
'''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__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'MarkupLMFeatureExtractor'
  _UpperCamelCase							=			('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
  _UpperCamelCase							=			True

  def __call__(self	:			str						,			lowerCAmelCase_	:			Optional[Any]=None						,			lowerCAmelCase_	:			Optional[int]=None						,			lowerCAmelCase_	:			Tuple=None						,			lowerCAmelCase_	:			Dict=None						,			lowerCAmelCase_	:			Optional[int]=None						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Union[bool, str, PaddingStrategy] = False						,			lowerCAmelCase_	:			Union[bool, str, TruncationStrategy] = None						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			int = 0						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			bool = False						,			lowerCAmelCase_	:			bool = False						,			lowerCAmelCase_	:			bool = False						,			lowerCAmelCase_	:			bool = False						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Optional[Union[str, TensorType]] = None						,			**lowerCAmelCase_	:			Union[str, Any]						,			) ->							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(lowerCAmelCase_     )
              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(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    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=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 		_lowerCamelCase						(self	:			Optional[int]						,			*lowerCAmelCase_	:			Union[str, Any]						,			**lowerCAmelCase_	:			int     ) ->							Union[str, Any]:
        return self.tokenizer.batch_decode(*lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int						,			*lowerCAmelCase_	:			Union[str, Any]						,			**lowerCAmelCase_	:			Optional[int]     ) ->							Dict:
        return self.tokenizer.decode(*lowerCAmelCase_						,			**lowerCAmelCase_     )


  @property
  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Union[str, Any]:
        snake_case_    							=				self.tokenizer.model_input_names
        return tokenizer_input_names





"""simple docstring"""






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  @cached_property
  def 							lowercase_     (						self						)				->					Optional[Any]:
     return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'						)
  @cached_property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[Any]						=    self.tokenizer_ab
     a   :	int						=    ' I am a small frog.'
     a   :	Any						=    tok([src_text]  ,					padding=__UpperCAmelCase  ,					truncation=__UpperCAmelCase						)['input_ids']
     a   :	List[str]						=    tok.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase  ,					clean_up_tokenization_spaces=__UpperCAmelCase						)[0]
     assert src_text == decoded
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	Optional[int]						=    self.rust_tokenizer_ab
     a   :	List[Any]						=    ' I am a small frog.'
     a   :	Union[str, Any]						=    tok([src_text]  ,					padding=__UpperCAmelCase  ,					truncation=__UpperCAmelCase						)['input_ids']
     a   :	Optional[Any]						=    tok.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase  ,					clean_up_tokenization_spaces=__UpperCAmelCase						)[0]
     assert src_text == decoded
  def 							lowercase_     (						self						)				->					Tuple:
     assert self.tokenizer_ab.add_prefix_space
     assert self.tokenizer_ab([' Sam', 'Sam']						).input_ids == [[55_02, 2], [55_02, 2]]






  def 							lowercase_     (						self						)				->					Tuple:
     assert self.rust_tokenizer_ab.add_prefix_space
     assert self.rust_tokenizer_ab([' Sam', 'Sam']						).input_ids == [[55_02, 2], [55_02, 2]]





