

'''simple docstring'''

from typing import Optional, Union

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







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''MarkupLMFeatureExtractor'''
				snake_case_							     = ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
				snake_case_							     = True

				def __call__(       self,						lowercase_=None,						lowercase_=None,						lowercase_=None,						lowercase_=None,						lowercase_=None,						lowercase_ = True,						lowercase_ = False,						lowercase_ = None,						lowercase_ = None,						lowercase_ = 0,						lowercase_ = None,						lowercase_ = None,						lowercase_ = None,						lowercase_ = False,						lowercase_ = False,						lowercase_ = False,						lowercase_ = False,						lowercase_ = True,						lowercase_ = None,						**lowercase_,						)    ->							BatchEncoding:
					# first, create nodes and xpaths
					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`' )

						snake_case							=					self.feature_extractor(lowercase_ )
						snake_case							=					features['nodes']
						snake_case							=					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(lowercase_,						lowercase_ ):
							snake_case							=					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

					return encoded_inputs

				def 					_lowerCamelCase    (       self,						*lowercase_,						**lowercase_ )    ->							Optional[int]:
					return self.tokenizer.batch_decode(*lowercase_,						**lowercase_ )

				def 					_lowerCamelCase    (       self,						*lowercase_,						**lowercase_ )    ->							Tuple:
					return self.tokenizer.decode(*lowercase_,						**lowercase_ )






				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.tokenizer.model_input_names
					return tokenizer_input_names










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase					:					Any 		=		{
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Any 		=		[
		    "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "UniSpeechSatForAudioFrameClassification",
		    "UniSpeechSatForCTC",
		    "UniSpeechSatForPreTraining",
		    "UniSpeechSatForSequenceClassification",
		    "UniSpeechSatForXVector",
		    "UniSpeechSatModel",
		    "UniSpeechSatPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					int 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




