

'''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__    :							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 						_a							(					__lowerCAmelCase		:   int  ):

		"""simple docstring"""

		snake_case__    :							Dict							    =	{}
		state_dict.pop('''pixel_mean''' ,				__snake_case  )
		state_dict.pop('''pixel_std''' ,				__snake_case  )

		snake_case__    :							Optional[int]							    =	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:
								snake_case__    :							int							    =	key.replace(__snake_case ,				__snake_case  )

				if re.match(__snake_case ,				__snake_case  ):
						snake_case__    :							Tuple							    =	int(re.match(__snake_case ,				__snake_case  ).group(2  )  )
						if layer_nb == 0:
								snake_case__    :							str							    =	key.replace('''layers.0''' ,				'''proj_in'''  )
						elif layer_nb == 1:
								snake_case__    :							int							    =	key.replace('''layers.1''' ,				'''layers.0'''  )
						elif layer_nb == 2:
								snake_case__    :							Tuple							    =	key.replace('''layers.2''' ,				'''proj_out'''  )

				snake_case__    :							Any							    =	value

		snake_case__    :							Optional[int]							    =	model_state_dict[
		    '''prompt_encoder.shared_embedding.positional_embedding'''
		]

		return model_state_dict




def 						_a							(					__lowerCAmelCase		:   Optional[int] ,				__lowerCAmelCase		:   Any ,				__lowerCAmelCase		:   List[Any] ,				__lowerCAmelCase		:   str="ybelkada/segment-anything"  ):

		"""simple docstring"""

		snake_case__    :							Optional[int]							    =	hf_hub_download(__snake_case ,				F"""checkpoints/{model_name}.pth"""  )

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

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

				snake_case__    :							Optional[Any]							    =	SamConfig(
				    vision_config=__snake_case ,				)

		snake_case__    :							Dict							    =	torch.load(__snake_case ,				map_location='''cpu'''  )
		snake_case__    :							int							    =	replace_keys(__snake_case  )

		snake_case__    :							Optional[Any]							    =	SamImageProcessor()

		snake_case__    :							List[str]							    =	SamProcessor(image_processor=__snake_case  )
		snake_case__    :							int							    =	SamModel(__snake_case  )

		hf_model.load_state_dict(__snake_case  )
		snake_case__    :							Optional[int]							    =	hf_model.to('''cuda'''  )

		snake_case__    :							Dict							    =	'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
		snake_case__    :							List[Any]							    =	Image.open(requests.get(__snake_case ,				stream=__snake_case  ).raw  ).convert('''RGB'''  )

		snake_case__    :							List[str]							    =	[[[4_00, 6_50]]]
		snake_case__    :							Any							    =	[[1]]

		snake_case__    :							int							    =	processor(images=np.array(__snake_case  ) ,				return_tensors='''pt'''  ).to('''cuda'''  )

		with torch.no_grad():
				snake_case__    :							int							    =	hf_model(**__snake_case  )
		snake_case__    :							Optional[int]							    =	output.iou_scores.squeeze()

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

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

				with torch.no_grad():
						snake_case__    :							str							    =	hf_model(**__snake_case  )
				snake_case__    :							str							    =	output.iou_scores.squeeze()

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

				snake_case__    :							List[Any]							    =	((75, 2_75, 17_25, 8_50),)

				snake_case__    :							str							    =	processor(images=np.array(__snake_case  ) ,				input_boxes=__snake_case ,				return_tensors='''pt'''  ).to('''cuda'''  )

				with torch.no_grad():
						snake_case__    :							Any							    =	hf_model(**__snake_case  )
				snake_case__    :							Tuple							    =	output.iou_scores.squeeze()

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

				# Test with 2 points and 1 image.
				snake_case__    :							Optional[Any]							    =	[[[4_00, 6_50], [8_00, 6_50]]]
				snake_case__    :							Any							    =	[[1, 1]]

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

				with torch.no_grad():
						snake_case__    :							int							    =	hf_model(**__snake_case  )
				snake_case__    :							Tuple							    =	output.iou_scores.squeeze()

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


if __name__ == "__main__":
					lowerCAmelCase__    :							Tuple					    =    argparse.ArgumentParser()
					lowerCAmelCase__    :							Dict					    =    ['''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__    :							str					    =    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



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   for i in range(len(__snake_case)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         lowerCAmelCase_			=      True
         create_state_space_tree(__snake_case	,   __snake_case	,   index + 1	,   __snake_case)
         current_sequence.pop()
         lowerCAmelCase_			=      False
A_     :						list[int | str]    							=[3, 1, 2, 4]
generate_all_permutations(sequence)

A_     :						list[int | str]    							=["A", "B", "C"]






generate_all_permutations(sequence_a)





