import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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

    return name



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __UpperCamelCase					:Union[str, Any]        =		orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

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

        else:
            __UpperCamelCase					:Optional[Any]        =		val

    return orig_state_dict



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		ViTMAEConfig()
    if "large" in checkpoint_url:
        __UpperCamelCase					:Optional[Any]        =		1_024
        __UpperCamelCase					:Optional[Any]        =		4_096
        __UpperCamelCase					:int        =		24
        __UpperCamelCase					:Dict        =		16
    elif "huge" in checkpoint_url:
        __UpperCamelCase					:Union[str, Any]        =		14
        __UpperCamelCase					:Optional[Any]        =		1_280
        __UpperCamelCase					:int        =		5_120
        __UpperCamelCase					:Union[str, Any]        =		32
        __UpperCamelCase					:Optional[Any]        =		16

    __UpperCamelCase					:int        =		ViTMAEForPreTraining(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:int        =		torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )['''model''']

    __UpperCamelCase					:List[Any]        =		ViTMAEImageProcessor(size=config.image_size     )

    __UpperCamelCase					:Any        =		convert_state_dict(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    model.load_state_dict(SCREAMING_SNAKE_CASE     )
    model.eval()

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

    __UpperCamelCase					:Dict        =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,      stream=SCREAMING_SNAKE_CASE     ).raw     )
    __UpperCamelCase					:Optional[Any]        =		ViTMAEImageProcessor(size=config.image_size     )
    __UpperCamelCase					:Tuple        =		image_processor(images=SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''     )

    # forward pass
    torch.manual_seed(2     )
    __UpperCamelCase					:str        =		model(**SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:int        =		outputs.logits

    if "large" in checkpoint_url:
        __UpperCamelCase					:Dict        =		torch.tensor(
            [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]     )
    elif "huge" in checkpoint_url:
        __UpperCamelCase					:Optional[int]        =		torch.tensor(
            [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]     )
    else:
        __UpperCamelCase					:List[Any]        =		torch.tensor(
            [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]     )

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

    print(f"""Saving model to {pytorch_dump_folder_path}"""     )
    model.save_pretrained(SCREAMING_SNAKE_CASE     )

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= 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.'''
    )

    __lowercase										= parser.parse_args()
    convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase										= logging.get_logger(__name__)

__lowercase										= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase										= {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase										= {'''allegro/herbert-base-cased''': 514}
__lowercase										= {}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				VOCAB_FILES_NAMES
    a__     :						Dict      =				PRETRAINED_VOCAB_FILES_MAP
    a__     :						Tuple      =				PRETRAINED_INIT_CONFIGURATION
    a__     :						Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__     :						Union[str, Any]      =				HerbertTokenizer







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase="</s>"			,			**__lowercase			,			)	->     Optional[Any]:
        super().__init__(
            __lowercase			,			__lowercase			,			tokenizer_file=__lowercase			,			cls_token=__lowercase			,			unk_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			sep_token=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:List[str]        =		[self.cls_token_id]
        __UpperCamelCase					:Tuple        =		[self.sep_token_id]
        if token_ids_a is None:
            return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase			,			token_ids_a=__lowercase			,			already_has_special_tokens=__lowercase)

        if token_ids_a is None:
            return [1] + ([0] * len(__lowercase)) + [1]
        return [1] + ([0] * len(__lowercase)) + [1] + ([0] * len(__lowercase)) + [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:Optional[Any]        =		[self.sep_token_id]
        __UpperCamelCase					:int        =		[self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:
        __UpperCamelCase					:Optional[int]        =		self._tokenizer.model.save(__lowercase			,			name=__lowercase)
        return tuple(__lowercase)





