




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,
)


SCREAMING_SNAKE_CASE__       :      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      (      _SCREAMING_SNAKE_CASE    )	-> Tuple:
       lowerCamelCase   :      Optional[int]			 =					{}
       state_dict.pop("pixel_mean" ,_SCREAMING_SNAKE_CASE    )
       state_dict.pop("pixel_std" ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Tuple			 =					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:
                            lowerCamelCase   :      Dict			 =					key.replace(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

              if re.match(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ):
                     lowerCamelCase   :      str			 =					int(re.match(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ).group(2    )    )
                     if layer_nb == 0:
                            lowerCamelCase   :      int			 =					key.replace("layers.0" ,"proj_in"    )
                     elif layer_nb == 1:
                            lowerCamelCase   :      int			 =					key.replace("layers.1" ,"layers.0"    )
                     elif layer_nb == 2:
                            lowerCamelCase   :      Tuple			 =					key.replace("layers.2" ,"proj_out"    )

              lowerCamelCase   :      List[Any]			 =					value

       lowerCamelCase   :      Tuple			 =					model_state_dict[
           "prompt_encoder.shared_embedding.positional_embedding"
       ]

       return model_state_dict

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="ybelkada/segment-anything"    )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					hf_hub_download(_SCREAMING_SNAKE_CASE ,f'''checkpoints/{model_name}.pth'''    )

       if "sam_vit_b" in model_name:
              lowerCamelCase   :      Optional[int]			 =					SamConfig()
       elif "sam_vit_l" in model_name:
              lowerCamelCase   :      Optional[Any]			 =					SamVisionConfig(
                  hidden_size=1024 ,num_hidden_layers=24 ,num_attention_heads=16 ,global_attn_indexes=[5, 11, 17, 23] ,)

              lowerCamelCase   :      int			 =					SamConfig(
                  vision_config=_SCREAMING_SNAKE_CASE ,)
       elif "sam_vit_h" in model_name:
              lowerCamelCase   :      Optional[int]			 =					SamVisionConfig(
                  hidden_size=1280 ,num_hidden_layers=32 ,num_attention_heads=16 ,global_attn_indexes=[7, 15, 23, 31] ,)

              lowerCamelCase   :      int			 =					SamConfig(
                  vision_config=_SCREAMING_SNAKE_CASE ,)

       lowerCamelCase   :      Optional[Any]			 =					torch.load(_SCREAMING_SNAKE_CASE ,map_location="cpu"    )
       lowerCamelCase   :      List[Any]			 =					replace_keys(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[int]			 =					SamImageProcessor()

       lowerCamelCase   :      List[str]			 =					SamProcessor(image_processor=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[Any]			 =					SamModel(_SCREAMING_SNAKE_CASE    )

       hf_model.load_state_dict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					hf_model.to("cuda"    )

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

       lowerCamelCase   :      Dict			 =					[[[400, 650]]]
       lowerCamelCase   :      str			 =					[[1]]

       lowerCamelCase   :      Dict			 =					processor(images=np.array(_SCREAMING_SNAKE_CASE    ) ,return_tensors="pt"    ).to("cuda"    )

       with torch.no_grad():
              lowerCamelCase   :      str			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					output.iou_scores.squeeze()

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

              lowerCamelCase   :      str			 =					processor(
                  images=np.array(_SCREAMING_SNAKE_CASE    ) ,input_points=_SCREAMING_SNAKE_CASE ,input_labels=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    ).to("cuda"    )

              with torch.no_grad():
                     lowerCamelCase   :      Any			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      List[str]			 =					output.iou_scores.squeeze()

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

              lowerCamelCase   :      Any			 =					((75, 275, 1725, 850),)

              lowerCamelCase   :      Any			 =					processor(images=np.array(_SCREAMING_SNAKE_CASE    ) ,input_boxes=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    ).to("cuda"    )

              with torch.no_grad():
                     lowerCamelCase   :      Union[str, Any]			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      int			 =					output.iou_scores.squeeze()

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

              # Test with 2 points and 1 image.
              lowerCamelCase   :      Optional[Any]			 =					[[[400, 650], [800, 650]]]
              lowerCamelCase   :      Any			 =					[[1, 1]]

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

              with torch.no_grad():
                     lowerCamelCase   :      Optional[Any]			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      int			 =					output.iou_scores.squeeze()

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   argparse.ArgumentParser()
       SCREAMING_SNAKE_CASE__       :      int  =   ['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',
       )

       SCREAMING_SNAKE_CASE__       :      Any  =   parser.parse_args()

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








from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

				# Make string identifier
				_snake_case		=			0
				for row_vector in self.array:
							for obj in row_vector:
										_snake_case		=			max(lowercase			,  len(str(lowercase	)	)	)
				_snake_case		=			f'''%{max_element_length}s'''

				# Make string and return
				def single_line(lowercase	:    list[float]	) -> str:
							nonlocal string_format_identifier
							_snake_case		=			'['
							line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
							line += "]"
							return line

				s += "\n".join(single_line(lowercase	) for row_vector in self.array	)
				return s



	def __repr__(  self	:    Dict	):


				'''simple docstring'''


				return str(self	)



	def  A							(  self	:    str			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				if not (isinstance(lowercase			,  (list, tuple)	) and len(lowercase	) == 2):
							return False
				elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
							return False
				else:
							return True



	def __getitem__(  self	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				return self.array[loc[0]][loc[1]]



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	)
				assert self.row == another.row and self.column == another.column

				# Add
				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c] + another[r, c]
				return result



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			-self[r, c]
				return result



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


				if isinstance(lowercase			,  (int, float)	):  # Scalar multiplication
							_snake_case		=			Matrix(self.row			,  self.column	)
							for r in range(self.row	):
										for c in range(self.column	):
													_snake_case		=			self[r, c] * another
							return result
				elif isinstance(lowercase			,  lowercase	):  # Matrix multiplication
							assert self.column == another.row
							_snake_case		=			Matrix(self.row			,  another.column	)
							for r in range(self.row	):
										for c in range(another.column	):
													for i in range(self.column	):
																result[r, c] += self[r, i] * another[i, c]
							return result
				else:
							_snake_case		=			f'''Unsupported type given for another ({type(lowercase	)})'''
							raise TypeError(lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.column			,  self.row	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c]
				return result





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	) and isinstance(lowercase			,  lowercase	)
				assert self.row == self.column == u.row == v.row  # u, v should be column vector
				assert u.column == v.column == 1  # u, v should be column vector

				# Calculate
				_snake_case		=			v.transpose()
				_snake_case		=			(v_t * self * u)[0, 0] + 1
				if numerator_factor == 0:
							return None  # It's not invertable
				return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






			def        a_		(       ) ->  None:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			4, -2, 5
						print(f'''u is {u}''' )
						print(f'''v is {v}''' )
						print(f'''uv^T is {u * v.transpose()}''' )
						# Sherman Morrison
						print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()