





"""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	     =					{
    '''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 (     a_  :List[Any])						->						List[Any]:
    __a	:  List[Any]         =					{}
    state_dict.pop('''pixel_mean'''				,    a_)
    state_dict.pop('''pixel_std'''				,    a_)

    __a	:  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:
                __a	:  int         =					key.replace(a_				,    a_)

        if re.match(a_				,    a_):
            __a	:  Optional[Any]         =					int(re.match(a_				,    a_).group(2))
            if layer_nb == 0:
                __a	:  Any         =					key.replace('''layers.0'''				,    '''proj_in''')
            elif layer_nb == 1:
                __a	:  Dict         =					key.replace('''layers.1'''				,    '''layers.0''')
            elif layer_nb == 2:
                __a	:  Optional[int]         =					key.replace('''layers.2'''				,    '''proj_out''')

        __a	:  int         =					value

    __a	:  Union[str, Any]         =					model_state_dict[
        '''prompt_encoder.shared_embedding.positional_embedding'''
    ]

    return model_state_dict

def       __A (     a_  :Optional[int]				,    a_  :Optional[Any]				,    a_  :Dict				,    a_  :Optional[int]="ybelkada/segment-anything")						->						Dict:
    __a	:  Dict         =					hf_hub_download(a_				,    F"""checkpoints/{model_name}.pth""")

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

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

        __a	:  Optional[int]         =					SamConfig(
            vision_config=a_				,    )

    __a	:  int         =					torch.load(a_				,    map_location='''cpu''')
    __a	:  Tuple         =					replace_keys(a_)

    __a	:  Optional[int]         =					SamImageProcessor()

    __a	:  Any         =					SamProcessor(image_processor=a_)
    __a	:  Any         =					SamModel(a_)

    hf_model.load_state_dict(a_)
    __a	:  Dict         =					hf_model.to('''cuda''')

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

    __a	:  Tuple         =					[[[4_00, 6_50]]]
    __a	:  Tuple         =					[[1]]

    __a	:  Tuple         =					processor(images=np.array(a_)				,    return_tensors='''pt''').to('''cuda''')

    with torch.no_grad():
        __a	:  str         =					hf_model(**a_)
    __a	:  Optional[int]         =					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

        __a	:  Any         =					processor(
            images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

        with torch.no_grad():
            __a	:  Optional[int]         =					hf_model(**a_)
        __a	:  Optional[int]         =					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

        __a	:  str         =					((75, 2_75, 17_25, 8_50),)

        __a	:  List[str]         =					processor(images=np.array(a_)				,    input_boxes=a_				,    return_tensors='''pt''').to('''cuda''')

        with torch.no_grad():
            __a	:  Any         =					hf_model(**a_)
        __a	:  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.
        __a	:  int         =					[[[4_00, 6_50], [8_00, 6_50]]]
        __a	:  Dict         =					[[1, 1]]

        __a	:  Optional[Any]         =					processor(
            images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

        with torch.no_grad():
            __a	:  int         =					hf_model(**a_)
        __a	:  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	     =					argparse.ArgumentParser()
  A	     =					['''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	     =					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 sys
from collections import deque
from typing import Generic, TypeVar

A	     =					TypeVar('''T''')




class 			__lowercase     (     Generic[T]							):







					'''simple docstring'''




					__lowerCAmelCase					=					42  # Cache store of keys
					__lowerCAmelCase					=					42  # References of the keys in cache
					__lowerCAmelCase					=					10  # Maximum capacity of cache







					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Optional[int]         =					deque()
									__a	:  Dict         =					set()
									if not n:
													__a	:  List[Any]         =					sys.maxsize
									elif n < 0:
													raise ValueError('''n should be an integer greater than 0.'''       )
									else:
													__a	:  str         =					n







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if x not in self.key_reference:
													if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																	__a	:  int         =					self.dq_store.pop()
																	self.key_reference.remove(_UpperCAmelCase       )
									else:
													self.dq_store.remove(_UpperCAmelCase       )

									self.dq_store.appendleft(_UpperCAmelCase       )
									self.key_reference.add(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									for k in self.dq_store:
													print(_UpperCAmelCase       )







					def __repr__(  self       ):
									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A	     =					LRUCache(4)
		lru_cache.refer('''A''')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('''A''')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"