


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class      UpperCAmelCase_ ( UpperCamelCase		,			UpperCamelCase		,			UpperCamelCase   ):

   '''simple docstring'''




   @register_to_config
   def __init__(		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A = False						,    ):






     """simple docstring"""





     super().__init__()

     lowerCamelCase			: Dict   =    nn.Linear(__A						,    __A						,    bias=__A			)

     lowerCamelCase			: Optional[Any]   =    nn.Embedding(__A						,    __A			)
     lowerCamelCase			: List[str]   =    False

     lowerCamelCase			: Any   =    nn.Dropout(p=__A			)

     lowerCamelCase			: str   =    TaConfig(
         d_model=__A						,    num_heads=__A						,    d_kv=__A						,    d_ff=__A						,    feed_forward_proj=__A						,    dropout_rate=__A						,    is_decoder=__A						,    is_encoder_decoder=__A						,    )
     lowerCamelCase			: List[Any]   =    nn.ModuleList()
     for lyr_num in range(__A			):
       lowerCamelCase			: Optional[int]   =    TaBlock(__A			)
       self.encoders.append(__A			)

     lowerCamelCase			: List[Any]   =    TaLayerNorm(__A			)
     lowerCamelCase			: Any   =    nn.Dropout(p=__A			)






   def _snake_case   (		self						,    __A						,    __A			):






     """simple docstring"""





     lowerCamelCase			: str   =    self.input_proj(__A			)

     # terminal relative positional encodings
     lowerCamelCase			: Optional[Any]   =    encoder_inputs.shape[1]
     lowerCamelCase			: Optional[int]   =    torch.arange(__A						,    device=encoder_inputs.device			)

     lowerCamelCase			: List[Any]   =    encoder_inputs_mask.sum(-1			)
     lowerCamelCase			: Optional[int]   =    torch.roll(input_positions.unsqueeze(0			)						,    tuple(seq_lens.tolist()			)						,    dims=0			)
     x += self.position_encoding(__A			)

     lowerCamelCase			: List[str]   =    self.dropout_pre(__A			)

     # inverted the attention mask
     lowerCamelCase			: Any   =    encoder_inputs.size()
     lowerCamelCase			: Optional[int]   =    self.get_extended_attention_mask(__A						,    __A			)

     for lyr in self.encoders:
       lowerCamelCase			: Optional[int]   =    lyr(__A						,    __A			)[0]
     lowerCamelCase			: Dict   =    self.layer_norm(__A			)

     return self.dropout_post(__A			), encoder_inputs_mask







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		"MarkupLMFeatureExtractor"
   __A			:				List[str]							=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   __A			:				str							=		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						,    ):






     """simple docstring"""





     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`"			)

       lowerCamelCase			: Any   =    self.feature_extractor(__A			)
       lowerCamelCase			: Optional[int]   =    features["nodes"]
       lowerCamelCase			: Any   =    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			):
         lowerCamelCase			: Tuple   =    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     lowerCamelCase			: Tuple   =    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 _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,    **__A			)






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,    **__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.tokenizer.model_input_names
     return tokenizer_input_names



