCase       )
						state_dict.pop('''pixel_std'''       ,					__UpperCAmelCase       )

						__UpperCamelCase        =	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:
																								__UpperCamelCase        =	key.replace(__UpperCAmelCase       ,					__UpperCAmelCase       )

												if re.match(__UpperCAmelCase       ,					__UpperCAmelCase       ):
																		__UpperCamelCase        =	int(re.match(__UpperCAmelCase       ,					__UpperCAmelCase       ).group(2       )       )
																		if layer_nb == 0:
																								__UpperCamelCase        =	key.replace('''layers.0'''       ,					'''proj_in'''       )
																		elif layer_nb == 1:
																								__UpperCamelCase        =	key.replace('''layers.1'''       ,					'''layers.0'''       )
																		elif layer_nb == 2:
																								__UpperCamelCase        =	key.replace('''layers.2'''       ,					'''proj_out'''       )

												__UpperCamelCase        =	value

						__UpperCamelCase        =	model_state_dict[
						    '''prompt_encoder.shared_embedding.positional_embedding'''
						]

						return model_state_dict




def 		A_       (							snake_case							:      Tuple       ,					snake_case							:      Any       ,					snake_case							:      Any       ,					snake_case							:      Optional[Any]="ybelkada/segment-anything"       )					->			Dict:



						'''simple docstring'''

						__UpperCamelCase        =	hf_hub_download(__UpperCAmelCase       ,					f"checkpoints/{model_name}.pth"       )

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

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

												__UpperCamelCase        =	SamConfig(
												    vision_config=__UpperCAmelCase       ,					)

						__UpperCamelCase        =	torch.load(__UpperCAmelCase       ,					map_location='''cpu'''       )
						__UpperCamelCase        =	replace_keys(__UpperCAmelCase       )

						__UpperCamelCase        =	SamImageProcessor()

						__UpperCamelCase        =	SamProcessor(image_processor=__UpperCAmelCase       )
						__UpperCamelCase        =	SamModel(__UpperCAmelCase       )

						hf_model.load_state_dict(__UpperCAmelCase       )
						__UpperCamelCase        =	hf_model.to('''cuda'''       )

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

						__UpperCamelCase        =	[[[400, 650]]]
						__UpperCamelCase        =	[[1]]

						__UpperCamelCase        =	processor(images=np.array(__UpperCAmelCase       )       ,					return_tensors='''pt'''       ).to('''cuda'''       )

						with torch.no_grad():
												__UpperCamelCase        =	hf_model(**__UpperCAmelCase       )
						__UpperCamelCase        =	output.iou_scores.squeeze()

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

												__UpperCamelCase        =	processor(
												    images=np.array(__UpperCAmelCase       )       ,					input_points=__UpperCAmelCase       ,					input_labels=__UpperCAmelCase       ,					return_tensors='''pt'''       ).to('''cuda'''       )

												with torch.no_grad():
																		__UpperCamelCase        =	hf_model(**__UpperCAmelCase       )
												__UpperCamelCase        =	output.iou_scores.squeeze()

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

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

												__UpperCamelCase        =	processor(images=np.array(__UpperCAmelCase       )       ,					input_boxes=__UpperCAmelCase       ,					return_tensors='''pt'''       ).to('''cuda'''       )

												with torch.no_grad():
																		__UpperCamelCase        =	hf_model(**__UpperCAmelCase       )
												__UpperCamelCase        =	output.iou_scores.squeeze()

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

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

												__UpperCamelCase        =	processor(
												    images=np.array(__UpperCAmelCase       )       ,					input_points=__UpperCAmelCase       ,					input_labels=__UpperCAmelCase       ,					return_tensors='''pt'''       ).to('''cuda'''       )

												with torch.no_grad():
																		__UpperCamelCase        =	hf_model(**__UpperCAmelCase       )
												__UpperCamelCase        =	output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
				lowercase__							:							Union[str, Any]													=	argparse.ArgumentParser()
				lowercase__							:							Tuple													=	['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__							:							Tuple													=	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 __future__ import annotations

import math


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''	)

					if len(__UpperCAmelCase	) == 0:
										raise ValueError('''Scores cannot be empty'''	)

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )

					return min(
					    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_									=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
					snake_case_									=				math.log(len(__UpperCAmelCase	),      2	)
					print('''Optimal value : ''',      end=''''''	)
					print(minimax(0,      0,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

