




'''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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase=False       ,    _lowerCAmelCase=False    )->     List[Any]:
 __UpperCAmelCase					=   'backbone.' if is_semantic else ''

 __UpperCAmelCase					=   []
 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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=False       ,    _lowerCAmelCase=False    )->     Optional[int]:







 for i in range(config.num_hidden_layers    ):
  __UpperCAmelCase					=   'backbone.' if is_semantic else ''
  # queries, keys and values
  __UpperCAmelCase					=   state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'    )
  __UpperCAmelCase					=   state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'    )
  __UpperCAmelCase					=   state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'    )

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

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

  __UpperCAmelCase					=   gamma_a
  __UpperCAmelCase					=   gamma_a







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     int:
 __UpperCAmelCase					=   dct.pop(__snake_case    )
 __UpperCAmelCase					=   val







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







@torch.no_grad()
def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=False    )->     Union[str, Any]:
 __UpperCAmelCase					=   False if 'rvlcdip' in checkpoint_url else True
 __UpperCAmelCase					=   BeitConfig(use_absolute_position_embeddings=__snake_case       ,    use_mask_token=__snake_case    )

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __UpperCAmelCase					=   10_24
  __UpperCAmelCase					=   40_96
  __UpperCAmelCase					=   24
  __UpperCAmelCase					=   16

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

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

 __UpperCAmelCase					=   create_rename_keys(__snake_case       ,    has_lm_head=__snake_case    )
 for src, dest in rename_keys:
  rename_key(__snake_case       ,    __snake_case       ,    __snake_case    )
 read_in_q_k_v(__snake_case       ,    __snake_case       ,    has_lm_head=__snake_case    )

 # load HuggingFace model
 __UpperCAmelCase					=   BeitForMaskedImageModeling(__snake_case    ) if has_lm_head else BeitForImageClassification(__snake_case    )
 model.eval()
 model.load_state_dict(__snake_case    )

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

 __UpperCAmelCase					=   image_processor(images=__snake_case       ,    return_tensors='pt'    )
 __UpperCAmelCase					=   encoding['pixel_values']

 __UpperCAmelCase					=   model(__snake_case    )
 __UpperCAmelCase					=   outputs.logits

 # verify logits
 __UpperCAmelCase					=   [1, 16] if 'rvlcdip' in checkpoint_url else [1, 1_96, 81_92]
 assert logits.shape == torch.Size(__snake_case    ), "Shape of logits not as expected"

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

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


if __name__ == "__main__":
    _A:					Tuple							=				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)












'''simple docstring'''




from string import ascii_uppercase

_A:					Union[str, Any]							=				{char: i for i, char in enumerate(ascii_uppercase)}
_A:					str							=				dict(enumerate(ascii_uppercase))







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   len(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 while True:
  if x == i:
   __UpperCAmelCase					=   0
  if len(_lowerCAmelCase    ) == len(_lowerCAmelCase    ):
   break
  key += key[i]
  i += 1
 return key







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in message:
  if letter == " ":
   cipher_text += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] - dicta[key_new[i]]) % 26
   i += 1
   cipher_text += dicta[x]
 return cipher_text







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in cipher_text:
  if letter == " ":
   or_txt += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] + dicta[key_new[i]] + 26) % 26
   i += 1
   or_txt += dicta[x]
 return or_txt







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   'THE GERMAN ATTACK'
 __UpperCAmelCase					=   'SECRET'
 __UpperCAmelCase					=   generate_key(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   cipher_text(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print(F'Encrypted Text = {s}'    )
 print(F'Original Text = {original_text(_lowerCAmelCase       ,    _lowerCAmelCase    )}'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





