






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase : Any						   =       logging.get_logger(__name__)
lowerCamelCase : Tuple						   =       {'''vocab_file''': '''spiece.model'''}

lowerCamelCase : str						   =       {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

lowerCamelCase : Any						   =       {
    '''albert-base-v1''': 5_12,
    '''albert-large-v1''': 5_12,
    '''albert-xlarge-v1''': 5_12,
    '''albert-xxlarge-v1''': 5_12,
    '''albert-base-v2''': 5_12,
    '''albert-large-v2''': 5_12,
    '''albert-xlarge-v2''': 5_12,
    '''albert-xxlarge-v2''': 5_12,
}

lowerCamelCase : Optional[Any]						   =       '''▁'''

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			List[Any]									=      VOCAB_FILES_NAMES
					_A						:			int									=      PRETRAINED_VOCAB_FILES_MAP
					_A						:			Dict									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



					def __init__(  self				:	Union[str, Any]		,     __a				:	str		,     __a				:	List[Any]=True		,     __a				:	List[Any]=True		,     __a				:	List[str]=False		,     __a				:	Any="[CLS]"		,     __a				:	Tuple="[SEP]"		,     __a				:	Tuple="<unk>"		,     __a				:	str="[SEP]"		,     __a				:	Tuple="<pad>"		,     __a				:	Tuple="[CLS]"		,     __a				:	Dict="[MASK]"		,     __a				:	Optional[Dict[str, Any]] = None		,     **__a				:	List[str]		,     )			->  None:


												"""simple docstring"""

												__lowercase      :							Dict				=     (
												    AddedToken(__a		,     lstrip=__a		,     rstrip=__a		,     normalized=__a							)
												    if isinstance(__a		,     __a							)
												    else mask_token
												)

												__lowercase      :							int				=     {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=__a		,     remove_space=__a		,     keep_accents=__a		,     bos_token=__a		,     eos_token=__a		,     unk_token=__a		,     sep_token=__a		,     pad_token=__a		,     cls_token=__a		,     mask_token=__a		,     sp_model_kwargs=self.sp_model_kwargs		,     **__a		,     )

												__lowercase      :							List[Any]				=     do_lower_case
												__lowercase      :							str				=     remove_space
												__lowercase      :							Optional[Any]				=     keep_accents
												__lowercase      :							Any				=     vocab_file

												__lowercase      :							Any				=     spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(__a							)



					@property
					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Any:


												"""simple docstring"""

												return len(self.sp_model							)



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     {self.convert_ids_to_tokens(__a							): i for i in range(self.vocab_size							)}
												vocab.update(self.added_tokens_encoder							)
												return vocab



					def __getstate__(  self				:	Any							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Any				=     self.__dict__.copy()
												__lowercase      :							Dict				=     None
												return state



					def __setstate__(  self				:	Dict		,     __a				:	str							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							int				=     d

												# for backward compatibility
												if not hasattr(self		,     """sp_model_kwargs"""							):
																			__lowercase      :							List[str]				=     {}

												__lowercase      :							Tuple				=     spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(self.vocab_file							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Optional[int]							)			->  int:


												"""simple docstring"""

												if self.remove_space:
																			__lowercase      :							Union[str, Any]				=     """ """.join(inputs.strip().split()							)
												else:
																			__lowercase      :							Any				=     inputs
												__lowercase      :							Optional[int]				=     outputs.replace("""``"""		,     """\""""							).replace("""''"""		,     """\""""							)

												if not self.keep_accents:
																			__lowercase      :							Optional[int]				=     unicodedata.normalize("""NFKD"""		,     __a							)
																			__lowercase      :							Optional[Any]				=     """""".join([c for c in outputs if not unicodedata.combining(__a							)]							)
												if self.do_lower_case:
																			__lowercase      :							str				=     outputs.lower()

												return outputs



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	str							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.preprocess_text(__a							)
												__lowercase      :							Optional[Any]				=     self.sp_model.encode(__a		,     out_type=__a							)
												__lowercase      :							Optional[Any]				=     []
												for piece in pieces:
																			if len(__a							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
																										__lowercase      :							int				=     self.sp_model.EncodeAsPieces(piece[:-1].replace(__a		,     """"""							)							)
																										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																	if len(cur_pieces[0]							) == 1:
																																								__lowercase      :							Optional[int]				=     cur_pieces[1:]
																																	else:
																																								__lowercase      :							str				=     cur_pieces[0][1:]
																										cur_pieces.append(piece[-1]							)
																										new_pieces.extend(__a							)
																			else:
																										new_pieces.append(__a							)

												return new_pieces



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	Dict							)			->  Optional[Any]:


