





'''simple docstring'''


import logging
import os
import threading
import time


try:
       import warnings
except ImportError:
       _SCREAMING_SNAKE_CASE									=				None

try:
       import msvcrt
except ImportError:
       _SCREAMING_SNAKE_CASE									=				None

try:
       import fcntl
except ImportError:
       _SCREAMING_SNAKE_CASE									=				None


# Backward compatibility
# ------------------------------------------------
try:
       TimeoutError
except NameError:
       _SCREAMING_SNAKE_CASE									=				OSError


# Data
# ------------------------------------------------
_SCREAMING_SNAKE_CASE									=				[
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

_SCREAMING_SNAKE_CASE									=				"3.0.12"


_SCREAMING_SNAKE_CASE									=				None


def 						__a():



    '''simple docstring'''





    global _logger
    _lowerCAmelCase						      =   _logger or logging.getLogger(__name__     )
    return _logger

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   lock_file
     return None





 def __str__(						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   f'''The file lock \'{self.lock_file}\' could not be acquired.'''
     return temp







class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   lock
     return None




 def __enter__(						self		)     ->				Union[str, Any]:
     return self.lock





 def __exit__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     self.lock.release()
     return None







class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=-1      ,  _lowerCAmelCase=None		)     ->				List[str]:
     _lowerCAmelCase						      =   max_filename_length if max_filename_length is not None else 255
     # Hash the filename if it's too long
     _lowerCAmelCase						      =   self.hash_filename_if_too_long(_lowerCAmelCase      ,  _lowerCAmelCase		)
     # The path to the lock file.
     _lowerCAmelCase						      =   lock_file

     # The file descriptor for the *_lock_file* as it is returned by the
     # os.open() function.
     # This file lock is only NOT None, if the object currently holds the
     # lock.
     _lowerCAmelCase						      =   None

     # The default timeout value.
     _lowerCAmelCase						      =   timeout

     # We use this lock primarily for the lock counter.
     _lowerCAmelCase						      =   threading.Lock()

     # The lock counter is used for implementing the nested locking
     # mechanism. Whenever the lock is acquired, the counter is increased and
     # the lock is only released, when this value is 0 again.
     _lowerCAmelCase						      =   0
     return None




 @property
 def 				_snake_case (						self		)     ->				Tuple:
     return self._lock_file




 @property
 def 				_snake_case (						self		)     ->				List[Any]:
     return self._timeout




 @timeout.setter
 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   float(_lowerCAmelCase		)
     return None




 def 				_snake_case (						self		)     ->				Dict:
     raise NotImplementedError()




 def 				_snake_case (						self		)     ->				Dict:
     raise NotImplementedError()




 @property
 def 				_snake_case (						self		)     ->				List[str]:
     return self._lock_file_fd is not None




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=0.05		)     ->				int:
     # Use the default timeout, if no timeout is provided.
     if timeout is None:
         _lowerCAmelCase						      =   self.timeout

     # Increment the number right at the beginning.
     # We can still undo it, if something fails.
     with self._thread_lock:
         self._lock_counter += 1

     _lowerCAmelCase						      =   id(self		)
     _lowerCAmelCase						      =   self._lock_file
     _lowerCAmelCase						      =   time.time()
     try:
         while True:
             with self._thread_lock:
                 if not self.is_locked:
                     logger().debug(f'''Attempting to acquire lock {lock_id} on {lock_filename}'''		)
                     self._acquire()

             if self.is_locked:
                 logger().debug(f'''Lock {lock_id} acquired on {lock_filename}'''		)
                 break
             elif timeout >= 0 and time.time() - start_time > timeout:
                 logger().debug(f'''Timeout on acquiring lock {lock_id} on {lock_filename}'''		)
                 raise Timeout(self._lock_file		)
             else:
                 logger().debug(
                     f'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'''		)
                 time.sleep(_lowerCAmelCase		)
     except:  # noqa
         # Something did go wrong, so decrement the counter.
         with self._thread_lock:
             _lowerCAmelCase						      =   max(0      ,  self._lock_counter - 1		)

         raise
     return _Acquire_ReturnProxy(lock=self		)




 def 				_snake_case (						self      ,  _lowerCAmelCase=False		)     ->				int:
     with self._thread_lock:
         if self.is_locked:
             self._lock_counter -= 1

