


"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_rembert import RemBertTokenizer
else:
    __lowerCamelCase      :Any	    =		None

__lowerCamelCase      :List[Any]	    =		logging.get_logger(__name__)
__lowerCamelCase      :int	    =		{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase      :int	    =		{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase      :Optional[Any]	    =		{
    'google/rembert': 256,
}

__lowerCamelCase      :Tuple	    =		'▁'


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=VOCAB_FILES_NAMES
     snake_case__	:					Optional[int]													=PRETRAINED_VOCAB_FILES_MAP
     snake_case__	:					Union[str, Any]													=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__	:					Optional[int]													=RemBertTokenizer

     def __init__(	self:						Union[str, Any]     ,		__a:						Tuple=None     ,		__a:						Any=None     ,		__a:						str=True     ,		__a:						List[str]=True     ,		__a:						str=False     ,		__a:						Tuple="[CLS]"     ,		__a:						Optional[Any]="[SEP]"     ,		__a:						Union[str, Any]="<unk>"     ,		__a:						List[str]="[SEP]"     ,		__a:						str="<pad>"     ,		__a:						str="[CLS]"     ,		__a:						List[str]="[MASK]"     ,		**__a:						Dict     ,		)->    Optional[int]:
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase					:					Dict        =	AddedToken(__a     ,		lstrip=__a     ,		rstrip=__a	) if isinstance(__a     ,		__a	) else mask_token

           super().__init__(
               __a     ,		tokenizer_file=__a     ,		do_lower_case=__a     ,		remove_space=__a     ,		keep_accents=__a     ,		bos_token=__a     ,		eos_token=__a     ,		unk_token=__a     ,		sep_token=__a     ,		pad_token=__a     ,		cls_token=__a     ,		mask_token=__a     ,		**__a     ,		)

           lowerCamelCase					:					Optional[Any]        =	do_lower_case
           lowerCamelCase					:					str        =	remove_space
           lowerCamelCase					:					Any        =	keep_accents
           lowerCamelCase					:					Optional[Any]        =	vocab_file
           lowerCamelCase					:					Optional[int]        =	False if not self.vocab_file else True

     def 		a__						(	self:						str     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None	)->    List[int]:
           lowerCamelCase					:					Optional[Any]        =	[self.sep_token_id]
           lowerCamelCase					:					Dict        =	[self.cls_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 		a__						(	self:						Optional[Any]     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None     ,		__a:						bool = False	)->    List[int]:

           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           """You should not supply a second sequence if the provided sequence of """
                           """ids is already formatted with special tokens for the model."""	)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is not None:
                 return [1] + ([0] * len(__a	)) + [1] + ([0] * len(__a	)) + [1]
           return [1] + ([0] * len(__a	)) + [1]

     def 		a__						(	self:						Dict     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None	)->    List[int]:
           lowerCamelCase					:					Union[str, Any]        =	[self.sep_token_id]
           lowerCamelCase					:					List[str]        =	[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 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						Optional[str] = None	)->    Tuple[str]:
           if not os.path.isdir(__a	):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(__a	)	)
                 return
           lowerCamelCase					:					Optional[Any]        =	os.path.join(
               __a     ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(__a	):
                 copyfile(self.vocab_file     ,		__a	)

           return (out_vocab_file,)








"""simple docstring"""


from typing import List, Optional, Union

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


__lowerCamelCase      :str	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													='''time_series_transformer'''
     snake_case__	:					List[Any]													={
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }

     def __init__(	self:						List[str]     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						str = "student_t"     ,		__a:						str = "nll"     ,		__a:						int = 1     ,		__a:						List[int] = [1, 2, 3, 4, 5, 6, 7]     ,		__a:						Optional[Union[str, bool]] = "mean"     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						Optional[List[int]] = None     ,		__a:						Optional[List[int]] = None     ,		__a:						int = 32     ,		__a:						int = 32     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						bool = True     ,		__a:						str = "gelu"     ,		__a:						int = 64     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						int = 100     ,		__a:						float = 0.02     ,		__a:						Tuple=True     ,		**__a:						str     ,		)->    Any:
           # time series specific configuration
           lowerCamelCase					:					str        =	prediction_length
           lowerCamelCase					:					Optional[Any]        =	context_length or prediction_length
           lowerCamelCase					:					Tuple        =	distribution_output
           lowerCamelCase					:					Any        =	loss
           lowerCamelCase					:					List[Any]        =	input_size
           lowerCamelCase					:					int        =	num_time_features
           lowerCamelCase					:					Dict        =	lags_sequence
           lowerCamelCase					:					Optional[int]        =	scaling
           lowerCamelCase					:					int        =	num_dynamic_real_features
           lowerCamelCase					:					Tuple        =	num_static_real_features
           lowerCamelCase					:					Any        =	num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					int        =	cardinality
           else:
                 lowerCamelCase					:					Dict        =	[0]
           if embedding_dimension and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					str        =	embedding_dimension
           else:
                 lowerCamelCase					:					str        =	[min(50     ,		(cat + 1) // 2	) for cat in self.cardinality]
           lowerCamelCase					:					Any        =	num_parallel_samples

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

           lowerCamelCase					:					Optional[int]        =	dropout
           lowerCamelCase					:					List[str]        =	attention_dropout
           lowerCamelCase					:					Tuple        =	activation_dropout
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					int        =	decoder_layerdrop

           lowerCamelCase					:					Optional[int]        =	activation_function
           lowerCamelCase					:					Optional[Any]        =	init_std

           lowerCamelCase					:					Optional[Any]        =	use_cache

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







     @property
     def 		a__						(	self:						int	)->    int:
           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
           )




