






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_           = logging.get_logger(__name__)







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       List[str]=False,						UpperCAmelCase	:       List[str]=False   )     ->   Any:
				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      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Dict=False,						UpperCAmelCase	:       Any=False   )     ->   int:
				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      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Tuple   )     ->   int:
				A	 =      dct.pop(UpperCAmelCase   )
				A	 =      val







def      snake_case		(							)     ->   List[Any]:
				A	 =      'http://images.cocodataset.org/val2017/000000039769.jpg'
				A	 =      Image.open(requests.get(UpperCAmelCase,						stream=UpperCAmelCase   ).raw   )
				return im







@torch.no_grad()
def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Tuple=False   )     ->   List[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	 =      10_24
								A	 =      40_96
								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, 1_96, 81_92]
				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__":
					lowerCAmelCase_           = 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_           = parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)













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

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


if is_sentencepiece_available():
					from .tokenization_albert import AlbertTokenizer
else:
					lowerCAmelCase_           = None

lowerCAmelCase_           = logging.get_logger(__name__)
lowerCAmelCase_           = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    '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',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

lowerCAmelCase_           = '▁'
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   AlbertTokenizer
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       int=None						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       List[str]=True						,_SCREAMING_SNAKE_CASE		:       str=True						,_SCREAMING_SNAKE_CASE		:       Optional[int]=False						,_SCREAMING_SNAKE_CASE		:       List[str]="[CLS]"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="[SEP]"						,_SCREAMING_SNAKE_CASE		:       Tuple="<unk>"						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]="[SEP]"						,_SCREAMING_SNAKE_CASE		:       Tuple="<pad>"						,_SCREAMING_SNAKE_CASE		:       Any="[CLS]"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="[MASK]"						,**_SCREAMING_SNAKE_CASE		:       Any						,)    ->   Tuple:


						'''simple docstring'''
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						A	 =      (
						    AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE						,normalized=_SCREAMING_SNAKE_CASE		)
						    if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						    else mask_token
						)

						super().__init__(
						    _SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,do_lower_case=_SCREAMING_SNAKE_CASE						,remove_space=_SCREAMING_SNAKE_CASE						,keep_accents=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,sep_token=_SCREAMING_SNAKE_CASE						,pad_token=_SCREAMING_SNAKE_CASE						,cls_token=_SCREAMING_SNAKE_CASE						,mask_token=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      do_lower_case
						A	 =      remove_space
						A	 =      keep_accents
						A	 =      vocab_file
						A	 =      False if not self.vocab_file else True
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       List[int]						,_SCREAMING_SNAKE_CASE		:       Optional[List[int]] = None		)    ->   List[int]:


						'''simple docstring'''
						A	 =      [self.sep_token_id]
						A	 =      [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       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[int]						,_SCREAMING_SNAKE_CASE		:       Optional[List[int]] = None		)    ->   List[int]:


						'''simple docstring'''
						A	 =      [self.sep_token_id]
						A	 =      [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       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						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(_SCREAMING_SNAKE_CASE		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
						A	 =      os.path.join(
						    _SCREAMING_SNAKE_CASE						,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

						return (out_vocab_file,)





