






'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			_A     :      List[str]   					=None


_A     :      Tuple   					=logging.get_logger(__name__)


_A     :      List[Any]   					={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_A     :      Optional[Any]   					={
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

_A     :      Any   					={
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
_A     :      Optional[int]   					=['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']

class _lowercase     (    _lowercase		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	["""input_ids""", """attention_mask"""]
							a    				=	MBartTokenizer

							a    				=	[]
							a    				=	[]




							def __init__(							self:    int      ,							UpperCamelCase__:    Optional[int]=None      ,							UpperCamelCase__:    Tuple=None      ,							UpperCamelCase__:    int="<s>"      ,							UpperCamelCase__:    Dict="</s>"      ,							UpperCamelCase__:    Tuple="</s>"      ,							UpperCamelCase__:    Union[str, Any]="<s>"      ,							UpperCamelCase__:    Any="<unk>"      ,							UpperCamelCase__:    List[Any]="<pad>"      ,							UpperCamelCase__:    Union[str, Any]="<mask>"      ,							UpperCamelCase__:    Optional[int]=None      ,							UpperCamelCase__:    Any=None      ,							UpperCamelCase__:    List[str]=None      ,							**UpperCamelCase__:    Optional[int]      ,							):
														# Mask token behave like a normal word, i.e. include the space before it
														lowerCamelCase__ :     List[Any]	  =	AddedToken(UpperCamelCase__      ,							lstrip=UpperCamelCase__      ,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__      ,							UpperCamelCase__					) else mask_token

														super().__init__(
														    vocab_file=UpperCamelCase__      ,							tokenizer_file=UpperCamelCase__      ,							bos_token=UpperCamelCase__      ,							eos_token=UpperCamelCase__      ,							sep_token=UpperCamelCase__      ,							cls_token=UpperCamelCase__      ,							unk_token=UpperCamelCase__      ,							pad_token=UpperCamelCase__      ,							mask_token=UpperCamelCase__      ,							src_lang=UpperCamelCase__      ,							tgt_lang=UpperCamelCase__      ,							additional_special_tokens=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

														lowerCamelCase__ :     Dict	  =	vocab_file
														lowerCamelCase__ :     Optional[Any]	  =	False if not self.vocab_file else True

														lowerCamelCase__ :     List[Any]	  =	FAIRSEQ_LANGUAGE_CODES.copy()

														if additional_special_tokens is not None:
																					# Only add those special tokens if they are not already there.
																					_additional_special_tokens.extend(
																					    [t for t in additional_special_tokens if t not in _additional_special_tokens]					)

														self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}					)
														lowerCamelCase__ :     Dict	  =	{
														    lang_code: self.convert_tokens_to_ids(UpperCamelCase__					) for lang_code in FAIRSEQ_LANGUAGE_CODES
														}

														lowerCamelCase__ :     Optional[int]	  =	src_lang if src_lang is not None else """en_XX"""
														lowerCamelCase__ :     List[str]	  =	self.convert_tokens_to_ids(self._src_lang					)
														lowerCamelCase__ :     Optional[int]	  =	tgt_lang
														self.set_src_lang_special_tokens(self._src_lang					)




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														return self._src_lang




							@src_lang.setter
							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     Any	  =	new_src_lang
														self.set_src_lang_special_tokens(self._src_lang					)




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):
														if token_ids_a is None:
																					return self.prefix_tokens + token_ids_a + self.suffix_tokens
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):
														lowerCamelCase__ :     Tuple	  =	[self.sep_token_id]
														lowerCamelCase__ :     Optional[Any]	  =	[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 + sep + token_ids_a + sep					) * [0]




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str]      ,							UpperCamelCase__:    Optional[str]      ,							**UpperCamelCase__:    List[Any]					):
														if src_lang is None or tgt_lang is None:
																					raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""					)
														lowerCamelCase__ :     Any	  =	src_lang
														lowerCamelCase__ :     List[str]	  =	self(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	self.convert_tokens_to_ids(UpperCamelCase__					)
														lowerCamelCase__ :     List[Any]	  =	tgt_lang_id
														return inputs




