


"""simple docstring"""



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
  from .features import FeatureType







@dataclass
class _lowerCAmelCase       :
   __UpperCAmelCase				: List[str]
   __UpperCAmelCase				: Optional[str]													=						None
   # Automatically constructed
   __UpperCAmelCase				: ClassVar[str]													=						"dict"
   __UpperCAmelCase				: ClassVar[Any]													=						None
   __UpperCAmelCase				: str													=						field(default='''Translation'''					,							init=snake_case_					,							repr=snake_case_			)







   def __call__(			self		)			->	Optional[int]:

       '''simple docstring'''




       return pa.struct({lang: pa.string() for lang in sorted(self.languages		)}		)







   def 				lowerCamelCase	(			self		)			->	Union["FeatureType", Dict[str, "FeatureType"]]:

       '''simple docstring'''




       from .features import Value

       return {k: Value("string"		) for k in sorted(self.languages		)}







@dataclass
class _lowerCAmelCase       :
   __UpperCAmelCase				: Optional[List]													=						None
   __UpperCAmelCase				: Optional[int]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   # Automatically constructed
   __UpperCAmelCase				: ClassVar[str]													=						"dict"
   __UpperCAmelCase				: ClassVar[Any]													=						None
   __UpperCAmelCase				: str													=						field(default='''TranslationVariableLanguages'''					,							init=snake_case_					,							repr=snake_case_			)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   str         =					sorted(set(self.languages		)		) if self.languages else None
       snake_case					:   Dict         =					len(self.languages		) if self.languages else None







   def __call__(			self		)			->	str:

       '''simple docstring'''




       return pa.struct({"language": pa.list_(pa.string()		), "translation": pa.list_(pa.string()		)}		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					set(self.languages		)
       if self.languages and set(UpperCamelCase__		) - lang_set:
           raise ValueError(
               F'Some languages in example ({", ".join(sorted(set(UpperCamelCase__		) - lang_set		)		)}) are not in valid set ({", ".join(UpperCamelCase__		)}).'		)

       # Convert dictionary into tuples, splitting out cases where there are
       # multiple translations for a single language.
       snake_case					:   Optional[Any]         =					[]
       for lang, text in translation_dict.items():
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               translation_tuples.append((lang, text)		)
           else:
               translation_tuples.extend([(lang, el) for el in text]		)

        # Ensure translations are in ascending order by language code.
       snake_case      ,snake_case					:   Optional[Any]         =					zip(*sorted(UpperCamelCase__		)		)

       return {"language": languages, "translation": translations}







   def 				lowerCamelCase	(			self		)			->	Union["FeatureType", Dict[str, "FeatureType"]]:

       '''simple docstring'''




       from .features import Sequence, Value

       return {
           "language": Sequence(Value("string"		)		),
           "translation": Sequence(Value("string"		)		),
       }









"""simple docstring"""



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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''markuplm'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=256		,			UpperCamelCase__=1024		,			UpperCamelCase__=216		,			UpperCamelCase__=1001		,			UpperCamelCase__=32		,			UpperCamelCase__=50		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Any:

       '''simple docstring'''




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





