




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    				=      logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case=False      ,  snake_case=False							)    ->					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 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case      ,  snake_case=False      ,  snake_case=False							)    ->					Dict:
						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 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case      ,  snake_case							)    ->					Dict:
						_UpperCAmelCase	     =      dct.pop(UpperCamelCase__							)
						_UpperCAmelCase	     =      val




def 							_SCREAMING_SNAKE_CASE   (		)    ->					Any:
						_UpperCAmelCase	     =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCAmelCase	     =      Image.open(requests.get(UpperCamelCase__      ,  stream=UpperCamelCase__							).raw							)
						return im




@torch.no_grad()
def 							_SCREAMING_SNAKE_CASE   (		snake_case      ,  snake_case      ,  snake_case=False							)    ->					str:
						_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	     =      1_0_2_4
												_UpperCAmelCase	     =      4_0_9_6
												_UpperCAmelCase	     =      2_4
												_UpperCAmelCase	     =      1_6

						# labels
						if "rvlcdip" in checkpoint_url:
												_UpperCAmelCase	     =      1_6
												_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, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
						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__":
				a    				=      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    				=      parser.parse_args()
				convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase  ={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

lowerCamelCase  =logging.WARNING


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					os.getenv('''DATASETS_VERBOSITY'''			,		UpperCamelCase__		)
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                      f'''has to be one of: { ", ".join(log_levels.keys()		) }'''		)
      return _default_log_level


def 				SCREAMING_SNAKE_CASE_   (      ):
      return __name__.split('''.'''		)[0]


def 				SCREAMING_SNAKE_CASE_   (      ):
      return logging.getLogger(_get_library_name()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      # Apply our default configuration to the library root logger.
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(_get_default_logging_level()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(logging.NOTSET		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = None		):
      if name is None:
            UpperCamelCase__     : Union[str, Any]											=					_get_library_name()
      return logging.getLogger(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return _get_library_root_logger().getEffectiveLevel()


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      _get_library_root_logger().setLevel(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : str											=					False


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> int:  # pylint: disable=unused-argument




           """simple docstring"""







           UpperCamelCase__     : Dict											=					args[0] if args else None






     def __iter__(				self	)	-> Dict:




           """simple docstring"""







           return iter(self._iterator	)






     def __getattr__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           def empty_fn(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	):  # pylint: disable=unused-argument
                 return

           return empty_fn






     def __enter__(				self	)	-> Optional[int]:




           """simple docstring"""







           return self






     def __exit__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           return
lowerCamelCase  =True




class 					_lowerCamelCase			:





     """simple docstring"""







     def __call__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if _tqdm_active and not disable:
                 return tqdm_lib.tqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           else:
                 return EmptyTqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					None
           if _tqdm_active:
                 return tqdm_lib.tqdm.set_lock(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           if _tqdm_active:
                 return tqdm_lib.tqdm.get_lock()
lowerCamelCase  =_tqdm_cls()


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      return bool(_tqdm_active		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Dict											=					True


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Union[str, Any]											=					False