												"""simple docstring"""

												return self.sp_model.PieceToId(__a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Union[str, Any]							)			->  List[str]:


												"""simple docstring"""

												return self.sp_model.IdToPiece(__a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	List[Any]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Any				=     []
												__lowercase      :							Dict				=     """"""
												__lowercase      :							str				=     False
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										if not prev_is_special:
																																	out_string += " "
																										out_string += self.sp_model.decode(__a							) + token
																										__lowercase      :							Optional[Any]				=     True
																										__lowercase      :							List[str]				=     []
																			else:
																										current_sub_tokens.append(__a							)
																										__lowercase      :							Optional[Any]				=     False
												out_string += self.sp_model.decode(__a							)
												return out_string.strip()



					def       lowerCAmelCase				(  self				:	str		,     __a				:	List[int]		,     __a				:	Optional[List[int]] = None							)			->  List[int]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     [self.sep_token_id]
												__lowercase      :							Dict				=     [self.cls_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       lowerCAmelCase				(  self				:	int		,     __a				:	List[int]		,     __a				:	Optional[List[int]] = None		,     __a				:	bool = False							)			->  List[int]:


												"""simple docstring"""

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

												if token_ids_a is not None:
																			return [1] + ([0] * len(__a							)) + [1] + ([0] * len(__a							)) + [1]
												return [1] + ([0] * len(__a							)) + [1]



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	List[int]		,     __a				:	Optional[List[int]] = None							)			->  List[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     [self.sep_token_id]
												__lowercase      :							List[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							) * [0] + len(token_ids_a + sep							) * [1]



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	str		,     __a				:	Optional[str] = None							)			->  Tuple[str]:


												"""simple docstring"""

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

												if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							) and os.path.isfile(self.vocab_file							):
																			copyfile(self.vocab_file		,     __a							)
												elif not os.path.isfile(self.vocab_file							):
																			with open(__a		,     """wb"""							) as fi:
																										__lowercase      :							Tuple				=     self.sp_model.serialized_model_proto()
																										fi.write(__a							)

												return (out_vocab_file,)







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						str=False       ,       lowerCAmelCase_       :						Any=False      ):
							__lowercase      :							Any				=     """backbone.""" if is_semantic else """"""

							__lowercase      :							Optional[Any]				=     []
							for i in range(config.num_hidden_layers      ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")      )
														rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")      )
														rename_keys.append(
														    (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")      )
														rename_keys.append(
														    (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")      )
														rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")      )
														rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")      )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")      )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")      )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")      )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")      )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (F"{prefix}cls_token", """beit.embeddings.cls_token"""),
							        (F"{prefix}patch_embed.proj.weight", """beit.embeddings.patch_embeddings.projection.weight"""),
							        (F"{prefix}patch_embed.proj.bias", """beit.embeddings.patch_embeddings.projection.bias"""),
							        (F"{prefix}pos_embed", """beit.embeddings.position_embeddings"""),
							    ]      )

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("""mask_token""", """beit.embeddings.mask_token"""),
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														    ]      )
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
														        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]      )

							return rename_keys
def       snake_case_		(     lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Any=False       ,       lowerCAmelCase_       :						List[Any]=False      ):
							for i in range(config.num_hidden_layers      ):
														__lowercase      :							Tuple				=     """backbone.""" if is_semantic else """"""
														# queries, keys and values
														__lowercase      :							int				=     state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"      )
														__lowercase      :							Dict				=     state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"      )
														__lowercase      :							int				=     state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"      )

														__lowercase      :							List[str]				=     in_proj_weight[
														    : config.hidden_size, :
														]
														__lowercase      :							Union[str, Any]				=     q_bias
														__lowercase      :							Any				=     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowercase      :							Union[str, Any]				=     in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowercase      :							str				=     v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__lowercase      :							int				=     state_dict.pop(F"{prefix}blocks.{i}.gamma_1"      )
														__lowercase      :							str				=     state_dict.pop(F"{prefix}blocks.{i}.gamma_2"      )

