



import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   			=   {'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE__   			=   {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE__   			=   {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

SCREAMING_SNAKE_CASE__   			=   '''▁'''


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Tuple      							=     VOCAB_FILES_NAMES
					A__					:		Union[str, Any]      							=     PRETRAINED_VOCAB_FILES_MAP
					A__					:		Any      							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__					:		List[str]      							=     ["input_ids", "attention_mask"]



					def __init__(  self       :							Any					,       _snake_case       :							int					,       _snake_case       :							Optional[Any]="</s>"					,       _snake_case       :							Any="<unk>"					,       _snake_case       :							Optional[int]="<pad>"					,       _snake_case       :							List[Any]=1_00					,       _snake_case       :							str=None					,       _snake_case       :							Optional[Dict[str, Any]] = None					,       _snake_case       :							Tuple=True					,       **_snake_case       :							List[str]					,       ):






								"""simple docstring"""


								if extra_ids > 0 and additional_special_tokens is None:
											A__	  =     [F'''<extra_id_{i}>''' for i in range(_snake_case       )]
								elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											A__	  =     len(set(filter(lambda _snake_case       : bool('extra_id' in str(_snake_case       )       )					,       _snake_case       )       )       )
											if extra_tokens != extra_ids:
														raise ValueError(
														    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
														    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
														    ' tokens'       )
								if legacy:
											logger.warning_once(
											    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
											    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'       )

								A__	  =     legacy
								A__	  =     {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=_snake_case					,       unk_token=_snake_case					,       pad_token=_snake_case					,       extra_ids=_snake_case					,       additional_special_tokens=_snake_case					,       sp_model_kwargs=self.sp_model_kwargs					,       legacy=_snake_case					,       **_snake_case					,       )

								A__	  =     vocab_file
								A__	  =     extra_ids

								A__	  =     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(_snake_case       )



					@staticmethod
					def      _a				(  _snake_case       :							Union[str, Any]					,       _snake_case       :							int					,       _snake_case       :							Dict       ):






								"""simple docstring"""


								if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
											A__	  =     TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
														return init_max_model_length
											elif init_max_model_length is None:
														warnings.warn(
														    'This tokenizer was incorrectly instantiated with a model max length of'
														    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
														    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
														    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
														    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
														    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
														    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
														    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
														    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'					,       _snake_case					,       )

								return max_model_length



					@property
					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								return self.sp_model.get_piece_size() + self._extra_ids



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     {self.convert_ids_to_tokens(_snake_case       ): i for i in range(self.vocab_size       )}
								vocab.update(self.added_tokens_encoder       )
								return vocab



					def      _a				(  self       :							int					,       _snake_case       :							List[int]					,       _snake_case       :							Optional[List[int]] = None					,       _snake_case       :							bool = False       ):






								"""simple docstring"""


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

								# normal case: some special tokens
								if token_ids_a is None:
											return ([0] * len(_snake_case       )) + [1]
								return ([0] * len(_snake_case       )) + [1] + ([0] * len(_snake_case       )) + [1]



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								return list(
								    set(filter(lambda _snake_case       : bool(re.search(R'<extra_id_\d+>'					,       _snake_case       )       ) is not None					,       self.additional_special_tokens       )       )       )



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								return [self._convert_token_to_id(_snake_case       ) for token in self.get_sentinel_tokens()]



					def      _a				(  self       :							List[Any]					,       _snake_case       :							List[int]       ):






								"""simple docstring"""


								if len(_snake_case       ) > 0 and token_ids[-1] == self.eos_token_id:
											warnings.warn(
											    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
											    ' eos tokens being added.'       )
											return token_ids
								else:
											return token_ids + [self.eos_token_id]



					def      _a				(  self       :							Dict					,       _snake_case       :							List[int]					,       _snake_case       :							Optional[List[int]] = None       ):






								"""simple docstring"""


								A__	  =     [self.eos_token_id]

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



					def      _a				(  self       :							Dict					,       _snake_case       :							List[int]					,       _snake_case       :							Optional[List[int]] = None       ):






								"""simple docstring"""


								A__	  =     self._add_eos_if_not_present(_snake_case       )
								if token_ids_a is None:
											return token_ids_a
								else:
											A__	  =     self._add_eos_if_not_present(_snake_case       )
											return token_ids_a + token_ids_a



					def __getstate__(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.__dict__.copy()
								A__	  =     None
								return state



					def __setstate__(  self       :							Optional[Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     d

								# for backward compatibility
								if not hasattr(self					,       'sp_model_kwargs'       ):
											A__	  =     {}

								A__	  =     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(self.vocab_file       )



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							"TextInput"					,       **_snake_case       :							Tuple       ):






								"""simple docstring"""


								if not self.legacy:
											A__	  =     SPIECE_UNDERLINE + text.replace(_snake_case					,       ' '       )
								return super().tokenize(_snake_case					,       **_snake_case       )



