






import copy

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


_UpperCAmelCase					:						List[Any]         =     logging.get_logger(__name__)

_UpperCAmelCase					:						List[Any]         =     {}


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Dict 	=     'fsmt_decoder'





     def __init__(	self							, _UpperCAmelCase=0							, _UpperCAmelCase=0  )		->				Dict:
        super().__init__()
        lowerCamelCase									=vocab_size
        lowerCamelCase									=bos_token_id




class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    int 	=     'fsmt'
     __lowerCamelCase       :    Dict 	=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





     def __init__(	self							, _UpperCAmelCase=["en", "de"]							, _UpperCAmelCase=42024							, _UpperCAmelCase=42024							, _UpperCAmelCase="relu"							, _UpperCAmelCase=1024							, _UpperCAmelCase=200							, _UpperCAmelCase=1024							, _UpperCAmelCase=4096							, _UpperCAmelCase=12							, _UpperCAmelCase=16							, _UpperCAmelCase=0.0							, _UpperCAmelCase=4096							, _UpperCAmelCase=12							, _UpperCAmelCase=16							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.02							, _UpperCAmelCase=2							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=5							, _UpperCAmelCase=1.0							, _UpperCAmelCase=False							, _UpperCAmelCase=True							, _UpperCAmelCase=1							, _UpperCAmelCase=0							, _UpperCAmelCase=2							, _UpperCAmelCase=2							, **_UpperCAmelCase							, )		->				str:
        lowerCamelCase									=langs
        lowerCamelCase									=src_vocab_size
        lowerCamelCase									=tgt_vocab_size
        lowerCamelCase									=d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

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

        lowerCamelCase									=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  )		->				Union[str, Any]:
        lowerCamelCase									=copy.deepcopy(self.__dict__  )
        lowerCamelCase									=self.decoder.to_dict()
        lowerCamelCase									=self.__class__.model_type
        return output

"""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       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Optional[Any]         =		'MarkupLMFeatureExtractor'
	snake_case__							:		Tuple         =		('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
	snake_case__							:		Dict         =		True


	def __call__(  self :Any       ,    __magic_name__ :Dict=None       ,    __magic_name__ :Dict=None       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :Tuple=None       ,    __magic_name__ :bool = True       ,    __magic_name__ :Union[bool, str, PaddingStrategy] = False       ,    __magic_name__ :Union[bool, str, TruncationStrategy] = None       ,    __magic_name__ :Optional[int] = None       ,    __magic_name__ :int = 0       ,    __magic_name__ :Optional[int] = None       ,    __magic_name__ :Optional[bool] = None       ,    __magic_name__ :Optional[bool] = None       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = True       ,    __magic_name__ :Optional[Union[str, TensorType]] = None       ,    **__magic_name__ :Optional[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`'''			)

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

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

			return encoded_inputs


	def    _UpperCamelCase							(  self :Tuple       ,    *__magic_name__ :Optional[int]       ,    **__magic_name__ :Any			)			->   int:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Tuple       ,    *__magic_name__ :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   Dict:

			'''simple docstring'''






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






	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.tokenizer.model_input_names
			return tokenizer_input_names




