




from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Tuple				    ="MarkupLMFeatureExtractor"
	lowerCamelCase__		: Union[str, Any]				    =("MarkupLMTokenizer", "MarkupLMTokenizerFast")
	lowerCamelCase__		: Dict				    =True






	def __call__(	self				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase = True				,     lowerCamelCase = False				,     lowerCamelCase = None				,     lowerCamelCase = None				,     lowerCamelCase = 0				,     lowerCamelCase = None				,     lowerCamelCase = None				,     lowerCamelCase = None				,     lowerCamelCase = False				,     lowerCamelCase = False				,     lowerCamelCase = False				,     lowerCamelCase = False				,     lowerCamelCase = True				,     lowerCamelCase = None				,     **lowerCamelCase				,     )      ->   BatchEncoding:




						"""simple docstring"""






						if self.parse_html:
											if html_strings is None:
																raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''						)

											if nodes is not None or xpaths is not None or node_labels is not None:
																raise ValueError(
																    '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''						)

											__magic_name__					:							Tuple		   =							self.feature_extractor(lowerCamelCase						)
											__magic_name__					:							Optional[Any]		   =							features['''nodes''']
											__magic_name__					:							Dict		   =							features['''xpaths''']
						else:
											if html_strings is not None:
																raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''						)
											if nodes is None or xpaths is None:
																raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''						)

        # # second, apply the tokenizer
						if questions is not None and self.parse_html:
											if isinstance(lowerCamelCase				,     lowerCamelCase						):
																__magic_name__					:							Union[str, Any]		   =							[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

						__magic_name__					:							Any		   =							self.tokenizer(
						    text=questions if questions is not None else nodes				,     text_pair=nodes if questions is not None else None				,     xpaths=lowerCamelCase				,     node_labels=lowerCamelCase				,     add_special_tokens=lowerCamelCase				,     padding=lowerCamelCase				,     truncation=lowerCamelCase				,     max_length=lowerCamelCase				,     stride=lowerCamelCase				,     pad_to_multiple_of=lowerCamelCase				,     return_token_type_ids=lowerCamelCase				,     return_attention_mask=lowerCamelCase				,     return_overflowing_tokens=lowerCamelCase				,     return_special_tokens_mask=lowerCamelCase				,     return_offsets_mapping=lowerCamelCase				,     return_length=lowerCamelCase				,     verbose=lowerCamelCase				,     return_tensors=lowerCamelCase				,     **lowerCamelCase				,     )

						return encoded_inputs






	def  lowercase      (	self				,     *lowerCamelCase				,     **lowerCamelCase						)      ->   int:




						"""simple docstring"""






						return self.tokenizer.batch_decode(*lowerCamelCase				,     **lowerCamelCase						)






	def  lowercase      (	self				,     *lowerCamelCase				,     **lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						return self.tokenizer.decode(*lowerCamelCase				,     **lowerCamelCase						)






	@property
	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							self.tokenizer.model_input_names
						return tokenizer_input_names









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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''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 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Tuple				    ="yoso"






	def __init__(	self				,     lowerCamelCase=50265				,     lowerCamelCase=768				,     lowerCamelCase=12				,     lowerCamelCase=12				,     lowerCamelCase=3072				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0.1				,     lowerCamelCase=4096				,     lowerCamelCase=1				,     lowerCamelCase=0.0_2				,     lowerCamelCase=1e-12				,     lowerCamelCase="absolute"				,     lowerCamelCase=True				,     lowerCamelCase=9				,     lowerCamelCase=64				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=True				,     lowerCamelCase=1				,     lowerCamelCase=0				,     lowerCamelCase=2				,     **lowerCamelCase				,     )      ->   Tuple:




						"""simple docstring"""






						super().__init__(pad_token_id=lowerCamelCase				,     bos_token_id=lowerCamelCase				,     eos_token_id=lowerCamelCase				,     **lowerCamelCase						)

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



