"""simple docstring"""




from typing import List, Optional

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


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}


class      lowerCamelCase_					(       __lowerCamelCase			):






    '''simple docstring'''






    a__     :						int      =				'autoformer'
    a__     :						int      =				{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
        'num_hidden_layers': 'encoder_layers',
    }







    def __init__(     self			,			__lowercase = None			,			__lowercase = None			,			__lowercase = "student_t"			,			__lowercase = "nll"			,			__lowercase = 1			,			__lowercase = [1, 2, 3, 4, 5, 6, 7]			,			__lowercase = True			,			__lowercase = 0			,			__lowercase = 0			,			__lowercase = 0			,			__lowercase = 0			,			__lowercase = None			,			__lowercase = None			,			__lowercase = 64			,			__lowercase = 2			,			__lowercase = 2			,			__lowercase = 2			,			__lowercase = 2			,			__lowercase = 32			,			__lowercase = 32			,			__lowercase = "gelu"			,			__lowercase = 0.1			,			__lowercase = 0.1			,			__lowercase = 0.1			,			__lowercase = 0.1			,			__lowercase = 0.1			,			__lowercase = 100			,			__lowercase = 0.02			,			__lowercase = True			,			__lowercase=True			,			__lowercase = 10			,			__lowercase = 25			,			__lowercase = 3			,			**__lowercase			,			)	->     List[str]:
        # time series specific configuration
        __UpperCamelCase					:List[Any]        =		prediction_length
        __UpperCamelCase					:Optional[Any]        =		context_length if context_length is not None else prediction_length
        __UpperCamelCase					:Any        =		distribution_output
        __UpperCamelCase					:Optional[Any]        =		loss
        __UpperCamelCase					:Optional[int]        =		input_size
        __UpperCamelCase					:Optional[Any]        =		num_time_features
        __UpperCamelCase					:Union[str, Any]        =		lags_sequence
        __UpperCamelCase					:int        =		scaling
        __UpperCamelCase					:Any        =		num_dynamic_real_features
        __UpperCamelCase					:Optional[int]        =		num_static_real_features
        __UpperCamelCase					:List[Any]        =		num_static_categorical_features
        if cardinality is not None and num_static_categorical_features > 0:
            if len(UpperCamelCase_) != num_static_categorical_features:
                raise ValueError(
                    '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
            __UpperCamelCase					:Union[str, Any]        =		cardinality
        else:
            __UpperCamelCase					:Tuple        =		[0]
        if embedding_dimension is not None and num_static_categorical_features > 0:
            if len(UpperCamelCase_) != num_static_categorical_features:
                raise ValueError(
                    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
            __UpperCamelCase					:str        =		embedding_dimension
        else:
            __UpperCamelCase					:List[Any]        =		[min(50			,			(cat + 1) // 2) for cat in self.cardinality]
        __UpperCamelCase					:Dict        =		num_parallel_samples

        # Transformer architecture configuration
        __UpperCamelCase					:Union[str, Any]        =		input_size * len(self.lags_sequence) + self._number_of_features
        __UpperCamelCase					:int        =		d_model
        __UpperCamelCase					:Tuple        =		encoder_attention_heads
        __UpperCamelCase					:Dict        =		decoder_attention_heads
        __UpperCamelCase					:List[str]        =		encoder_ffn_dim
        __UpperCamelCase					:Optional[int]        =		decoder_ffn_dim
        __UpperCamelCase					:List[Any]        =		encoder_layers
        __UpperCamelCase					:Optional[int]        =		decoder_layers

        __UpperCamelCase					:Dict        =		dropout
        __UpperCamelCase					:Union[str, Any]        =		attention_dropout
        __UpperCamelCase					:str        =		activation_dropout
        __UpperCamelCase					:List[str]        =		encoder_layerdrop
        __UpperCamelCase					:Any        =		decoder_layerdrop

        __UpperCamelCase					:Union[str, Any]        =		activation_function
        __UpperCamelCase					:int        =		init_std

        __UpperCamelCase					:Union[str, Any]        =		use_cache

        # Autoformer
        __UpperCamelCase					:Optional[int]        =		label_length
        __UpperCamelCase					:Tuple        =		moving_average
        __UpperCamelCase					:Optional[int]        =		autocorrelation_factor

        super().__init__(is_encoder_decoder=UpperCamelCase_			,			**UpperCamelCase_)







    @property
    def       UpperCamelCase__       (     self)	->     List[Any]:
        return (
            sum(self.embedding_dimension)
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase										= logging.get_logger(__name__)

__lowercase										= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase										= {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase										= {'''allegro/herbert-base-cased''': 514}
__lowercase										= {}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				VOCAB_FILES_NAMES
    a__     :						Dict      =				PRETRAINED_VOCAB_FILES_MAP
    a__     :						Tuple      =				PRETRAINED_INIT_CONFIGURATION
    a__     :						Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__     :						Union[str, Any]      =				HerbertTokenizer







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase="</s>"			,			**__lowercase			,			)	->     Optional[Any]:
        super().__init__(
            __lowercase			,			__lowercase			,			tokenizer_file=__lowercase			,			cls_token=__lowercase			,			unk_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			sep_token=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:List[str]        =		[self.cls_token_id]
        __UpperCamelCase					:Tuple        =		[self.sep_token_id]
        if token_ids_a is None:
            return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase			,			token_ids_a=__lowercase			,			already_has_special_tokens=__lowercase)

        if token_ids_a is None:
            return [1] + ([0] * len(__lowercase)) + [1]
        return [1] + ([0] * len(__lowercase)) + [1] + ([0] * len(__lowercase)) + [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:Optional[Any]        =		[self.sep_token_id]
        __UpperCamelCase					:int        =		[self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:
        __UpperCamelCase					:Optional[int]        =		self._tokenizer.model.save(__lowercase			,			name=__lowercase)
        return tuple(__lowercase)