														__lowercase      :							List[str]				=     gamma_a
														__lowercase      :							Optional[int]				=     gamma_a
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						int      ):
							__lowercase      :							Tuple				=     dct.pop(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     val
def       snake_case_		(     ):
							__lowercase      :							Optional[int]				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__lowercase      :							Any				=     Image.open(requests.get(lowerCAmelCase_       ,       stream=lowerCAmelCase_      ).raw      )
							return im
@torch.no_grad()
def       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Optional[int]=False      ):
							__lowercase      :							Dict				=     False if """rvlcdip""" in checkpoint_url else True
							__lowercase      :							Tuple				=     BeitConfig(use_absolute_position_embeddings=lowerCAmelCase_       ,       use_mask_token=lowerCAmelCase_      )

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														__lowercase      :							Union[str, Any]				=     1024
														__lowercase      :							Optional[int]				=     4096
														__lowercase      :							List[Any]				=     24
														__lowercase      :							Dict				=     16

							# labels
							if "rvlcdip" in checkpoint_url:
														__lowercase      :							Optional[int]				=     16
														__lowercase      :							Any				=     """huggingface/label-files"""
														__lowercase      :							Union[str, Any]				=     """rvlcdip-id2label.json"""
														__lowercase      :							List[str]				=     json.load(open(hf_hub_download(lowerCAmelCase_       ,       lowerCAmelCase_       ,       repo_type="""dataset"""      )       ,       """r"""      )      )
														__lowercase      :							Optional[int]				=     {int(lowerCAmelCase_      ): v for k, v in idalabel.items()}
														__lowercase      :							Union[str, Any]				=     idalabel
														__lowercase      :							Optional[Any]				=     {v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							__lowercase      :							Optional[int]				=     torch.hub.load_state_dict_from_url(lowerCAmelCase_       ,       map_location="""cpu"""      )["""model"""]

							__lowercase      :							Union[str, Any]				=     create_rename_keys(lowerCAmelCase_       ,       has_lm_head=lowerCAmelCase_      )
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )
							read_in_q_k_v(lowerCAmelCase_       ,       lowerCAmelCase_       ,       has_lm_head=lowerCAmelCase_      )

							# load HuggingFace model
							__lowercase      :							Dict				=     BeitForMaskedImageModeling(lowerCAmelCase_      ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_      )
							model.eval()
							model.load_state_dict(lowerCAmelCase_      )

							# Check outputs on an image
							__lowercase      :							List[str]				=     BeitImageProcessor(
							    size=config.image_size       ,       resample=PILImageResampling.BILINEAR       ,       do_center_crop=lowerCAmelCase_      )
							__lowercase      :							List[str]				=     prepare_img()

							__lowercase      :							Optional[Any]				=     image_processor(images=lowerCAmelCase_       ,       return_tensors="""pt"""      )
							__lowercase      :							Optional[int]				=     encoding["""pixel_values"""]

							__lowercase      :							str				=     model(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     outputs.logits

							# verify logits
							__lowercase      :							str				=     [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
							assert logits.shape == torch.Size(lowerCAmelCase_      ), "Shape of logits not as expected"

							Path(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
							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 push_to_hub:
														if has_lm_head:
																					__lowercase      :							Optional[Any]				=     """dit-base""" if """base""" in checkpoint_url else """dit-large"""
														else:
																					__lowercase      :							Tuple				=     """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
														image_processor.push_to_hub(
														    repo_path_or_name=Path(lowerCAmelCase_       ,       lowerCAmelCase_      )       ,       organization="""nielsr"""       ,       commit_message="""Add image processor"""       ,       use_temp_dir=lowerCAmelCase_       ,       )
														model.push_to_hub(
														    repo_path_or_name=Path(lowerCAmelCase_       ,       lowerCAmelCase_      )       ,       organization="""nielsr"""       ,       commit_message="""Add model"""       ,       use_temp_dir=lowerCAmelCase_       ,       )


if __name__ == "__main__":
	lowerCamelCase : List[str]						   =       argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
	    type=str,
	    help='''URL to the original PyTorch checkpoint (.pth file).''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	)
	lowerCamelCase : List[str]						   =       parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)