'''simple docstring'''

from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

UpperCamelCase__     :	List[str]				=			_symbol_database.Default()


UpperCamelCase__     :	Optional[Any]				=			_descriptor_pool.Default().AddSerializedFile(
    b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

UpperCamelCase__     :	Dict				=			globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
  UpperCamelCase__     :	List[Any]				=			None
  UpperCamelCase__     :	List[str]				=			b'H\003'
  # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
  # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
  # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
  # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
  # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
  UpperCamelCase__     :	List[str]				=			45
  UpperCamelCase__     :	Optional[int]				=			1_581
  UpperCamelCase__     :	Dict				=			1_517
  UpperCamelCase__     :	Union[str, Any]				=			1_570
  UpperCamelCase__     :	Union[str, Any]				=			1_584
  UpperCamelCase__     :	Optional[Any]				=			1_793
  UpperCamelCase__     :	Optional[int]				=			1_795
  UpperCamelCase__     :	int				=			1_916
  UpperCamelCase__     :	List[str]				=			1_864
  UpperCamelCase__     :	List[str]				=			1_905
  UpperCamelCase__     :	Union[str, Any]				=			1_919
  UpperCamelCase__     :	List[Any]				=			2_429
  UpperCamelCase__     :	Dict				=			2_208
  UpperCamelCase__     :	Tuple				=			2_418
  UpperCamelCase__     :	str				=			2_323
  UpperCamelCase__     :	Any				=			2_407
# @@protoc_insertion_point(module_scope)





'''simple docstring'''

from typing import List, Optional, Union

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


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

UpperCamelCase__     :	Optional[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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   '''time_series_transformer'''
      UpperCAmelCase_      :      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_ = "mean"					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = 32					,lowerCamelCase_ = 32					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = True					,lowerCamelCase_ = "gelu"					,lowerCamelCase_ = 64					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 100					,lowerCamelCase_ = 0.02					,lowerCamelCase_=True					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  prediction_length
         UpperCAmelCase__							:				Optional[Any]   				=  context_length or prediction_length
         UpperCAmelCase__							:				Union[str, Any]   				=  distribution_output
         UpperCAmelCase__							:				List[Any]   				=  loss
         UpperCAmelCase__							:				Optional[int]   				=  input_size
         UpperCAmelCase__							:				Tuple   				=  num_time_features
         UpperCAmelCase__							:				str   				=  lags_sequence
         UpperCAmelCase__							:				Union[str, Any]   				=  scaling
         UpperCAmelCase__							:				Optional[int]   				=  num_dynamic_real_features
         UpperCAmelCase__							:				Optional[Any]   				=  num_static_real_features
         UpperCAmelCase__							:				str   				=  num_static_categorical_features
         if cardinality 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`'''							)
            UpperCAmelCase__							:				Union[str, Any]   				=  cardinality
         else:
            UpperCAmelCase__							:				List[str]   				=  [0]
         if embedding_dimension 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`'''							)
            UpperCAmelCase__							:				int   				=  embedding_dimension
         else:
            UpperCAmelCase__							:				Union[str, Any]   				=  [min(50					,(cat + 1) // 2							) for cat in self.cardinality]
         UpperCAmelCase__							:				Dict   				=  num_parallel_samples

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

         UpperCAmelCase__							:				Optional[Any]   				=  dropout
         UpperCAmelCase__							:				Any   				=  attention_dropout
         UpperCAmelCase__							:				Optional[int]   				=  activation_dropout
         UpperCAmelCase__							:				List[Any]   				=  encoder_layerdrop
         UpperCAmelCase__							:				Optional[int]   				=  decoder_layerdrop

         UpperCAmelCase__							:				List[Any]   				=  activation_function
         UpperCAmelCase__							:				List[Any]   				=  init_std

         UpperCAmelCase__							:				Optional[Any]   				=  use_cache

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




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         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
         )




