import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any						)					->				Tuple:

						"""simple docstring"""



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

						return name







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Optional[Any]						)					->				str:

						"""simple docstring"""



						for key in orig_state_dict.copy().keys():
												lowerCAmelCase__        =  orig_state_dict.pop(UpperCamelCase_						)

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

												else:
																		lowerCAmelCase__        =  val

						return orig_state_dict







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Optional[int]						)					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  ViTMAEConfig()
						if "large" in checkpoint_url:
												lowerCAmelCase__        =  1024
												lowerCAmelCase__        =  4096
												lowerCAmelCase__        =  24
												lowerCAmelCase__        =  16
						elif "huge" in checkpoint_url:
												lowerCAmelCase__        =  14
												lowerCAmelCase__        =  1280
												lowerCAmelCase__        =  5120
												lowerCAmelCase__        =  32
												lowerCAmelCase__        =  16

						lowerCAmelCase__        =  ViTMAEForPreTraining(UpperCamelCase_						)

						lowerCAmelCase__        =  torch.hub.load_state_dict_from_url(UpperCamelCase_      ,    map_location='cpu'						)['model']

						lowerCAmelCase__        =  ViTMAEImageProcessor(size=config.image_size						)

						lowerCAmelCase__        =  convert_state_dict(UpperCamelCase_      ,    UpperCamelCase_						)

						model.load_state_dict(UpperCamelCase_						)
						model.eval()

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

						lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
						lowerCAmelCase__        =  ViTMAEImageProcessor(size=config.image_size						)
						lowerCAmelCase__        =  image_processor(images=UpperCamelCase_      ,    return_tensors='pt'						)

						# forward pass
						torch.manual_seed(2						)
						lowerCAmelCase__        =  model(**UpperCamelCase_						)
						lowerCAmelCase__        =  outputs.logits

						if "large" in checkpoint_url:
												lowerCAmelCase__        =  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:
												lowerCAmelCase__        =  torch.tensor(
												    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]						)
						else:
												lowerCAmelCase__        =  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]      ,    UpperCamelCase_      ,    atol=1e-4						)

						print(F"Saving model to {pytorch_dump_folder_path}"						)
						model.save_pretrained(UpperCamelCase_						)

						print(F"Saving image processor to {pytorch_dump_folder_path}"						)
						image_processor.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case      :      Dict             =       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."""
					)

					__snake_case      :      Union[str, Any]             =       parser.parse_args()
					convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__snake_case      :      Optional[Any]             =       TypeVar("""KEY""")
__snake_case      :      str             =       TypeVar("""VAL""")


@dataclass(frozen=__lowercase						,   slots=__lowercase)
class       __SCREAMING_SNAKE_CASE   (				Generic[KEY, VAL]):
						_SCREAMING_SNAKE_CASE		:    KEY
						_SCREAMING_SNAKE_CASE		:    VAL




class       __SCREAMING_SNAKE_CASE   (				_Item):






						def __init__(		self				):






												"""simple docstring"""
												super().__init__(_UpperCamelCase       ,  _UpperCamelCase				)







						def __bool__(		self				):






												"""simple docstring"""
												return False




__snake_case      :      int             =       _DeletedItem()


class       __SCREAMING_SNAKE_CASE   (				MutableMapping[KEY, VAL]):






						def __init__(		self       ,  _UpperCamelCase = 8       ,  _UpperCamelCase = 0.75				):






												"""simple docstring"""
												lowerCAmelCase__        =  initial_block_size
												lowerCAmelCase__        =  [None] * initial_block_size
												assert 0.0 < capacity_factor < 1.0
												lowerCAmelCase__        =  capacity_factor
												lowerCAmelCase__        =  0






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return hash(_UpperCamelCase				) % len(self._buckets				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return (ind + 1) % len(self._buckets				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self._buckets[ind]
												if not stored:
																		lowerCAmelCase__        =  _Item(_UpperCamelCase       ,  _UpperCamelCase				)
																		self._len += 1
																		return True
												elif stored.key == key:
																		lowerCAmelCase__        =  _Item(_UpperCamelCase       ,  _UpperCamelCase				)
																		return True
												else:
																		return False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  len(self._buckets				) * self._capacity_factor
												return len(self				) >= int(_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if len(self._buckets				) <= self._initial_block_size:
																		return False
												lowerCAmelCase__        =  len(self._buckets				) * self._capacity_factor / 2
												return len(self				) < limit






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self._buckets
												lowerCAmelCase__        =  [None] * new_size
												lowerCAmelCase__        =  0
												for item in old_buckets:
																		if item:
																								self._add_item(item.key       ,  item.val				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self._resize(len(self._buckets				) * 2				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self._resize(len(self._buckets				) // 2				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self._get_bucket_index(_UpperCamelCase				)
												for _ in range(len(self._buckets				)				):
																		yield ind
																		lowerCAmelCase__        =  self._get_next_ind(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												for ind in self._iterate_buckets(_UpperCamelCase				):
																		if self._try_set(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):
																								break






						def __setitem__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if self._is_full():
																		self._size_up()

												self._add_item(_UpperCamelCase       ,  _UpperCamelCase				)






						def __delitem__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												for ind in self._iterate_buckets(_UpperCamelCase				):
																		lowerCAmelCase__        =  self._buckets[ind]
																		if item is None:
																								raise KeyError(_UpperCamelCase				)
																		if item is _deleted:
																								continue
																		if item.key == key:
																								lowerCAmelCase__        =  _deleted
																								self._len -= 1
																								break
												if self._is_sparse():
																		self._size_down()






						def __getitem__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												for ind in self._iterate_buckets(_UpperCamelCase				):
																		lowerCAmelCase__        =  self._buckets[ind]
																		if item is None:
																								break
																		if item is _deleted:
																								continue
																		if item.key == key:
																								return item.val
												raise KeyError(_UpperCamelCase				)






						def __len__(		self				):






												"""simple docstring"""
												return self._len






						def __iter__(		self				):






												"""simple docstring"""
												yield from (item.key for item in self._buckets if item)







						def __repr__(		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  ' ,'.join(
												    F"{item.key}: {item.val}" for item in self._buckets if item				)
												return F"HashMap({val_string})"





