

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[int]  	=					'MarkupLMFeatureExtractor'
		__lowerCAmelCase       :		Optional[Any]  	=					('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
		__lowerCAmelCase       :		Dict  	=					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					: Optional[int]								=      logging.get_logger(__name__)

UpperCamelCase__					: int								=      {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					'pegasus_x'
  __lowerCAmelCase       :		Optional[int]  	=					['past_key_values']
  __lowerCAmelCase       :		List[str]  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=96103  ,						_A=16384  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0  ,						_A=True  ,						_A=0  ,						_A=1  ,						_A=1  ,						_A=32  ,						_A=512  ,						_A=True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE_													=  num_global_tokens
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  stagger_local_blocks

      super().__init__(
          pad_token_id=_A  ,						eos_token_id=_A  ,						is_encoder_decoder=_A  ,						decoder_start_token_id=_A  ,						forced_eos_token_id=_A  ,						**_A  ,						)

  @property
  def   lowerCAmelCase__       (   self):
      return self.encoder_attention_heads


  @property
  def   lowerCAmelCase__       (   self):
      return self.d_model


