'''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()
lowercase__  :      Optional[Any]        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  List[str]=False							,    _UpperCamelCase  :  Tuple=False					):


      '''simple docstring'''


      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       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Dict=False							,    _UpperCamelCase  :  Tuple=False					):


      '''simple docstring'''


      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       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  List[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  dct.pop(_UpperCamelCase					)
      UpperCAmelCase_            =  val



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase_            =  Image.open(requests.get(_UpperCamelCase							,    stream=_UpperCamelCase					).raw					)
      return im



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  int=False					):


      '''simple docstring'''


      UpperCAmelCase_            =  False if '''rvlcdip''' in checkpoint_url else True
      UpperCAmelCase_            =  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:
            UpperCAmelCase_            =  1024
            UpperCAmelCase_            =  4096
            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(_UpperCamelCase							,    _UpperCamelCase							,    repo_type='''dataset'''					)							,    '''r'''					)					)
            UpperCAmelCase_            =  {int(_UpperCamelCase					): 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(_UpperCamelCase							,    map_location='''cpu'''					)['''model''']

      UpperCAmelCase_            =  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
      UpperCAmelCase_            =  BeitForMaskedImageModeling(_UpperCamelCase					) if has_lm_head else BeitForImageClassification(_UpperCamelCase					)
      model.eval()
      model.load_state_dict(_UpperCamelCase					)

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

      UpperCAmelCase_            =  image_processor(images=_UpperCamelCase							,    return_tensors='''pt'''					)
      UpperCAmelCase_            =  encoding['''pixel_values''']

      UpperCAmelCase_            =  model(_UpperCamelCase					)
      UpperCAmelCase_            =  outputs.logits

      # verify logits
      UpperCAmelCase_            =  [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
      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:
                  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(_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__":
      lowercase__  :      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",
      )
      lowercase__  :      int        =					parser.parse_args()
      convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

      return [s[i:] + s[:i] for i in range(len(_UpperCamelCase					)					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

      UpperCAmelCase_            =  all_rotations(_UpperCamelCase					)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      UpperCAmelCase_            =  {
          "bwt_string": "".join([word[-1] for word in rotations]					),
          "idx_original_string": rotations.index(_UpperCamelCase					),
      }
      return response



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter bwt_string type must be str.'''					)
      if not bwt_string:
            raise ValueError('''The parameter bwt_string must not be empty.'''					)
      try:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      except ValueError:
            raise TypeError(
                '''The parameter idx_original_string type must be int or passive'''
                ''' of cast to int.'''					)
      if idx_original_string < 0:
            raise ValueError('''The parameter idx_original_string must not be lower than 0.'''					)
      if idx_original_string >= len(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      Dict        =					reverse_bwt(result["bwt_string"], result["idx_original_string"])
      print(
          F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
          F'''we get original string \'{original_string}\''''
      )





