
'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Union[str, Any]	=		"markuplm"

						def __init__(			self       ,							a=3_05_22       ,							a=7_68       ,							a=12       ,							a=12       ,							a=30_72       ,							a="gelu"       ,							a=0.1       ,							a=0.1       ,							a=5_12       ,							a=2       ,							a=0.02       ,							a=1e-12       ,							a=0       ,							a=0       ,							a=2       ,							a=2_56       ,							a=10_24       ,							a=2_16       ,							a=10_01       ,							a=32       ,							a=50       ,							a="absolute"       ,							a=True       ,							a=None       ,							**a       ,							)					->      Union[str, Any]:


								'''simple docstring'''

								super().__init__(
								    pad_token_id=a       ,							bos_token_id=a       ,							eos_token_id=a       ,							**a       ,							)
								_UpperCamelCase						    =	vocab_size
								_UpperCamelCase						    =	hidden_size
								_UpperCamelCase						    =	num_hidden_layers
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	hidden_act
								_UpperCamelCase						    =	intermediate_size
								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	type_vocab_size
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	layer_norm_eps
								_UpperCamelCase						    =	position_embedding_type
								_UpperCamelCase						    =	use_cache
								_UpperCamelCase						    =	classifier_dropout
								# additional properties
								_UpperCamelCase						    =	max_depth
								_UpperCamelCase						    =	max_xpath_tag_unit_embeddings
								_UpperCamelCase						    =	max_xpath_subs_unit_embeddings
								_UpperCamelCase						    =	tag_pad_id
								_UpperCamelCase						    =	subs_pad_id
								_UpperCamelCase						    =	xpath_unit_hidden_size





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if "model" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""model."""						,  """""" )
		if "norm1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm1"""						,  """attention.output.LayerNorm""" )
		if "norm2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm2"""						,  """output.LayerNorm""" )
		if "norm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm"""						,  """LayerNorm""" )
		if "transformer" in orig_key:
				_UpperCamelCase						    =	orig_key.split(""".""" )[0].split("""_""" )[-1]
				_UpperCamelCase						    =	orig_key.replace(F'transformer_{layer_num}'						,  F'encoder.layer.{layer_num}' )
		if "mha.attn" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha.attn"""						,  """attention.self""" )
		if "mha" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha"""						,  """attention""" )
		if "W_q" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_q"""						,  """self.query""" )
		if "W_k" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_k"""						,  """self.key""" )
		if "W_v" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_v"""						,  """self.value""" )
		if "ff1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff1"""						,  """intermediate.dense""" )
		if "ff2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff2"""						,  """output.dense""" )
		if "ff" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff"""						,  """output.dense""" )
		if "mlm_class" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm.mlm_class"""						,  """cls.predictions.decoder""" )
		if "mlm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm"""						,  """cls.predictions.transform""" )
		if "cls" not in orig_key:
				_UpperCamelCase						    =	"""yoso.""" + orig_key

		return orig_key


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						_UpperCamelCase						    =	val

		_UpperCamelCase						    =	orig_state_dict["""cls.predictions.decoder.bias"""]
		_UpperCamelCase						    =	torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )["""model_state_dict"""]
		_UpperCamelCase						    =	YosoConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	YosoForMaskedLM(lowerCAmelCase )

		_UpperCamelCase						    =	convert_checkpoint_helper(config.max_position_embeddings						,  lowerCAmelCase )

		print(model.load_state_dict(lowerCAmelCase ) )
		model.eval()
		model.save_pretrained(lowerCAmelCase )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



