

'''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'''







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

       def __call__(	self					:  Union[str, Any]						,	__snake_case					:  Union[str, Any]=None						,	__snake_case					:  Optional[int]=None						,	__snake_case					:  Optional[Any]=None						,	__snake_case					:  Union[str, Any]=None						,	__snake_case					:  int=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						,	)   -> BatchEncoding:







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

        lowerCamelCase      						=    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					:  int						,	*__snake_case					:  Optional[Any]						,	**__snake_case					:  Tuple     )   -> List[Any]:







        '''simple docstring'''

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

       def   lowerCamelCase__						(	self					:  str						,	*__snake_case					:  Any						,	**__snake_case					:  List[str]     )   -> Tuple:







        '''simple docstring'''

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





       @property
       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> str:







        '''simple docstring'''

        lowerCamelCase      						=    self.tokenizer.model_input_names
        return tokenizer_input_names







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     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase				):


       '''simple docstring'''







       @register_to_config
       def __init__(	self					:  Any						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  float						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  str						,	__snake_case					:  bool = False						,	)   -> int:







        '''simple docstring'''

        super().__init__()

        lowerCamelCase      						=    nn.Linear(__snake_case						,	__snake_case						,	bias=__snake_case     )

        lowerCamelCase      						=    nn.Embedding(__snake_case						,	__snake_case     )
        lowerCamelCase      						=    False

        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )

        lowerCamelCase      						=    TaConfig(
            d_model=__snake_case						,	num_heads=__snake_case						,	d_kv=__snake_case						,	d_ff=__snake_case						,	feed_forward_proj=__snake_case						,	dropout_rate=__snake_case						,	is_decoder=__snake_case						,	is_encoder_decoder=__snake_case						,	)
        lowerCamelCase      						=    nn.ModuleList()
        for lyr_num in range(__snake_case     ):
         lowerCamelCase      						=    TaBlock(__snake_case     )
         self.encoders.append(__snake_case     )

        lowerCamelCase      						=    TaLayerNorm(__snake_case     )
        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )





       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[Any]						,	__snake_case					:  List[Any]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    self.input_proj(__snake_case     )

        # terminal relative positional encodings
        lowerCamelCase      						=    encoder_inputs.shape[1]
        lowerCamelCase      						=    torch.arange(__snake_case						,	device=encoder_inputs.device     )

        lowerCamelCase      						=    encoder_inputs_mask.sum(-1     )
        lowerCamelCase      						=    torch.roll(input_positions.unsqueeze(0     )						,	tuple(seq_lens.tolist()     )						,	dims=0     )
        x += self.position_encoding(__snake_case     )

        lowerCamelCase      						=    self.dropout_pre(__snake_case     )

        # inverted the attention mask
        lowerCamelCase      						=    encoder_inputs.size()
        lowerCamelCase      						=    self.get_extended_attention_mask(__snake_case						,	__snake_case     )

        for lyr in self.encoders:
         lowerCamelCase      						=    lyr(__snake_case						,	__snake_case     )[0]
        lowerCamelCase      						=    self.layer_norm(__snake_case     )

        return self.dropout_post(__snake_case     ), encoder_inputs_mask