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    str = "en_XX"      ,							UpperCamelCase__:    Optional[List[str]] = None      ,							UpperCamelCase__:    str = "ro_RO"      ,							**UpperCamelCase__:    Optional[Any]      ,							):
														lowerCamelCase__ :     Tuple	  =	src_lang
														lowerCamelCase__ :     Any	  =	tgt_lang
														return super().prepare_seqaseq_batch(UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return self.set_src_lang_special_tokens(self.src_lang					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														return self.set_tgt_lang_special_tokens(self.tgt_lang					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     List[Any]	  =	self.convert_tokens_to_ids(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	[]
														lowerCamelCase__ :     str	  =	[self.eos_token_id, self.cur_lang_code]

														lowerCamelCase__ :     Optional[int]	  =	self.convert_ids_to_tokens(self.prefix_tokens					)
														lowerCamelCase__ :     Optional[Any]	  =	self.convert_ids_to_tokens(self.suffix_tokens					)

														lowerCamelCase__ :     int	  =	processors.TemplateProcessing(
														    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str      ,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str      ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,							self.prefix_tokens + self.suffix_tokens					)					)      ,							)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     Optional[int]	  =	self.convert_tokens_to_ids(UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	[]
														lowerCamelCase__ :     int	  =	[self.eos_token_id, self.cur_lang_code]

														lowerCamelCase__ :     List[str]	  =	self.convert_ids_to_tokens(self.prefix_tokens					)
														lowerCamelCase__ :     Optional[int]	  =	self.convert_ids_to_tokens(self.suffix_tokens					)

														lowerCamelCase__ :     Optional[int]	  =	processors.TemplateProcessing(
														    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str      ,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str      ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,							self.prefix_tokens + self.suffix_tokens					)					)      ,							)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""					)

														if not os.path.isdir(UpperCamelCase__					):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
																					return
														lowerCamelCase__ :     int	  =	os.path.join(
														    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase__					):
																					copyfile(self.vocab_file      ,							UpperCamelCase__					)

														return (out_vocab_file,)









'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





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

							return name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Tuple:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__ :     List[str]	  =	orig_state_dict.pop(UpperCamelCase				)

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

														else:
																					lowerCamelCase__ :     int	  =	val

							return orig_state_dict





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

							lowerCamelCase__ :     List[Any]	  =	ViTMAEForPreTraining(UpperCamelCase				)

							lowerCamelCase__ :     str	  =	torch.hub.load_state_dict_from_url(UpperCamelCase    ,      map_location="""cpu"""				)["""model"""]

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

							lowerCamelCase__ :     List[str]	  =	convert_state_dict(UpperCamelCase    ,      UpperCamelCase				)

							model.load_state_dict(UpperCamelCase				)
							model.eval()

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

							lowerCamelCase__ :     List[Any]	  =	Image.open(requests.get(UpperCamelCase    ,      stream=UpperCamelCase				).raw				)
							lowerCamelCase__ :     str	  =	ViTMAEImageProcessor(size=config.image_size				)
							lowerCamelCase__ :     Any	  =	image_processor(images=UpperCamelCase    ,      return_tensors="""pt"""				)

							# forward pass
							torch.manual_seed(2				)
							lowerCamelCase__ :     Optional[Any]	  =	model(**UpperCamelCase				)
							lowerCamelCase__ :     Optional[Any]	  =	outputs.logits

							if "large" in checkpoint_url:
														lowerCamelCase__ :     List[Any]	  =	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__ :     Optional[Any]	  =	torch.tensor(
														    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
							else:
														lowerCamelCase__ :     int	  =	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__":
			_A     :      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.'''
			)

			_A     :      Tuple   					=parser.parse_args()
			convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

