


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



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

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        _lowercase			:				List[Any] 						=						orig_state_dict.pop(lowerCamelCase_					)

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

        else:
            _lowercase			:				str 						=						val

    return orig_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    _lowercase			:				str 						=						ViTMAEConfig()
    if "large" in checkpoint_url:
        _lowercase			:				Dict 						=						1024
        _lowercase			:				List[str] 						=						4096
        _lowercase			:				str 						=						24
        _lowercase			:				Optional[Any] 						=						16
    elif "huge" in checkpoint_url:
        _lowercase			:				int 						=						14
        _lowercase			:				Union[str, Any] 						=						1280
        _lowercase			:				Union[str, Any] 						=						5120
        _lowercase			:				Tuple 						=						32
        _lowercase			:				Optional[Any] 						=						16

    _lowercase			:				Union[str, Any] 						=						ViTMAEForPreTraining(lowerCamelCase_					)

    _lowercase			:				Dict 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    _lowercase			:				Union[str, Any] 						=						ViTMAEImageProcessor(size=config.image_size					)

    _lowercase			:				Optional[Any] 						=						convert_state_dict(lowerCamelCase_				,					lowerCamelCase_					)

    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    _lowercase			:				Optional[Any] 						=						'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

    _lowercase			:				Dict 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[str] 						=						ViTMAEImageProcessor(size=config.image_size					)
    _lowercase			:				str 						=						image_processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    # forward pass
    torch.manual_seed(2					)
    _lowercase			:				str 						=						model(**lowerCamelCase_					)
    _lowercase			:				Optional[int] 						=						outputs.logits

    if "large" in checkpoint_url:
        _lowercase			:				Union[str, Any] 						=						torch.tensor(
            [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]					)
    elif "huge" in checkpoint_url:
        _lowercase			:				Any 						=						torch.tensor(
            [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]					)
    else:
        _lowercase			:				Optional[int] 						=						torch.tensor(
            [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]					)

    # 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__":
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					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			: Dict				   =					parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)


_lowerCamelCase      :					Tuple					    =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase      :					List[str]					    =      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase      :					List[str]					    =      0

_lowerCamelCase      :					Any					    =      0xe_0_0_0
_lowerCamelCase      :					Union[str, Any]					    =      0xe_0_0_1
_lowerCamelCase      :					Any					    =      0xe_0_0_2
_lowerCamelCase      :					List[str]					    =      0xe_0_0_3
_lowerCamelCase      :					Any					    =      0xe_0_0_4

# Maps special codepoints to human-readable names.
_lowerCamelCase      :					Dict[int, str]					    =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase      :					Dict[str, int]					    =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

       def __init__(							self ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=False ,   lowercase__=2_0_4_8 ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else mask_token

             super().__init__(
                 bos_token=lowercase__ ,   eos_token=lowercase__ ,   sep_token=lowercase__ ,   cls_token=lowercase__ ,   pad_token=lowercase__ ,   mask_token=lowercase__ ,   add_prefix_space=lowercase__ ,   model_max_length=lowercase__ ,   **lowercase__ ,   )

             # Creates a mapping for looking up the IDs of special symbols.
             __A    ={}
             for codepoint, name in SPECIAL_CODEPOINTS.items():
                   __A    =codepoint

             # Creates a mapping for looking up the string forms of special symbol IDs.
             __A    ={
                 codepoint: name for name, codepoint in self._special_codepoints.items()
             }

             __A    =UNICODE_VOCAB_SIZE
             __A    =len(self._special_codepoints     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._unicode_vocab_size

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return list(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   return ord(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid token: \'{token}\''''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   if index in SPECIAL_CODEPOINTS:
                         return SPECIAL_CODEPOINTS[index]
                   return chr(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid id: {index}'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return "".join(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =cls + token_ids_a + sep
             if token_ids_a is not None:
                   result += token_ids_a + sep
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=lowercase__ ,   token_ids_a=lowercase__ ,   already_has_special_tokens=lowercase__     )

             __A    =[1] + ([0] * len(lowercase__     )) + [1]
             if token_ids_a is not None:
                   result += ([0] * len(lowercase__     )) + [1]
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =len(cls + token_ids_a + sep     ) * [0]
             if token_ids_a is not None:
                   result += len(token_ids_a + sep     ) * [1]
             return result


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return ()


