"""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__						:						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_		(      lowerCamelCase ):
				UpperCAmelCase__    =							{}
				state_dict.pop('pixel_mean'      ,    lowerCamelCase )
				state_dict.pop('pixel_std'      ,    lowerCamelCase )

				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(lowerCamelCase      ,    lowerCamelCase )

								if re.match(lowerCamelCase      ,    lowerCamelCase ):
												UpperCAmelCase__    =							int(re.match(lowerCamelCase      ,    lowerCamelCase ).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_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase="ybelkada/segment-anything" ):
				UpperCAmelCase__    =							hf_hub_download(lowerCamelCase      ,    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=1_0_2_4      ,    num_hidden_layers=2_4      ,    num_attention_heads=1_6      ,    global_attn_indexes=[5, 1_1, 1_7, 2_3]      ,    )

								UpperCAmelCase__    =							SamConfig(
								    vision_config=lowerCamelCase      ,    )
				elif "sam_vit_h" in model_name:
								UpperCAmelCase__    =							SamVisionConfig(
								    hidden_size=1_2_8_0      ,    num_hidden_layers=3_2      ,    num_attention_heads=1_6      ,    global_attn_indexes=[7, 1_5, 2_3, 3_1]      ,    )

								UpperCAmelCase__    =							SamConfig(
								    vision_config=lowerCamelCase      ,    )

				UpperCAmelCase__    =							torch.load(lowerCamelCase      ,    map_location='cpu' )
				UpperCAmelCase__    =							replace_keys(lowerCamelCase )

				UpperCAmelCase__    =							SamImageProcessor()

				UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase )
				UpperCAmelCase__    =							SamModel(lowerCamelCase )

				hf_model.load_state_dict(lowerCamelCase )
				UpperCAmelCase__    =							hf_model.to('cuda' )

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

				UpperCAmelCase__    =							[[[4_0_0, 6_5_0]]]
				UpperCAmelCase__    =							[[1]]

				UpperCAmelCase__    =							processor(images=np.array(lowerCamelCase )      ,    return_tensors='pt' ).to('cuda' )

				with torch.no_grad():
								UpperCAmelCase__    =							hf_model(**lowerCamelCase )
				UpperCAmelCase__    =							output.iou_scores.squeeze()

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

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

								with torch.no_grad():
												UpperCAmelCase__    =							hf_model(**lowerCamelCase )
								UpperCAmelCase__    =							output.iou_scores.squeeze()

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

								UpperCAmelCase__    =							((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

								UpperCAmelCase__    =							processor(images=np.array(lowerCamelCase )      ,    input_boxes=lowerCamelCase      ,    return_tensors='pt' ).to('cuda' )

								with torch.no_grad():
												UpperCAmelCase__    =							hf_model(**lowerCamelCase )
								UpperCAmelCase__    =							output.iou_scores.squeeze()

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

								# Test with 2 points and 1 image.
								UpperCAmelCase__    =							[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
								UpperCAmelCase__    =							[[1, 1]]

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

								with torch.no_grad():
												UpperCAmelCase__    =							hf_model(**lowerCamelCase )
								UpperCAmelCase__    =							output.iou_scores.squeeze()

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


if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			argparse.ArgumentParser()
						lowerCAmelCase__						:						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',
						)

						lowerCAmelCase__						:						List[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"""






from __future__ import annotations

from math import ceil, floor, sqrt



def 						a_		(      lowerCamelCase = 2_0_0_0_0_0_0 ):
				UpperCAmelCase__    =							[0]
				UpperCAmelCase__    =							42

				for idx in range(1      ,    ceil(sqrt(target * 2 ) * 1.1 ) ):
								triangle_numbers.append(triangle_numbers[-1] + idx )

				# we want this to be as close as possible to target
				UpperCAmelCase__    =							0
				# the area corresponding to the grid that gives the product closest to target
				UpperCAmelCase__    =							0
				# an estimate of b, using the quadratic formula
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_floor
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_ceil
				UpperCAmelCase__    =							42

				for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,    1 ):
								UpperCAmelCase__    =							(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
								UpperCAmelCase__    =							floor(lowerCamelCase )
								UpperCAmelCase__    =							ceil(lowerCamelCase )
								UpperCAmelCase__    =							triangle_numbers[b_floor]
								UpperCAmelCase__    =							triangle_numbers[b_ceil]

								if abs(target - triangle_b_first_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_first_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_floor

								if abs(target - triangle_b_second_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_second_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_ceil

				return area


if __name__ == "__main__":
						print(F"""{solution() = }""")



