






"""simple docstring"""







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

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


__a       :	Optional[int]             = threading.Lock()
__a       :	Optional[logging.Handler]             = None

__a       :	Optional[Any]             = {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

__a       :	Tuple             = logging.WARNING

__a       :	Optional[Any]             = True





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  os.getenv('''TRANSFORMERS_VERBOSITY''' ,     lowerCamelCase_)
					if env_level_str:
										if env_level_str in log_levels:
															return log_levels[env_level_str]
										else:
															logging.getLogger().warning(
															    f'Unknown option TRANSFORMERS_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       (      ):
					global _default_handler

					with _lock:
										if _default_handler:
															# This library has already configured the library root logger.
															return
										a__          =  logging.StreamHandler()  # Set sys.stderr as stream.
										a__          =  sys.stderr.flush

										# Apply our default configuration to the library root logger.
										a__          =  _get_library_root_logger()
										library_root_logger.addHandler(_default_handler)
										library_root_logger.setLevel(_get_default_logging_level())
										a__          =  False





def        SCREAMING_SNAKE_CASE       (      ):
					global _default_handler

					with _lock:
										if not _default_handler:
															return

										a__          =  _get_library_root_logger()
										library_root_logger.removeHandler(_default_handler)
										library_root_logger.setLevel(logging.NOTSET)
										a__          =  None





def        SCREAMING_SNAKE_CASE       (      ):
					return log_levels





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ = None):

					if name is None:
										a__          =  _get_library_name()

					_configure_library_root_logger()
					return logging.getLogger(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					_configure_library_root_logger()
					return _get_library_root_logger().getEffectiveLevel()





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					_configure_library_root_logger()
					_get_library_root_logger().setLevel(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					return set_verbosity(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					return set_verbosity(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					return set_verbosity(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					return set_verbosity(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().removeHandler(_default_handler)





def        SCREAMING_SNAKE_CASE       (      ):
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().addHandler(_default_handler)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					_configure_library_root_logger()

					assert handler is not None
					_get_library_root_logger().addHandler(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					_configure_library_root_logger()

					assert handler is not None and handler not in _get_library_root_logger().handlers
					_get_library_root_logger().removeHandler(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					_configure_library_root_logger()
					a__          =  False





def        SCREAMING_SNAKE_CASE       (      ):
					_configure_library_root_logger()
					a__          =  True





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  _get_library_root_logger().handlers

					for handler in handlers:
										a__          =  logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s''')
										handler.setFormatter(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  _get_library_root_logger().handlers

					for handler in handlers:
										handler.setFormatter(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      self ,     *lowerCamelCase_ ,     **lowerCamelCase_):
					a__          =  os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS''' ,     lowerCamelCase_)
					if no_advisory_warnings:
										return
					self.warning(*lowerCamelCase_ ,     **lowerCamelCase_)


__a       :	int             = warning_advice





@functools.lru_cache(lowerCamelCase_)
def        SCREAMING_SNAKE_CASE       (      self ,     *lowerCamelCase_ ,     **lowerCamelCase_):
					self.warning(*lowerCamelCase_ ,     **lowerCamelCase_)


__a       :	List[Any]             = warning_once







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     int    ):  # pylint: disable=unused-argument

												'''simple docstring'''

												a__          =  args[0] if args else None




							def __iter__(   self:     int    ):

												'''simple docstring'''

												return iter(self._iterator    )




							def __getattr__(   self:     List[str]				,		__A:     Dict    ):

												'''simple docstring'''

												def empty_fn(*__A:     str				,		**__A:     Any    ):  # pylint: disable=unused-argument
																	return

												return empty_fn




							def __enter__(   self:     Optional[int]    ):

												'''simple docstring'''

												return self





							def __exit__(   self:     Any				,		__A:     List[Any]				,		__A:     str				,		__A:     Tuple    ):

												'''simple docstring'''

												return







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __call__(   self:     List[str]				,		*__A:     Any				,		**__A:     Optional[int]    ):

												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm(*__A				,		**__A    )
												else:
																	return EmptyTqdm(*__A				,		**__A    )




							def  lowercase		(   self:     Tuple				,		*__A:     List[Any]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  None
												if _tqdm_active:
																	return tqdm_lib.tqdm.set_lock(*__A				,		**__A    )





							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm.get_lock()

__a       :	Optional[int]             = _tqdm_cls()





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





def        SCREAMING_SNAKE_CASE       (      ):
					global _tqdm_active
					a__          =  True
					hf_hub_utils.enable_progress_bars()





def        SCREAMING_SNAKE_CASE       (      ):
					global _tqdm_active
					a__          =  False
					hf_hub_utils.disable_progress_bars()










"""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()
__a       :	int             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					a__          =  '''backbone.''' if is_semantic else ''''''

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

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

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

										a__          =  gamma_a
										a__          =  gamma_a





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





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





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  False if '''rvlcdip''' in checkpoint_url else True
					a__          =  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:
										a__          =  1024
										a__          =  4096
										a__          =  24
										a__          =  16

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

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

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

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

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

					a__          =  model(lowerCamelCase_)
					a__          =  outputs.logits

					# verify logits
					a__          =  [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
					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:
															a__          =  '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
										else:
															a__          =  '''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__":
	__a       :	Dict             = 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       :	List[Any]             = parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


