




from typing import TYPE_CHECKING

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


UpperCamelCase								=     {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     ['OneFormerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     [
         'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'OneFormerForUniversalSegmentation',
         'OneFormerModel',
         'OneFormerPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
     from .processing_oneformer import OneFormerProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_oneformer import OneFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_oneformer import (
              ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              OneFormerForUniversalSegmentation,
              OneFormerModel,
              OneFormerPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=     _LazyModule(__name__, globals()['__file__'], _import_structure)






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     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Any   		=  'MarkupLMFeatureExtractor'
     lowerCAmelCase				:       Optional[Any]   		=  ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
     lowerCAmelCase				:       List[Any]   		=  True
     def __call__(	self   :			Dict					,    __snake_case   :			Tuple=None					,    __snake_case   :			List[Any]=None					,    __snake_case   :			Tuple=None					,    __snake_case   :			Tuple=None					,    __snake_case   :			Any=None					,    __snake_case   :			bool = True					,    __snake_case   :			Union[bool, str, PaddingStrategy] = False					,    __snake_case   :			Union[bool, str, TruncationStrategy] = None					,    __snake_case   :			Optional[int] = None					,    __snake_case   :			int = 0					,    __snake_case   :			Optional[int] = None					,    __snake_case   :			Optional[bool] = None					,    __snake_case   :			Optional[bool] = None					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Union[str, TensorType]] = None					,    **__snake_case   :			Any					,    ):
         # 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`'''				)

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

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

         return encoded_inputs
     def   lowerCamelCase_   (	self   :			List[Any]					,    *__snake_case   :			Dict					,    **__snake_case   :			Optional[int]				):
         return self.tokenizer.batch_decode(*__snake_case					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    *__snake_case   :			Any					,    **__snake_case   :			Dict				):
         return self.tokenizer.decode(*__snake_case					,    **__snake_case				)




     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     self.tokenizer.model_input_names
         return tokenizer_input_names





