






"""simple docstring"""







from __future__ import annotations

__a       :	Optional[Any]      						= '#'







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  {}




							def  lowercase		(   self:     Dict				,		__A:     str    ):

												'''simple docstring'''

												a__          =  self._trie
												for char in text:
																	if char not in trie:
																						a__          =  {}
																	a__          =  trie[char]
												a__          =  True




							def  lowercase		(   self:     List[str]				,		__A:     str    ):

												'''simple docstring'''

												a__          =  self._trie
												for char in prefix:
																	if char in trie:
																						a__          =  trie[char]
																	else:
																						return []
												return self._elements(__A    )





							def  lowercase		(   self:     str				,		__A:     dict    ):

												'''simple docstring'''

												a__          =  []
												for c, v in d.items():
																	a__          =  [''' '''] if c == END else [(c + s) for s in self._elements(__A    )]
																	result.extend(__A    )
												return tuple(__A    )

__a       :	Any      						= Trie()
__a       :	str      						= ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  trie.find_word(lowerCamelCase_)
					return tuple(string + word for word in suffixes)





def        SCREAMING_SNAKE_CASE       (      ):
					print(autocomplete_using_trie('''de'''))


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()










"""simple docstring"""







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()
__a       :	int             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  False if '''rvlcdip''' in checkpoint_url else True
					a__          =  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:
										a__          =  1024
										a__          =  4096
										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(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): 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(lowerCamelCase_ ,     map_location='''cpu''')['''model''']

					a__          =  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
					a__          =  BeitForMaskedImageModeling(lowerCamelCase_) if has_lm_head else BeitForImageClassification(lowerCamelCase_)
					model.eval()
					model.load_state_dict(lowerCamelCase_)

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

					a__          =  image_processor(images=lowerCamelCase_ ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']

					a__          =  model(lowerCamelCase_)
					a__          =  outputs.logits

					# verify logits
					a__          =  [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:
															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(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__":
	__a       :	Dict             = 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',
	)
	__a       :	List[Any]             = parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


