


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


snake_case				:    str       				=     logging.get_logger(__name__)

snake_case				:    Optional[int]       				=     {
    '''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 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'markuplm'




 def __init__(		self					,  _lowerCamelCase=3_0522					,  _lowerCamelCase=768					,  _lowerCamelCase=12					,  _lowerCamelCase=12					,  _lowerCamelCase=3072					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=512					,  _lowerCamelCase=2					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-12					,  _lowerCamelCase=0					,  _lowerCamelCase=0					,  _lowerCamelCase=2					,  _lowerCamelCase=256					,  _lowerCamelCase=1024					,  _lowerCamelCase=216					,  _lowerCamelCase=1001					,  _lowerCamelCase=32					,  _lowerCamelCase=50					,  _lowerCamelCase="absolute"					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  **_lowerCamelCase					,  ):
       super().__init__(
           pad_token_id=_lowerCamelCase					,  bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase					,  **_lowerCamelCase					,  )
       a						:Optional[Any] 							=       vocab_size
       a						:int 							=       hidden_size
       a						:List[Any] 							=       num_hidden_layers
       a						:str 							=       num_attention_heads
       a						:Tuple 							=       hidden_act
       a						:Any 							=       intermediate_size
       a						:Optional[int] 							=       hidden_dropout_prob
       a						:Optional[Any] 							=       attention_probs_dropout_prob
       a						:Any 							=       max_position_embeddings
       a						:Union[str, Any] 							=       type_vocab_size
       a						:Optional[int] 							=       initializer_range
       a						:Any 							=       layer_norm_eps
       a						:Any 							=       position_embedding_type
       a						:Optional[Any] 							=       use_cache
       a						:Optional[Any] 							=       classifier_dropout
       # additional properties
       a						:Optional[int] 							=       max_depth
       a						:int 							=       max_xpath_tag_unit_embeddings
       a						:Optional[Any] 							=       max_xpath_subs_unit_embeddings
       a						:Union[str, Any] 							=       tag_pad_id
       a						:str 							=       subs_pad_id
       a						:List[str] 							=       xpath_unit_hidden_size












"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  A_  :       Tuple										=  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)