



"""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 			__SCREAMING_SNAKE_CASE      ( lowercase_  ):


					'''simple docstring'''





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



					def __call__(			self				:    Optional[int],    lowerCamelCase				:    Optional[int]=None,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Any=None,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Dict=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				:    str,    )->   Union[str, Any]:
								# 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`'''	)

											lowerCamelCase__						:						Union[str, Any]						      =self.feature_extractor(__UpperCamelCase	)
											lowerCamelCase__						:						List[Any]						      =features['''nodes''']
											lowerCamelCase__						:						List[str]						      =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(__UpperCamelCase,    __UpperCamelCase	):
														lowerCamelCase__						:						Tuple						      =[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

								return encoded_inputs



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    Optional[Any]	)->   int:
								return self.tokenizer.batch_decode(*__UpperCamelCase,    **__UpperCamelCase	)



					def 						snake_case			(			self				:    Optional[Any],    *lowerCamelCase				:    List[Any],    **lowerCamelCase				:    Any	)->   int:
								return self.tokenizer.decode(*__UpperCamelCase,    **__UpperCamelCase	)




					@property
					def 						snake_case			(			self				:    List[Any]	)->   Optional[Any]:
								lowerCamelCase__						:						Dict						      =self.tokenizer.model_input_names
								return tokenizer_input_names











"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