					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							Tuple					,       **_snake_case       :							int       ):






								"""simple docstring"""


								if not self.legacy:
											A__	  =     text.startswith(_snake_case       )
											if is_first:
														A__	  =     text[1:]

								A__	  =     self.sp_model.encode(_snake_case					,       out_type=_snake_case       )

								if not self.legacy and not is_first and not text.startswith(' '       ) and tokens[0].startswith(_snake_case       ):
											A__	  =     ([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
								return tokens



					def      _a				(  self       :							Tuple					,       _snake_case       :							Optional[int]       ):






								"""simple docstring"""


								if token.startswith('<extra_id_'       ):
											A__	  =     re.match(R'<extra_id_(\d+)>'					,       _snake_case       )
											A__	  =     int(match.group(1       )       )
											return self.vocab_size - num - 1
								return self.sp_model.piece_to_id(_snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								if index < self.sp_model.get_piece_size():
											A__	  =     self.sp_model.IdToPiece(_snake_case       )
								else:
											A__	  =     F'''<extra_id_{self.vocab_size - 1 - index}>'''
								return token



					def      _a				(  self       :							Any					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     []
								A__	  =     ''
								A__	  =     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(_snake_case       ) + token
														A__	  =     True
														A__	  =     []
											else:
														current_sub_tokens.append(_snake_case       )
														A__	  =     False
								out_string += self.sp_model.decode(_snake_case       )
								return out_string.strip()



					def      _a				(  self       :							List[str]					,       _snake_case       :							str					,       _snake_case       :							Optional[str] = None       ):






								"""simple docstring"""


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

								if os.path.abspath(self.vocab_file       ) != os.path.abspath(_snake_case       ) and os.path.isfile(self.vocab_file       ):
											copyfile(self.vocab_file					,       _snake_case       )
								elif not os.path.isfile(self.vocab_file       ):
											with open(_snake_case					,       'wb'       ) as fi:
														A__	  =     self.sp_model.serialized_model_proto()
														fi.write(_snake_case       )

								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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Dict:
			A__	  =     'backbone.' if is_semantic else ''

			A__	  =     []
			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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Optional[Any]:
			for i in range(config.num_hidden_layers       ):
						A__	  =     'backbone.' if is_semantic else ''
						# queries, keys and values
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''       )

						A__	  =     in_proj_weight[
						    : config.hidden_size, :
						]
						A__	  =     q_bias
						A__	  =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__	  =     in_proj_weight[
						    -config.hidden_size :, :
						]
						A__	  =     v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''       )

						A__	  =     gamma_a
						A__	  =     gamma_a


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							)    ->   Dict:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False       )    ->   str:
			A__	  =     False if 'rvlcdip' in checkpoint_url else True
			A__	  =     BeitConfig(use_absolute_position_embeddings=__UpperCamelCase			,						use_mask_token=__UpperCamelCase       )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						A__	  =     1_024
						A__	  =     4_096
						A__	  =     24
						A__	  =     16

			# labels
			if "rvlcdip" in checkpoint_url:
						A__	  =     16
						A__	  =     'huggingface/label-files'
						A__	  =     'rvlcdip-id2label.json'
						A__	  =     json.load(open(hf_hub_download(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
						A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
						A__	  =     idalabel
						A__	  =     {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			A__	  =     torch.hub.load_state_dict_from_url(__UpperCamelCase			,						map_location='cpu'       )['model']

			A__	  =     create_rename_keys(__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			read_in_q_k_v(__UpperCamelCase			,						__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )

			# load HuggingFace model
			A__	  =     BeitForMaskedImageModeling(__UpperCamelCase       ) if has_lm_head else BeitForImageClassification(__UpperCamelCase       )
			model.eval()
			model.load_state_dict(__UpperCamelCase       )

			# Check outputs on an image
			A__	  =     BeitImageProcessor(
			    size=config.image_size			,						resample=PILImageResampling.BILINEAR			,						do_center_crop=__UpperCamelCase       )
			A__	  =     prepare_img()

			A__	  =     image_processor(images=__UpperCamelCase			,						return_tensors='pt'       )
			A__	  =     encoding['pixel_values']

			A__	  =     model(__UpperCamelCase       )
			A__	  =     outputs.logits

			# verify logits
			A__	  =     [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
			assert logits.shape == torch.Size(__UpperCamelCase       ), "Shape of logits not as expected"

			Path(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
			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 push_to_hub:
						if has_lm_head:
									A__	  =     'dit-base' if 'base' in checkpoint_url else 'dit-large'
						else:
									A__	  =     '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(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add image processor'			,						use_temp_dir=__UpperCamelCase			,						)
						model.push_to_hub(
						    repo_path_or_name=Path(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add model'			,						use_temp_dir=__UpperCamelCase			,						)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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''',
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




