






"""simple docstring"""



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


__A		:	Dict      =		logging.get_logger(__name__)

__A		:	Optional[Any]      =		{
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}





class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Any							   =       'yoso'




					def __init__(    self					:						str   ,						A					:						Dict=5_02_65   ,						A					:						Any=7_68   ,						A					:						Union[str, Any]=12   ,						A					:						List[Any]=12   ,						A					:						Dict=30_72   ,						A					:						Dict="gelu"   ,						A					:						Dict=0.1   ,						A					:						Optional[int]=0.1   ,						A					:						str=40_96   ,						A					:						str=1   ,						A					:						Dict=0.02   ,						A					:						Optional[int]=1e-12   ,						A					:						str="absolute"   ,						A					:						Dict=True   ,						A					:						List[str]=9   ,						A					:						Optional[int]=64   ,						A					:						List[Any]=None   ,						A					:						Dict=True   ,						A					:						str=True   ,						A					:						int=1   ,						A					:						int=0   ,						A					:						List[str]=2   ,						**A					:						Optional[Any]   ,						)  ->  str:
							super().__init__(pad_token_id=A   ,						bos_token_id=A   ,						eos_token_id=A   ,						**A       )

							lowercase_			:			Optional[Any]								=       vocab_size
							lowercase_			:			int								=       max_position_embeddings
							lowercase_			:			Tuple								=       hidden_size
							lowercase_			:			Union[str, Any]								=       num_hidden_layers
							lowercase_			:			List[Any]								=       num_attention_heads
							lowercase_			:			List[str]								=       intermediate_size
							lowercase_			:			List[Any]								=       hidden_act
							lowercase_			:			Optional[Any]								=       hidden_dropout_prob
							lowercase_			:			Optional[int]								=       attention_probs_dropout_prob
							lowercase_			:			int								=       initializer_range
							lowercase_			:			Dict								=       type_vocab_size
							lowercase_			:			Dict								=       layer_norm_eps
							lowercase_			:			int								=       position_embedding_type
							lowercase_			:			Any								=       use_expectation
							lowercase_			:			Optional[int]								=       hash_code_len
							lowercase_			:			Optional[Any]								=       num_hash
							lowercase_			:			List[str]								=       conv_window
							lowercase_			:			int								=       use_fast_hash
							lowercase_			:			Optional[int]								=       lsh_backward











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		:	Any      =		{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	List[str]      =		['''MarkupLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MarkupLMForQuestionAnswering''',
						    '''MarkupLMForSequenceClassification''',
						    '''MarkupLMForTokenClassification''',
						    '''MarkupLMModel''',
						    '''MarkupLMPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
						from .feature_extraction_markuplm import MarkupLMFeatureExtractor
						from .processing_markuplm import MarkupLMProcessor
						from .tokenization_markuplm import MarkupLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_markuplm_fast import MarkupLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_markuplm import (
												    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MarkupLMForQuestionAnswering,
												    MarkupLMForSequenceClassification,
												    MarkupLMForTokenClassification,
												    MarkupLMModel,
												    MarkupLMPreTrainedModel,
												)


else:
						import sys

						__A		:	Optional[Any]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)



