


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


__UpperCamelCase       :       Union[str, Any]						       =				logging.get_logger(__name__)

def 			_a  (				SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   r'''\w+[.]\d+'''
 UpperCamelCase__					:    List[Any]          =   re.findall(__lowerCAmelCase    , __lowerCAmelCase     )
 for pat in pats:
  UpperCamelCase__					:    Tuple          =   key.replace(__lowerCAmelCase    , '''_'''.join(pat.split('''.'''     )     )     )
 return key

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   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)
 ):
  UpperCamelCase__					:    int          =   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:
  UpperCamelCase__					:    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:
  UpperCamelCase__					:    str          =   pt_tuple_key[:-1] + ('''embedding''',)
  return renamed_pt_tuple_key, pt_tensor

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

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

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

 # old PyTorch layer norm bias
 UpperCamelCase__					:    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 			_a  (				SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Tuple=42     ):







 """simple docstring"""



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

 # Step 2: Since the model is stateless, get random Flax params
 UpperCamelCase__					:    Tuple          =   flax_model.init_weights(PRNGKey(__lowerCAmelCase     )     )

 UpperCamelCase__					:    List[Any]          =   flatten_dict(__lowerCAmelCase     )
 UpperCamelCase__					:    Tuple          =   {}

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

  # Correctly rename weight parameters
  UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   rename_key_and_reshape_tensor(__lowerCAmelCase    , __lowerCAmelCase    , __lowerCAmelCase     )

  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
  UpperCamelCase__					:    Tuple          =   jnp.asarray(__lowerCAmelCase     )

 return unflatten_dict(__lowerCAmelCase     )






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


__UpperCamelCase       :       Optional[int]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       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       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      "markuplm"


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     Optional[int]=30522							,	lowerCamelCase__							:     Dict=768							,	lowerCamelCase__							:     List[str]=12							,	lowerCamelCase__							:     List[Any]=12							,	lowerCamelCase__							:     Optional[Any]=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Union[str, Any]=0.1							,	lowerCamelCase__							:     Optional[int]=0.1							,	lowerCamelCase__							:     Tuple=512							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     Union[str, Any]=0.02							,	lowerCamelCase__							:     Optional[Any]=1E-1_2							,	lowerCamelCase__							:     List[str]=0							,	lowerCamelCase__							:     Tuple=0							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     List[Any]=256							,	lowerCamelCase__							:     List[str]=1024							,	lowerCamelCase__							:     Optional[int]=216							,	lowerCamelCase__							:     Any=1001							,	lowerCamelCase__							:     int=32							,	lowerCamelCase__							:     Tuple=50							,	lowerCamelCase__							:     str="absolute"							,	lowerCamelCase__							:     List[str]=True							,	lowerCamelCase__							:     List[Any]=None							,	**lowerCamelCase__							:     List[str]							,	)  ->      Union[str, Any]:





      '''simple docstring'''






      super().__init__(
          pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__							,	)
      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    List[Any]          =   hidden_size
      UpperCamelCase__					:    Any          =   num_hidden_layers
      UpperCamelCase__					:    Tuple          =   num_attention_heads
      UpperCamelCase__					:    Any          =   hidden_act
      UpperCamelCase__					:    Dict          =   intermediate_size
      UpperCamelCase__					:    Optional[Any]          =   hidden_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Tuple          =   max_position_embeddings
      UpperCamelCase__					:    List[str]          =   type_vocab_size
      UpperCamelCase__					:    Any          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Any          =   position_embedding_type
      UpperCamelCase__					:    Optional[Any]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout
      # additional properties
      UpperCamelCase__					:    int          =   max_depth
      UpperCamelCase__					:    Optional[Any]          =   max_xpath_tag_unit_embeddings
      UpperCamelCase__					:    List[Any]          =   max_xpath_subs_unit_embeddings
      UpperCamelCase__					:    List[Any]          =   tag_pad_id
      UpperCamelCase__					:    Optional[int]          =   subs_pad_id
      UpperCamelCase__					:    Optional[Any]          =   xpath_unit_hidden_size


