




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			: List[str]			,							lowercase			: Optional[int]=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			: Any			,							lowercase			: Optional[int]			,							lowercase			: int=False			,							lowercase			: List[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			: List[Any]			,							lowercase			: Union[str, Any]			,							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					dct.pop(lowercase  )
      lowerCamelCase_	  =					val

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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

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


      '''simple docstring'''



      lowerCamelCase_	  =					False if 'rvlcdip' in checkpoint_url else True
      lowerCamelCase_	  =					BeitConfig(use_absolute_position_embeddings=lowercase			,							use_mask_token=lowercase  )

      # 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(lowercase			,							lowercase			,							repo_type='dataset'  )			,							'r'  )  )
            lowerCamelCase_	  =					{int(lowercase  ): 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(lowercase			,							map_location='cpu'  )['model']

      lowerCamelCase_	  =					create_rename_keys(lowercase			,							has_lm_head=lowercase  )
      for src, dest in rename_keys:
            rename_key(lowercase			,							lowercase			,							lowercase  )
      read_in_q_k_v(lowercase			,							lowercase			,							has_lm_head=lowercase  )

      # load HuggingFace model
      lowerCamelCase_	  =					BeitForMaskedImageModeling(lowercase  ) if has_lm_head else BeitForImageClassification(lowercase  )
      model.eval()
      model.load_state_dict(lowercase  )

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

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

      lowerCamelCase_	  =					model(lowercase  )
      lowerCamelCase_	  =					outputs.logits

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

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

      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(lowercase			,							lowercase  )			,							organization='nielsr'			,							commit_message='Add image processor'			,							use_temp_dir=lowercase			,							)
            model.push_to_hub(
                repo_path_or_name=Path(lowercase			,							lowercase  )			,							organization='nielsr'			,							commit_message='Add model'			,							use_temp_dir=lowercase			,							)


if __name__ == "__main__":
 lowerCamelCase       :	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       :	List[Any]									=     parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		None
 UpperCamelCase		     =		None

lowerCamelCase       :	str									=     namedtuple("CoinsDistribResult", "moves excess")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: TreeNode | None  ):


      '''simple docstring'''



      if root is None:
            return 0

      # Validation
      def count_nodes(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

      def count_coins(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_coins(node.left  ) + count_coins(node.right  ) + node.data

      if count_nodes(lowercase  ) != count_coins(lowercase  ):
            raise ValueError('The nodes number should be same as the number of coins'  )

      # Main calculation
      def get_distrib(lowercase			: TreeNode | None  ) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0			,							1  )

            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.left  )
            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.right  )

            lowerCamelCase_	  =					1 - left_distrib_excess
            lowerCamelCase_	  =					1 - right_distrib_excess

            lowerCamelCase_	  =					(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowercase  )
                + abs(lowercase  )
            )
            lowerCamelCase_	  =					node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowercase			,							lowercase  )

      return get_distrib(lowercase  )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



