


"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def   snake_case						(				_a:     Any							)->		List[Any]:
						'''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   snake_case						(				_a:     Optional[Any]		,   _a:     List[Any]							)->		Tuple:
						'''simple docstring'''


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

												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   snake_case						(				_a:     Union[str, Any]		,   _a:     List[Any]							)->		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(lowerCAmelCase__							)

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

						lowerCamelCase__           =		ViTMAEImageProcessor(size=config.image_size							)

						lowerCamelCase__           =		convert_state_dict(lowerCAmelCase__		,   lowerCAmelCase__							)

						model.load_state_dict(lowerCAmelCase__							)
						model.eval()

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

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

						# forward pass
						torch.manual_seed(2							)
						lowerCamelCase__           =		model(**lowerCAmelCase__							)
						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]		,   lowerCAmelCase__		,   atol=1E-4							)

						print(F'Saving model to {pytorch_dump_folder_path}'							)
						model.save_pretrained(lowerCAmelCase__							)

						print(F'Saving image processor to {pytorch_dump_folder_path}'							)
						image_processor.save_pretrained(lowerCAmelCase__							)


if __name__ == "__main__":
			_snake_case          =							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          =							parser.parse_args()
			convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""


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

_snake_case          =							TypeVar("KEY")
_snake_case          =							TypeVar("VAL")



@dataclass(frozen=SCREAMING_SNAKE_CASE_		,		slots=SCREAMING_SNAKE_CASE_ )
class   _a      (      Generic[KEY, VAL] ):
    a_    :				KEY
    a_    :				VAL





class   _a      (      _Item ):
    def __init__( self							:					List[str]				):
          super().__init__(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)


    def __bool__( self							:					str				):
          return False





_snake_case          =							_DeletedItem()



class   _a      (      MutableMapping[KEY, VAL] ):
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int = 8							,					SCREAMING_SNAKE_CASE__							:					float = 0.75				):
          lowerCamelCase__           =		initial_block_size
          lowerCamelCase__           =		[None] * initial_block_size
          assert 0.0 < capacity_factor < 1.0
          lowerCamelCase__           =		capacity_factor
          lowerCamelCase__           =		0
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          return hash(SCREAMING_SNAKE_CASE__				) % len(self._buckets				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					int				):
          return (ind + 1) % len(self._buckets				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					KEY							,					SCREAMING_SNAKE_CASE__							:					VAL				):
          lowerCamelCase__           =		self._buckets[ind]
          if not stored:
                lowerCamelCase__           =		_Item(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                self._len += 1
                return True
          elif stored.key == key:
                lowerCamelCase__           =		_Item(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                return True
          else:
                return False
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		len(self._buckets				) * self._capacity_factor
          return len(self				) >= int(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					int				):
          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							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int				):
          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							:					List[str]				):
          self._resize(len(self._buckets				) * 2				)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          self._resize(len(self._buckets				) // 2				)
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          lowerCamelCase__           =		self._get_bucket_index(SCREAMING_SNAKE_CASE__				)
          for _ in range(len(self._buckets				)				):
                yield ind
                lowerCamelCase__           =		self._get_next_ind(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					KEY							,					SCREAMING_SNAKE_CASE__							:					VAL				):
          for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__				):
                if self._try_set(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				):
                      break
    def __setitem__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					KEY							,					SCREAMING_SNAKE_CASE__							:					VAL				):
          if self._is_full():
                self._size_up()

          self._add_item(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    def __delitem__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__				):
                lowerCamelCase__           =		self._buckets[ind]
                if item is None:
                      raise KeyError(SCREAMING_SNAKE_CASE__				)
                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							:					str							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__				):
                lowerCamelCase__           =		self._buckets[ind]
                if item is None:
                      break
                if item is _deleted:
                      continue
                if item.key == key:
                      return item.val
          raise KeyError(SCREAMING_SNAKE_CASE__				)
    def __len__( self							:					List[Any]				):
          return self._len
    def __iter__( self							:					Optional[int]				):
          yield from (item.key for item in self._buckets if item)


    def __repr__( self							:					str				):
          lowerCamelCase__           =		' ,'.join(
              F'{item.key}: {item.val}' for item in self._buckets if item				)
          return F'HashMap({val_string})'



