

from typing import Optional, Union

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



class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		Optional[Any]         =     'MarkupLMFeatureExtractor'
  __lowerCAmelCase   :		int         =     ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
  __lowerCAmelCase   :		Optional[Any]         =     True

  def __call__(    self  ,						_A=None  ,						_A=None  ,						_A=None  ,						_A=None  ,						_A=None  ,						_A = True  ,						_A = False  ,						_A = None  ,						_A = None  ,						_A = 0  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = False  ,						_A = False  ,						_A = False  ,						_A = False  ,						_A = True  ,						_A = None  ,						**_A  ,						):
      # 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`')

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

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

      return encoded_inputs

  def   lowerCAmelCase__   (    self  ,						*_A  ,						**_A):
      return self.tokenizer.batch_decode(*_A  ,						**_A)

  def   lowerCAmelCase__   (    self  ,						*_A  ,						**_A):
      return self.tokenizer.decode(*_A  ,						**_A)


  @property
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.tokenizer.model_input_names
      return tokenizer_input_names





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__     :					List[str]								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


