
from typing import List, Optional

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


__magic_name__            =  logging.get_logger(__name__)

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




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Dict    	=     'autoformer'
      _A						:		List[Any]    	=     {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
          'num_hidden_layers': 'encoder_layers',
      }
      def __init__(			self		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = "student_t"		,	lowerCamelCase = "nll"		,	lowerCamelCase = 1		,	lowerCamelCase = [1, 2, 3, 4, 5, 6, 7]		,	lowerCamelCase = True		,	lowerCamelCase = 0		,	lowerCamelCase = 0		,	lowerCamelCase = 0		,	lowerCamelCase = 0		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = 64		,	lowerCamelCase = 2		,	lowerCamelCase = 2		,	lowerCamelCase = 2		,	lowerCamelCase = 2		,	lowerCamelCase = 32		,	lowerCamelCase = 32		,	lowerCamelCase = "gelu"		,	lowerCamelCase = 0.1		,	lowerCamelCase = 0.1		,	lowerCamelCase = 0.1		,	lowerCamelCase = 0.1		,	lowerCamelCase = 0.1		,	lowerCamelCase = 1_00		,	lowerCamelCase = 0.0_2		,	lowerCamelCase = True		,	lowerCamelCase=True		,	lowerCamelCase = 10		,	lowerCamelCase = 25		,	lowerCamelCase = 3		,	**lowerCamelCase		,	):
          # time series specific configuration
          snake_case__						=				prediction_length
          snake_case__						=				context_length if context_length is not None else prediction_length
          snake_case__						=				distribution_output
          snake_case__						=				loss
          snake_case__						=				input_size
          snake_case__						=				num_time_features
          snake_case__						=				lags_sequence
          snake_case__						=				scaling
          snake_case__						=				num_dynamic_real_features
          snake_case__						=				num_static_real_features
          snake_case__						=				num_static_categorical_features
          if cardinality is not None and num_static_categorical_features > 0:
              if len(lowerCamelCase ) != num_static_categorical_features:
                  raise ValueError(
                      "The cardinality should be a list of the same length as `num_static_categorical_features`" )
              snake_case__						=				cardinality
          else:
              snake_case__						=				[0]
          if embedding_dimension is not None and num_static_categorical_features > 0:
              if len(lowerCamelCase ) != num_static_categorical_features:
                  raise ValueError(
                      "The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
              snake_case__						=				embedding_dimension
          else:
              snake_case__						=				[min(50		,	(cat + 1) // 2 ) for cat in self.cardinality]
          snake_case__						=				num_parallel_samples

          # Transformer architecture configuration
          snake_case__						=				input_size * len(self.lags_sequence ) + self._number_of_features
          snake_case__						=				d_model
          snake_case__						=				encoder_attention_heads
          snake_case__						=				decoder_attention_heads
          snake_case__						=				encoder_ffn_dim
          snake_case__						=				decoder_ffn_dim
          snake_case__						=				encoder_layers
          snake_case__						=				decoder_layers

          snake_case__						=				dropout
          snake_case__						=				attention_dropout
          snake_case__						=				activation_dropout
          snake_case__						=				encoder_layerdrop
          snake_case__						=				decoder_layerdrop

          snake_case__						=				activation_function
          snake_case__						=				init_std

          snake_case__						=				use_cache

          # Autoformer
          snake_case__						=				label_length
          snake_case__						=				moving_average
          snake_case__						=				autocorrelation_factor

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



      @property
      def A_			(			self ):
          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
          )





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[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		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          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(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[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		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

          return (out_vocab_file,)



