




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       :	str				  =     logging.get_logger(__name__)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict			,							lowercase			: Optional[Any]=False			,							lowercase			: int=False  ):


      '''simple docstring'''



      lowerCamelCase_	  =					'''backbone.''' if is_semantic else ''''''

      lowerCamelCase_	  =					[]
      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    (							lowercase			: str			,							lowercase			: int			,							lowercase			: Optional[Any]=False			,							lowercase			: str=False  ):


      '''simple docstring'''



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

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

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

            lowerCamelCase_	  =					gamma_a
            lowerCamelCase_	  =					gamma_a

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Dict			,							lowercase			: Tuple  ):


      '''simple docstring'''



      lowerCamelCase_	  =					dct.pop(__UpperCamelCase  )
      lowerCamelCase_	  =					val

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Union[str, Any]			,							lowercase			: str=False  ):


      '''simple docstring'''



      lowerCamelCase_	  =					False if '''rvlcdip''' in checkpoint_url else True
      lowerCamelCase_	  =					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:
            lowerCamelCase_	  =					10_24
            lowerCamelCase_	  =					40_96
            lowerCamelCase_	  =					24
            lowerCamelCase_	  =					16

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

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

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

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

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

      lowerCamelCase_	  =					model(__UpperCamelCase  )
      lowerCamelCase_	  =					outputs.logits

      # verify logits
      lowerCamelCase_	  =					[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
      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:
                  lowerCamelCase_	  =					'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
            else:
                  lowerCamelCase_	  =					'''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__":
 lowerCamelCase       :	List[str]				  =     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       :	str				  =     parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


if __name__ == "__main__":
 print(solution())