             if self._lock_counter == 0 or force:
                 _lowerCAmelCase						      =   id(self		)
                 _lowerCAmelCase						      =   self._lock_file

                 logger().debug(f'''Attempting to release lock {lock_id} on {lock_filename}'''		)
                 self._release()
                 _lowerCAmelCase						      =   0
                 logger().debug(f'''Lock {lock_id} released on {lock_filename}'''		)

     return None




 def __enter__(						self		)     ->				List[str]:
     self.acquire()
     return self




 def __exit__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     self.release()
     return None




 def __del__(						self		)     ->				int:
     self.release(force=_lowerCAmelCase		)
     return None





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   os.path.basename(_lowerCAmelCase		)
     if len(_lowerCAmelCase		) > max_length and max_length > 0:
         _lowerCAmelCase						      =   os.path.dirname(_lowerCAmelCase		)
         _lowerCAmelCase						      =   str(hash(_lowerCAmelCase		)		)
         _lowerCAmelCase						      =   filename[: max_length - len(_lowerCAmelCase		) - 8] + "..." + hashed_filename + ".lock"
         return os.path.join(_lowerCAmelCase      ,  _lowerCAmelCase		)
     else:
         return path







class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=-1      ,  _lowerCAmelCase=None		)     ->				int:
     from .file_utils import relative_to_absolute_path

     super().__init__(_lowerCAmelCase      ,  timeout=_lowerCAmelCase      ,  max_filename_length=_lowerCAmelCase		)
     _lowerCAmelCase						      =   "\\\\?\\" + relative_to_absolute_path(self.lock_file		)




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   os.O_RDWR | os.O_CREAT | os.O_TRUNC

     try:
         _lowerCAmelCase						      =   os.open(self._lock_file      ,  _lowerCAmelCase		)
     except OSError:
         pass
     else:
         try:
             msvcrt.locking(_lowerCAmelCase      ,  msvcrt.LK_NBLCK      ,  1		)
         except OSError:
             os.close(_lowerCAmelCase		)
         else:
             _lowerCAmelCase						      =   fd
     return None





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self._lock_file_fd
     _lowerCAmelCase						      =   None
     msvcrt.locking(_lowerCAmelCase      ,  msvcrt.LK_UNLCK      ,  1		)
     os.close(_lowerCAmelCase		)

     try:
         os.remove(self._lock_file		)
     # Probably another instance of the application
     # that acquired the file lock.
     except OSError:
         pass
     return None







class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=-1      ,  _lowerCAmelCase=None		)     ->				Any:
     _lowerCAmelCase						      =   os.statvfs(os.path.dirname(_lowerCAmelCase		)		).f_namemax
     super().__init__(_lowerCAmelCase      ,  timeout=_lowerCAmelCase      ,  max_filename_length=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   os.O_RDWR | os.O_CREAT | os.O_TRUNC
     _lowerCAmelCase						      =   os.open(self._lock_file      ,  _lowerCAmelCase		)

     try:
         fcntl.flock(_lowerCAmelCase      ,  fcntl.LOCK_EX | fcntl.LOCK_NB		)
     except OSError:
         os.close(_lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   fd
     return None





 def 				_snake_case (						self		)     ->				int:
     # Do not remove the lockfile:
     #
     #   https://github.com/benediktschmitt/py-filelock/issues/31
     #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
     _lowerCAmelCase						      =   self._lock_file_fd
     _lowerCAmelCase						      =   None
     fcntl.flock(_lowerCAmelCase      ,  fcntl.LOCK_UN		)
     os.close(_lowerCAmelCase		)
     return None







class  lowerCAmelCase_		(		__magic_name__		):




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
     try:
         _lowerCAmelCase						      =   os.open(self._lock_file      ,  _lowerCAmelCase		)
     except OSError:
         pass
     else:
         _lowerCAmelCase						      =   fd
     return None





 def 				_snake_case (						self		)     ->				str:
     os.close(self._lock_file_fd		)
     _lowerCAmelCase						      =   None

     try:
         os.remove(self._lock_file		)
     # The file is already deleted and that's what we want.
     except OSError:
         pass
     return None







