






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if "cls_token" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''cls_token''',  '''vit.embeddings.cls_token''' )
				if "mask_token" in name:
								__magic_name__		:Dict        =      name.replace('''mask_token''',  '''decoder.mask_token''' )
				if "decoder_pos_embed" in name:
								__magic_name__		:Any        =      name.replace('''decoder_pos_embed''',  '''decoder.decoder_pos_embed''' )
				if "pos_embed" in name and "decoder" not in name:
								__magic_name__		:List[str]        =      name.replace('''pos_embed''',  '''vit.embeddings.position_embeddings''' )
				if "patch_embed.proj" in name:
								__magic_name__		:Dict        =      name.replace('''patch_embed.proj''',  '''vit.embeddings.patch_embeddings.projection''' )
				if "patch_embed.norm" in name:
								__magic_name__		:Optional[int]        =      name.replace('''patch_embed.norm''',  '''vit.embeddings.norm''' )
				if "decoder_blocks" in name:
								__magic_name__		:Optional[int]        =      name.replace('''decoder_blocks''',  '''decoder.decoder_layers''' )
				if "blocks" in name:
								__magic_name__		:Optional[int]        =      name.replace('''blocks''',  '''vit.encoder.layer''' )
				if "attn.proj" in name:
								__magic_name__		:Any        =      name.replace('''attn.proj''',  '''attention.output.dense''' )
				if "attn" in name:
								__magic_name__		:str        =      name.replace('''attn''',  '''attention.self''' )
				if "norm1" in name:
								__magic_name__		:Optional[int]        =      name.replace('''norm1''',  '''layernorm_before''' )
				if "norm2" in name:
								__magic_name__		:Optional[int]        =      name.replace('''norm2''',  '''layernorm_after''' )
				if "mlp.fc1" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''mlp.fc1''',  '''intermediate.dense''' )
				if "mlp.fc2" in name:
								__magic_name__		:Dict        =      name.replace('''mlp.fc2''',  '''output.dense''' )
				if "decoder_embed" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''decoder_embed''',  '''decoder.decoder_embed''' )
				if "decoder_norm" in name:
								__magic_name__		:List[Any]        =      name.replace('''decoder_norm''',  '''decoder.decoder_norm''' )
				if "decoder_pred" in name:
								__magic_name__		:str        =      name.replace('''decoder_pred''',  '''decoder.decoder_pred''' )
				if "norm.weight" in name and "decoder" not in name:
								__magic_name__		:Any        =      name.replace('''norm.weight''',  '''vit.layernorm.weight''' )
				if "norm.bias" in name and "decoder" not in name:
								__magic_name__		:str        =      name.replace('''norm.bias''',  '''vit.layernorm.bias''' )

				return name

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for key in orig_state_dict.copy().keys():
								__magic_name__		:str        =      orig_state_dict.pop(snake_case )

								if "qkv" in key:
												__magic_name__		:Any        =      key.split('''.''' )
												__magic_name__		:Tuple        =      int(key_split[1] )
												if "decoder_blocks" in key:
																__magic_name__		:Optional[Any]        =      config.decoder_hidden_size
																__magic_name__		:Optional[Any]        =      '''decoder.decoder_layers.'''
																if "weight" in key:
																				__magic_name__		:Union[str, Any]        =      val[:dim, :]
																				__magic_name__		:Dict        =      val[dim : dim * 2, :]
																				__magic_name__		:Optional[Any]        =      val[-dim:, :]
																elif "bias" in key:
																				__magic_name__		:Union[str, Any]        =      val[:dim]
																				__magic_name__		:List[Any]        =      val[dim : dim * 2]
																				__magic_name__		:Tuple        =      val[-dim:]
												else:
																__magic_name__		:Any        =      config.hidden_size
																__magic_name__		:str        =      '''vit.encoder.layer.'''
																if "weight" in key:
																				__magic_name__		:Union[str, Any]        =      val[:dim, :]
																				__magic_name__		:str        =      val[dim : dim * 2, :]
																				__magic_name__		:Union[str, Any]        =      val[-dim:, :]
																elif "bias" in key:
																				__magic_name__		:str        =      val[:dim]
																				__magic_name__		:str        =      val[dim : dim * 2]
																				__magic_name__		:Any        =      val[-dim:]

								else:
												__magic_name__		:Optional[Any]        =      val

				return orig_state_dict

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      ViTMAEConfig()
				if "large" in checkpoint_url:
								__magic_name__		:List[Any]        =      1_0_2_4
								__magic_name__		:int        =      4_0_9_6
								__magic_name__		:str        =      2_4
								__magic_name__		:Any        =      1_6
				elif "huge" in checkpoint_url:
								__magic_name__		:str        =      1_4
								__magic_name__		:str        =      1_2_8_0
								__magic_name__		:Optional[int]        =      5_1_2_0
								__magic_name__		:int        =      3_2
								__magic_name__		:int        =      1_6

