




"""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   :  List[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 						snake_case__       (					_lowerCamelCase				)			->str:






		"""simple docstring"""


		__lowercase				:		int												=				{}
		state_dict.pop("pixel_mean",  _lowerCamelCase				)
		state_dict.pop("pixel_std",  _lowerCamelCase				)

		__lowercase				:		List[Any]												=				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:
								__lowercase				:		Dict												=				key.replace(_lowerCamelCase,  _lowerCamelCase				)

				if re.match(_lowerCamelCase,  _lowerCamelCase				):
						__lowercase				:		Any												=				int(re.match(_lowerCamelCase,  _lowerCamelCase				).group(2				)				)
						if layer_nb == 0:
								__lowercase				:		Tuple												=				key.replace("layers.0",  "proj_in"				)
						elif layer_nb == 1:
								__lowercase				:		List[Any]												=				key.replace("layers.1",  "layers.0"				)
						elif layer_nb == 2:
								__lowercase				:		str												=				key.replace("layers.2",  "proj_out"				)

				__lowercase				:		Any												=				value

		__lowercase				:		Dict												=				model_state_dict[
		    "prompt_encoder.shared_embedding.positional_embedding"
		]

		return model_state_dict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase="ybelkada/segment-anything"				)			->Optional[int]:






		"""simple docstring"""


		__lowercase				:		Any												=				hf_hub_download(_lowerCamelCase,  F'checkpoints/{model_name}.pth'				)

		if "sam_vit_b" in model_name:
				__lowercase				:		Any												=				SamConfig()
		elif "sam_vit_l" in model_name:
				__lowercase				:		int												=				SamVisionConfig(
				    hidden_size=10_24,  num_hidden_layers=24,  num_attention_heads=16,  global_attn_indexes=[5, 11, 17, 23],  )

				__lowercase				:		str												=				SamConfig(
				    vision_config=_lowerCamelCase,  )
		elif "sam_vit_h" in model_name:
				__lowercase				:		Any												=				SamVisionConfig(
				    hidden_size=12_80,  num_hidden_layers=32,  num_attention_heads=16,  global_attn_indexes=[7, 15, 23, 31],  )

				__lowercase				:		Any												=				SamConfig(
				    vision_config=_lowerCamelCase,  )

		__lowercase				:		int												=				torch.load(_lowerCamelCase,  map_location="cpu"				)
		__lowercase				:		Optional[int]												=				replace_keys(_lowerCamelCase				)

		__lowercase				:		List[Any]												=				SamImageProcessor()

		__lowercase				:		Any												=				SamProcessor(image_processor=_lowerCamelCase				)
		__lowercase				:		Optional[Any]												=				SamModel(_lowerCamelCase				)

		hf_model.load_state_dict(_lowerCamelCase				)
		__lowercase				:		Any												=				hf_model.to("cuda"				)

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

		__lowercase				:		Optional[Any]												=				[[[4_00, 6_50]]]
		__lowercase				:		Any												=				[[1]]

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

		with torch.no_grad():
				__lowercase				:		List[Any]												=				hf_model(**_lowerCamelCase				)
		__lowercase				:		List[Any]												=				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

				__lowercase				:		int												=				processor(
				    images=np.array(_lowerCamelCase				),  input_points=_lowerCamelCase,  input_labels=_lowerCamelCase,  return_tensors="pt"				).to("cuda"				)

				with torch.no_grad():
						__lowercase				:		Tuple												=				hf_model(**_lowerCamelCase				)
				__lowercase				:		Tuple												=				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

				__lowercase				:		Tuple												=				((75, 2_75, 17_25, 8_50),)

				__lowercase				:		List[str]												=				processor(images=np.array(_lowerCamelCase				),  input_boxes=_lowerCamelCase,  return_tensors="pt"				).to("cuda"				)

				with torch.no_grad():
						__lowercase				:		Union[str, Any]												=				hf_model(**_lowerCamelCase				)
				__lowercase				:		Any												=				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.
				__lowercase				:		List[Any]												=				[[[4_00, 6_50], [8_00, 6_50]]]
				__lowercase				:		List[Any]												=				[[1, 1]]

				__lowercase				:		Tuple												=				processor(
				    images=np.array(_lowerCamelCase				),  input_points=_lowerCamelCase,  input_labels=_lowerCamelCase,  return_tensors="pt"				).to("cuda"				)

				with torch.no_grad():
						__lowercase				:		List[str]												=				hf_model(**_lowerCamelCase				)
				__lowercase				:		List[Any]												=				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__":
						__A   :  Dict					=      argparse.ArgumentParser()
						__A   :  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',
						)

						__A   :  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 collections import deque

from .hash_table import HashTable




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	Optional[int]    ,   *lowercase__					:	Any    ,   **lowercase__					:	Union[str, Any]						):
						super().__init__(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	Dict    ,   lowercase__					:	Dict						):
						__lowercase				:		Dict												=				deque([]						) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.values[key]



				def       snake_case    (     self					:	str						):
						return (
						    sum(self.charge_factor - len(lowercase__						) for slot in self.values						)
						    / self.size_table
						    * self.charge_factor
						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str=None						):
						if not (
						    len(self.values[key]						) == self.charge_factor and self.values.count(lowercase__						) == 0
						):
								return key
						return super()._collision_resolution(lowercase__    ,   lowercase__						)
