






'''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()
__lowerCAmelCase       		=      logging.get_logger(__name__)





def 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   str=False						,		__A							:   List[str]=False      ):
				a_		:  str											=      '''backbone.''' if is_semantic else ''''''

				a_		:  Union[str, 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 		_UpperCAmelCase   (							__A							:   int						,		__A							:   Dict						,		__A							:   Optional[int]=False						,		__A							:   Tuple=False      ):
				for i in range(config.num_hidden_layers      ):
								a_		:  List[str]											=      '''backbone.''' if is_semantic else ''''''
								# queries, keys and values
								a_		:  Union[str, Any]											=      state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'      )
								a_		:  Any											=      state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'      )
								a_		:  Any											=      state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'      )

								a_		:  int											=      in_proj_weight[
								    : config.hidden_size, :
								]
								a_		:  Union[str, Any]											=      q_bias
								a_		:  Any											=      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a_		:  Optional[int]											=      in_proj_weight[
								    -config.hidden_size :, :
								]
								a_		:  Dict											=      v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								a_		:  str											=      state_dict.pop(f'{prefix}blocks.{i}.gamma_1'      )
								a_		:  Optional[Any]											=      state_dict.pop(f'{prefix}blocks.{i}.gamma_2'      )

								a_		:  Union[str, Any]											=      gamma_a
								a_		:  int											=      gamma_a





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   Any						,		__A							:   List[Any]      ):
				a_		:  Optional[Any]											=      dct.pop(__A      )
				a_		:  List[str]											=      val





def 		_UpperCAmelCase   (							):
				a_		:  List[str]											=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				a_		:  Tuple											=      Image.open(requests.get(__A						,		stream=__A      ).raw      )
				return im





@torch.no_grad()
def 		_UpperCAmelCase   (							__A							:   Tuple						,		__A							:   Any						,		__A							:   List[Any]=False      ):
				a_		:  Optional[Any]											=      False if '''rvlcdip''' in checkpoint_url else True
				a_		:  Tuple											=      BeitConfig(use_absolute_position_embeddings=__A						,		use_mask_token=__A      )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								a_		:  Dict											=      10_24
								a_		:  int											=      40_96
								a_		:  Tuple											=      24
								a_		:  str											=      16

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

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

				a_		:  Any											=      create_rename_keys(__A						,		has_lm_head=__A      )
				for src, dest in rename_keys:
								rename_key(__A						,		__A						,		__A      )
				read_in_q_k_v(__A						,		__A						,		has_lm_head=__A      )

				# load HuggingFace model
				a_		:  int											=      BeitForMaskedImageModeling(__A      ) if has_lm_head else BeitForImageClassification(__A      )
				model.eval()
				model.load_state_dict(__A      )

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

				a_		:  Union[str, Any]											=      image_processor(images=__A						,		return_tensors='''pt'''      )
				a_		:  List[str]											=      encoding['''pixel_values''']

				a_		:  Optional[int]											=      model(__A      )
				a_		:  str											=      outputs.logits

				# verify logits
				a_		:  Union[str, Any]											=      [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
				assert logits.shape == torch.Size(__A      ), "Shape of logits not as expected"

				Path(__A      ).mkdir(exist_ok=__A      )
				print(f'Saving model to {pytorch_dump_folder_path}'      )
				model.save_pretrained(__A      )
				print(f'Saving image processor to {pytorch_dump_folder_path}'      )
				image_processor.save_pretrained(__A      )

				if push_to_hub:
								if has_lm_head:
												a_		:  str											=      '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
								else:
												a_		:  int											=      '''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(__A						,		__A      )						,		organization='''nielsr'''						,		commit_message='''Add image processor'''						,		use_temp_dir=__A						,		)
								model.push_to_hub(
								    repo_path_or_name=Path(__A						,		__A      )						,		organization='''nielsr'''						,		commit_message='''Add model'''						,		use_temp_dir=__A						,		)


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)












'''simple docstring'''







def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):

				def get_matched_characters(__A							:   str						,		__A							:   str      ) -> str:
								a_		:  Union[str, Any]											=      []
								a_		:  int											=      min(len(_stra      )						,		len(_stra      )      ) // 2
								for i, l in enumerate(_stra      ):
												a_		:  Any											=      int(max(0						,		i - limit      )      )
												a_		:  Union[str, Any]											=      int(min(i + limit + 1						,		len(_stra      )      )      )
												if l in _stra[left:right]:
																matched.append(__A      )
																a_		:  Any											=      f'{_stra[0:_stra.index(__A      )]} {_stra[_stra.index(__A      ) + 1:]}'

								return "".join(__A      )

				# matching characters
				a_		:  Optional[Any]											=      get_matched_characters(__A						,		__A      )
				a_		:  int											=      get_matched_characters(__A						,		__A      )
				a_		:  Any											=      len(__A      )

				# transposition
				a_		:  List[Any]											=      (
				    len([(ca, ca) for ca, ca in zip(__A						,		__A      ) if ca != ca]      ) // 2
				)

				if not match_count:
								a_		:  Dict											=      0.0
				else:
								a_		:  Optional[int]											=      (
								    1
								    / 3
								    * (
								        match_count / len(__A      )
								        + match_count / len(__A      )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				a_		:  List[str]											=      0
				for ca, ca in zip(stra[:4]						,		stra[:4]      ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))