				__magic_name__		:Optional[Any]        =      ViTMAEForPreTraining(snake_case )

				__magic_name__		:Any        =      torch.hub.load_state_dict_from_url(snake_case,  map_location='''cpu''' )['''model''']

				__magic_name__		:str        =      ViTMAEImageProcessor(size=config.image_size )

				__magic_name__		:Dict        =      convert_state_dict(snake_case,  snake_case )

				model.load_state_dict(snake_case )
				model.eval()

				__magic_name__		:Tuple        =      '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				__magic_name__		:Optional[Any]        =      Image.open(requests.get(snake_case,  stream=snake_case ).raw )
				__magic_name__		:int        =      ViTMAEImageProcessor(size=config.image_size )
				__magic_name__		:Optional[int]        =      image_processor(images=snake_case,  return_tensors='''pt''' )

				# forward pass
				torch.manual_seed(2 )
				__magic_name__		:Any        =      model(**snake_case )
				__magic_name__		:Union[str, Any]        =      outputs.logits

				if "large" in checkpoint_url:
								__magic_name__		:Optional[int]        =      torch.tensor(
								    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] )
				elif "huge" in checkpoint_url:
								__magic_name__		:Any        =      torch.tensor(
								    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] )
				else:
								__magic_name__		:Any        =      torch.tensor(
								    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] )

				# verify logits
				assert torch.allclose(logits[0, :3, :3],  snake_case,  atol=1E-4 )

				print(f'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(snake_case )

				print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			List[str]							=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
					    type=str,
					    help="""URL of the checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					SCREAMING_SNAKE_CASE__      :			str							=		parser.parse_args()
					convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)














from __future__ import annotations

from typing import Any
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 0     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      row, column
								__magic_name__		:Union[str, Any]        =      [[default_value for c in range(__lowerCAmelCase     )] for r in range(__lowerCAmelCase     )]







				def __str__(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

								# Make string identifier
								__magic_name__		:Dict        =      0
								for row_vector in self.array:
												for obj in row_vector:
																__magic_name__		:Dict        =      max(__lowerCAmelCase       ,					len(str(__lowerCAmelCase     )     )     )
								__magic_name__		:List[str]        =      F'''%{max_element_length}s'''

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

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







				def __repr__(						self     ):
								"""simple docstring"""
								return str(self     )







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







				def __getitem__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								return self.array[loc[0]][loc[1]]







				def __setitem__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      value







				def __add__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == another.row and self.column == another.column

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







				def __neg__(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      -self[r, c]
								return result







				def __sub__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self + (-another)







				def __mul__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								if isinstance(__lowerCAmelCase       ,					(int, float)     ):  # Scalar multiplication
												__magic_name__		:List[str]        =      Matrix(self.row       ,					self.column     )
												for r in range(self.row     ):
																for c in range(self.column     ):
																				__magic_name__		:Tuple        =      self[r, c] * another
												return result
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):  # Matrix multiplication
												assert self.column == another.row
												__magic_name__		:List[Any]        =      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:
												__magic_name__		:List[str]        =      F'''Unsupported type given for another ({type(__lowerCAmelCase     )})'''
												raise TypeError(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix(self.column       ,					self.row     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:List[str]        =      self[r, c]
								return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								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
								__magic_name__		:int        =      v.transpose()
								__magic_name__		:str        =      (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      __lowercase					( ):







									"""simple docstring"""






									__magic_name__		:Optional[int]        =      Matrix(3,  3,  0 )
									for i in range(3 ):
													__magic_name__		:Tuple        =      1
									print(f'''a^(-1) is {ainv}''' )
									# u, v
									__magic_name__		:Optional[int]        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      1, 2, -3
									__magic_name__		:int        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      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(snake_case,  snake_case )}''' )

					def      __lowercase					( ):







									"""simple docstring"""






									import doctest

									doctest.testmod()

					testa()






