
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  :					str													=       {
    '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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					{}
							state_dict.pop("pixel_mean"						,  _lowerCamelCase)
							state_dict.pop("pixel_std"						,  _lowerCamelCase)

							__UpperCamelCase	:   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:
																												__UpperCamelCase	:   Tuple			   =					key.replace(_lowerCamelCase						,  _lowerCamelCase)

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

														__UpperCamelCase	:   Union[str, Any]			   =					value

							__UpperCamelCase	:   int			   =					model_state_dict[
							    "prompt_encoder.shared_embedding.positional_embedding"
							]

							return model_state_dict




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Any="ybelkada/segment-anything")					->    Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					hf_hub_download(_lowerCamelCase						,  F'checkpoints/{model_name}.pth')

							if "sam_vit_b" in model_name:
														__UpperCamelCase	:   List[str]			   =					SamConfig()
							elif "sam_vit_l" in model_name:
														__UpperCamelCase	:   str			   =					SamVisionConfig(
														    hidden_size=1_024						,  num_hidden_layers=24						,  num_attention_heads=16						,  global_attn_indexes=[5, 11, 17, 23]						,  )

														__UpperCamelCase	:   Any			   =					SamConfig(
														    vision_config=_lowerCamelCase						,  )
							elif "sam_vit_h" in model_name:
														__UpperCamelCase	:   Optional[int]			   =					SamVisionConfig(
														    hidden_size=1_280						,  num_hidden_layers=32						,  num_attention_heads=16						,  global_attn_indexes=[7, 15, 23, 31]						,  )

														__UpperCamelCase	:   Tuple			   =					SamConfig(
														    vision_config=_lowerCamelCase						,  )

							__UpperCamelCase	:   Optional[int]			   =					torch.load(_lowerCamelCase						,  map_location="cpu")
							__UpperCamelCase	:   Union[str, Any]			   =					replace_keys(_lowerCamelCase)

							__UpperCamelCase	:   int			   =					SamImageProcessor()

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

							hf_model.load_state_dict(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					hf_model.to("cuda")

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

							__UpperCamelCase	:   List[str]			   =					[[[400, 650]]]
							__UpperCamelCase	:   Optional[int]			   =					[[1]]

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

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

														__UpperCamelCase	:   List[Any]			   =					processor(
														    images=np.array(_lowerCamelCase)						,  input_points=_lowerCamelCase						,  input_labels=_lowerCamelCase						,  return_tensors="pt").to("cuda")

														with torch.no_grad():
																					__UpperCamelCase	:   Optional[int]			   =					hf_model(**_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					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

														__UpperCamelCase	:   Dict			   =					((75, 275, 1_725, 850),)

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

														with torch.no_grad():
																					__UpperCamelCase	:   Optional[int]			   =					hf_model(**_lowerCamelCase)
														__UpperCamelCase	:   List[str]			   =					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.
														__UpperCamelCase	:   Optional[Any]			   =					[[[400, 650], [800, 650]]]
														__UpperCamelCase	:   Dict			   =					[[1, 1]]

														__UpperCamelCase	:   Optional[Any]			   =					processor(
														    images=np.array(_lowerCamelCase)						,  input_points=_lowerCamelCase						,  input_labels=_lowerCamelCase						,  return_tensors="pt").to("cuda")

														with torch.no_grad():
																					__UpperCamelCase	:   Optional[Any]			   =					hf_model(**_lowerCamelCase)
														__UpperCamelCase	:   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__":
		lowercase  :					Dict													=       argparse.ArgumentParser()
		lowercase  :					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',
		)

		lowercase  :					Dict													=       parser.parse_args()

		convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]						)		-> None:
								__UpperCamelCase	:   dict[str, TrieNode]			   =					{}  # Mapping from char to TrieNode
								__UpperCamelCase	:   List[str]			   =					False







	def    _lowerCamelCase				(					self		:Any     ,   a		:list[str]						)		-> None:
								for word in words:
															self.insert(a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str						)		-> None:
								__UpperCamelCase	:   Dict			   =					self
								for char in word:
															if char not in curr.nodes:
																						__UpperCamelCase	:   List[Any]			   =					TrieNode()
															__UpperCamelCase	:   List[Any]			   =					curr.nodes[char]
								__UpperCamelCase	:   Union[str, Any]			   =					True







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> bool:
								__UpperCamelCase	:   Union[str, Any]			   =					self
								for char in word:
															if char not in curr.nodes:
																						return False
															__UpperCamelCase	:   Union[str, Any]			   =					curr.nodes[char]
								return curr.is_leaf







	def    _lowerCamelCase				(					self		:Any     ,   a		:str						)		-> None:

								def _delete(a		:TrieNode     ,   a		:str     ,   a		:int						) -> bool:
															if index == len(a						):
																						# If word does not exist
																						if not curr.is_leaf:
																													return False
																						__UpperCamelCase	:   str			   =					False
																						return len(curr.nodes						) == 0
															__UpperCamelCase	:   List[Any]			   =					word[index]
															__UpperCamelCase	:   Optional[int]			   =					curr.nodes.get(a						)
															# If char not in current trie node
															if not char_node:
																						return False
															# Flag to check if node can be deleted
															__UpperCamelCase	:   int			   =					_delete(a     ,   a     ,   index + 1						)
															if delete_curr:
																						del curr.nodes[char]
																						return len(curr.nodes						) == 0
															return delete_curr

								_delete(self     ,   a     ,   0						)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			TrieNode						,  _lowerCamelCase :			str)					->    None:






							'''simple docstring'''





							if node.is_leaf:
														print(_lowerCamelCase						,  end=" ")

							for key, value in node.nodes.items():
														print_words(_lowerCamelCase						,  word + key)




def 	_SCREAMING_SNAKE_CASE					(		)					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					"banana bananas bandana band apple all beast".split()
							__UpperCamelCase	:   Union[str, Any]			   =					TrieNode()
							root.insert_many(_lowerCamelCase)
							# print_words(root, "")
							assert all(root.find(_lowerCamelCase) for word in words)
							assert root.find("banana")
							assert not root.find("bandanas")
							assert not root.find("apps")
							assert root.find("apple")
							assert root.find("all")
							root.delete("all")
							assert not root.find("all")
							root.delete("banana")
							assert not root.find("banana")
							assert root.find("bananas")
							return True




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			bool)					->    None:






							'''simple docstring'''





							print(str(_lowerCamelCase)						,  "works!" if passes else "doesn't work :(")




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							assert test_trie()




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							print_results("Testing trie functionality"						,  test_trie())


if __name__ == "__main__":
		main()