'''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_ : Tuple						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Dict=False ,		__lowerCamelCase			:						str=False				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      "backbone." if is_semantic else ""

      lowercase_     =      []
      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 __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						str=False ,		__lowerCamelCase			:						Optional[Any]=False				)	->							List[str]:
      '''simple docstring'''


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

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

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

            lowercase_     =      gamma_a
            lowercase_     =      gamma_a


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Optional[Any]				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      dct.pop(__lowerCamelCase				)
      lowercase_     =      val


def __a (	)	->							Any:
      '''simple docstring'''


      lowercase_     =      "http://images.cocodataset.org/val2017/000000039769.jpg"
      lowercase_     =      Image.open(requests.get(__lowerCamelCase ,		stream=__lowerCamelCase				).raw				)
      return im


@torch.no_grad()
def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Tuple=False				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      False if "rvlcdip" in checkpoint_url else True
      lowercase_     =      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:
            lowercase_     =      1_024
            lowercase_     =      4_096
            lowercase_     =      24
            lowercase_     =      16

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

      # load state_dict of original model, remove and rename some keys
      lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)["model"]

      lowercase_     =      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
      lowercase_     =      BeitForMaskedImageModeling(__lowerCamelCase				) if has_lm_head else BeitForImageClassification(__lowerCamelCase				)
      model.eval()
      model.load_state_dict(__lowerCamelCase				)

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

      lowercase_     =      image_processor(images=__lowerCamelCase ,		return_tensors="pt"				)
      lowercase_     =      encoding["pixel_values"]

      lowercase_     =      model(__lowerCamelCase				)
      lowercase_     =      outputs.logits

      # verify logits
      lowercase_     =      [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
      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:
                  lowercase_     =      "dit-base" if "base" in checkpoint_url else "dit-large"
            else:
                  lowercase_     =      "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__":
       lowerCAmelCase_ : Optional[Any]						  =      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_ : Any						  =      parser.parse_args()
       convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

def __a (	__lowerCamelCase			:						list[int] ,		__lowerCamelCase			:						int				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      lowercase_     =      [[False] * (required_sum + 1) for _ in range(arr_len + 1				)]

      # for each arr value, a sum of zero(0) can be formed by not taking any element
      # hence True/1
      for i in range(arr_len + 1				):
            lowercase_     =      True

      # sum is not zero and set is empty then false
      for i in range(1 ,		required_sum + 1				):
            lowercase_     =      False

      for i in range(1 ,		arr_len + 1				):
            for j in range(1 ,		required_sum + 1				):
                  if arr[i - 1] > j:
                        lowercase_     =      subset[i - 1][j]
                  if arr[i - 1] <= j:
                        lowercase_     =      subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

      return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()