_SCREAMING_SNAKE_CASE									=				None

if msvcrt:
       _SCREAMING_SNAKE_CASE									=				WindowsFileLock
elif fcntl:
       _SCREAMING_SNAKE_CASE									=				UnixFileLock
else:
       _SCREAMING_SNAKE_CASE									=				SoftFileLock

       if warnings is not None:
              warnings.warn("only soft file lock is available")










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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Any=False			,					snake_case       : Any=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""

     UpperCAmelCase__						:    Any							=							[]
     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       : str			,					snake_case       : List[str]			,					snake_case       : List[Any]=False			,					snake_case       : int=False				)->		Any:






     '''simple docstring'''



     for i in range(config.num_hidden_layers				):
          UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__						:    List[str]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'				)
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'				)
          UpperCAmelCase__						:    Optional[int]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'				)

          UpperCAmelCase__						:    List[Any]							=							in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__						:    Dict							=							q_bias
          UpperCAmelCase__						:    Optional[int]							=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__						:    Any							=							in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__						:    int							=							v_bias

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

          UpperCAmelCase__						:    Dict							=							gamma_a
          UpperCAmelCase__						:    Tuple							=							gamma_a


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Dict			,					snake_case       : Tuple				)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							dct.pop(snake_case				)
     UpperCAmelCase__						:    Tuple							=							val


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__						:    List[str]							=							Image.open(requests.get(snake_case			,					stream=snake_case				).raw				)
     return im


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : str			,					snake_case       : Optional[Any]=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							False if "rvlcdip" in checkpoint_url else True
     UpperCAmelCase__						:    int							=							BeitConfig(use_absolute_position_embeddings=snake_case			,					use_mask_token=snake_case				)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCAmelCase__						:    Optional[int]							=							1024
          UpperCAmelCase__						:    Optional[Any]							=							4096
          UpperCAmelCase__						:    List[str]							=							24
          UpperCAmelCase__						:    List[str]							=							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase__						:    List[Any]							=							16
          UpperCAmelCase__						:    Optional[int]							=							"huggingface/label-files"
          UpperCAmelCase__						:    List[Any]							=							"rvlcdip-id2label.json"
          UpperCAmelCase__						:    List[str]							=							json.load(open(hf_hub_download(snake_case			,					snake_case			,					repo_type="dataset"				)			,					"r"				)				)
          UpperCAmelCase__						:    Dict							=							{int(snake_case				): v for k, v in idalabel.items()}
          UpperCAmelCase__						:    List[Any]							=							idalabel
          UpperCAmelCase__						:    Optional[Any]							=							{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__						:    List[Any]							=							torch.hub.load_state_dict_from_url(snake_case			,					map_location="cpu"				)["model"]

     UpperCAmelCase__						:    str							=							create_rename_keys(snake_case			,					has_lm_head=snake_case				)
     for src, dest in rename_keys:
          rename_key(snake_case			,					snake_case			,					snake_case				)
     read_in_q_k_v(snake_case			,					snake_case			,					has_lm_head=snake_case				)

     # load HuggingFace model
     UpperCAmelCase__						:    int							=							BeitForMaskedImageModeling(snake_case				) if has_lm_head else BeitForImageClassification(snake_case				)
     model.eval()
     model.load_state_dict(snake_case				)

     # Check outputs on an image
     UpperCAmelCase__						:    List[Any]							=							BeitImageProcessor(
         size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case				)
     UpperCAmelCase__						:    Any							=							prepare_img()

     UpperCAmelCase__						:    Any							=							image_processor(images=snake_case			,					return_tensors="pt"				)
     UpperCAmelCase__						:    List[str]							=							encoding["pixel_values"]

     UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							outputs.logits

     # verify logits
     UpperCAmelCase__						:    Union[str, Any]							=							[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(snake_case				), "Shape of logits not as expected"

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

     if push_to_hub:
          if has_lm_head:
               UpperCAmelCase__						:    Any							=							"dit-base" if "base" in checkpoint_url else "dit-large"
          else:
               UpperCAmelCase__						:    int							=							"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(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add image processor"			,					use_temp_dir=snake_case			,					)
          model.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add model"			,					use_temp_dir=snake_case			,					)


if __name__ == "__main__":
 _lowerCAmelCase	:	Tuple				=      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)


