

'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      Any					 =     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase :      Optional[Any]					 =     {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase :      int					 =     {
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}







class 							A__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				['input_ids', 'attention_mask']
  A__		 =				RobertaTokenizer





  def __init__(		self      :	Dict							,					_a      :	Optional[int]=None							,					_a      :	Optional[Any]=None							,					_a      :	str=None							,					_a      :	List[Any]="replace"							,					_a      :	List[Any]="<s>"							,					_a      :	List[str]="</s>"							,					_a      :	str="</s>"							,					_a      :	List[Any]="<s>"							,					_a      :	Optional[Any]="<unk>"							,					_a      :	List[str]="<pad>"							,					_a      :	Optional[int]="<mask>"							,					_a      :	str=False							,					_a      :	str=True							,					**_a      :	Tuple							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(
           _a							,					_a							,					tokenizer_file=_a							,					errors=_a							,					bos_token=_a							,					eos_token=_a							,					sep_token=_a							,					cls_token=_a							,					unk_token=_a							,					pad_token=_a							,					mask_token=_a							,					add_prefix_space=_a							,					trim_offsets=_a							,					**_a							,					)

       _SCREAMING_SNAKE_CASE							    =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get('add_prefix_space'							,					_a ) != add_prefix_space:
            _SCREAMING_SNAKE_CASE							    =getattr(_a							,					pre_tok_state.pop('type' ) )
            _SCREAMING_SNAKE_CASE							    =add_prefix_space
            _SCREAMING_SNAKE_CASE							    =pre_tok_class(**_a )

       _SCREAMING_SNAKE_CASE							    =add_prefix_space

       _SCREAMING_SNAKE_CASE							    ='post_processor'
       _SCREAMING_SNAKE_CASE							    =getattr(self.backend_tokenizer							,					_a							,					_a )
       if tokenizer_component_instance:
            _SCREAMING_SNAKE_CASE							    =json.loads(tokenizer_component_instance.__getstate__() )

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                 _SCREAMING_SNAKE_CASE							    =tuple(state['sep'] )
            if "cls" in state:
                 _SCREAMING_SNAKE_CASE							    =tuple(state['cls'] )

            _SCREAMING_SNAKE_CASE							    =False

            if state.get('add_prefix_space'							,					_a ) != add_prefix_space:
                 _SCREAMING_SNAKE_CASE							    =add_prefix_space
                 _SCREAMING_SNAKE_CASE							    =True

            if state.get('trim_offsets'							,					_a ) != trim_offsets:
                 _SCREAMING_SNAKE_CASE							    =trim_offsets
                 _SCREAMING_SNAKE_CASE							    =True

            if changes_to_apply:
                 _SCREAMING_SNAKE_CASE							    =getattr(_a							,					state.pop('type' ) )
                 _SCREAMING_SNAKE_CASE							    =component_class(**_a )
                 setattr(self.backend_tokenizer							,					_a							,					_a )





  @property
  def 						A  (		self      :	Optional[int] )	->    str:





       '''simple docstring'''






       if self._mask_token is None:
            if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.' )
            return None
       return str(self._mask_token )





  @mask_token.setter
  def 						A  (		self      :	Dict							,					_a      :	int )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else value
       _SCREAMING_SNAKE_CASE							    =value





  def 						A  (		self      :	List[str]							,					*_a      :	str							,					**_a      :	Optional[Any] )	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.get('is_split_into_words'							,					_a )
       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_a							,					**_a )





  def 						A  (		self      :	Any							,					*_a      :	List[Any]							,					**_a      :	Union[str, Any] )	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.get('is_split_into_words'							,					_a )

       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_a							,					**_a )





  def 						A  (		self      :	List[str]							,					_a      :	str							,					_a      :	Optional[str] = None )	->    Tuple[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._tokenizer.model.save(_a							,					name=_a )
       return tuple(_a )





  def 						A  (		self      :	int							,					_a      :	int							,					_a      :	Optional[int]=None )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def 						A  (		self      :	Tuple							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.sep_token_id]
       _SCREAMING_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 + sep + token_ids_a + sep ) * [0]



'''simple docstring'''
from typing import List, Optional, Union

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


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

lowerCamelCase :      str					 =     {
    "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__    (       A__  ):
  A__		 =				'time_series_transformer'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }





  def __init__(		self      :	Optional[int]							,					_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      :	Union[str, Any]=True							,					**_a      :	Optional[Any]							,					)	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prediction_length
       _SCREAMING_SNAKE_CASE							    =context_length or prediction_length
       _SCREAMING_SNAKE_CASE							    =distribution_output
       _SCREAMING_SNAKE_CASE							    =loss
       _SCREAMING_SNAKE_CASE							    =input_size
       _SCREAMING_SNAKE_CASE							    =num_time_features
       _SCREAMING_SNAKE_CASE							    =lags_sequence
       _SCREAMING_SNAKE_CASE							    =scaling
       _SCREAMING_SNAKE_CASE							    =num_dynamic_real_features
       _SCREAMING_SNAKE_CASE							    =num_static_real_features
       _SCREAMING_SNAKE_CASE							    =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`' )
            _SCREAMING_SNAKE_CASE							    =cardinality
       else:
            _SCREAMING_SNAKE_CASE							    =[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`' )
            _SCREAMING_SNAKE_CASE							    =embedding_dimension
       else:
            _SCREAMING_SNAKE_CASE							    =[min(50							,					(cat + 1) // 2 ) for cat in self.cardinality]
       _SCREAMING_SNAKE_CASE							    =num_parallel_samples

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

       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =decoder_layerdrop

       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std

       _SCREAMING_SNAKE_CASE							    =use_cache

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





  @property
  def 						A  (		self      :	List[Any] )	->    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
       )
