





from typing import List, Optional, Union

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


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	'informer'
   _UpperCamelCase   :		List[Any]			 =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
       'num_hidden_layers': 'encoder_layers',
   }


   def __init__(   self      :    Dict      ,					a      :    Optional[int] = None      ,					a      :    Optional[int] = None      ,					a      :    str = "student_t"      ,					a      :    str = "nll"      ,					a      :    int = 1      ,					a      :    List[int] = None      ,					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 = 64      ,					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      :    float = 0.05      ,					a      :    float = 0.1      ,					a      :    float = 0.1      ,					a      :    float = 0.1      ,					a      :    float = 0.1      ,					a      :    int = 100      ,					a      :    float = 0.02      ,					a      :    int=True      ,					a      :    str = "prob"      ,					a      :    int = 5      ,					a      :    bool = True      ,					**a      :    List[str]      ,					)->       Any:





       """simple docstring"""







       lowercase__           =  prediction_length
       lowercase__           =  context_length or prediction_length
       lowercase__           =  distribution_output
       lowercase__           =  loss
       lowercase__           =  input_size
       lowercase__           =  num_time_features
       lowercase__           =  lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       lowercase__           =  scaling
       lowercase__           =  num_dynamic_real_features
       lowercase__           =  num_static_real_features
       lowercase__           =  num_static_categorical_features

       # set cardinality
       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`'	)
           lowercase__           =  cardinality
       else:
           lowercase__           =  [0]

       # set embedding_dimension
       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`'	)
           lowercase__           =  embedding_dimension
       else:
           lowercase__           =  [min(50      ,					(cat + 1) // 2	) for cat in self.cardinality]

       lowercase__           =  num_parallel_samples

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

       lowercase__           =  dropout
       lowercase__           =  attention_dropout
       lowercase__           =  activation_dropout
       lowercase__           =  encoder_layerdrop
       lowercase__           =  decoder_layerdrop

       lowercase__           =  activation_function
       lowercase__           =  init_std

       lowercase__           =  use_cache

       # Informer
       lowercase__           =  attention_type
       lowercase__           =  sampling_factor
       lowercase__           =  distil

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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       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
       )









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_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_          =		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Dict			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Dict			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Dict			 =	['input_ids', 'attention_mask']
   _UpperCamelCase   :		Optional[int]			 =	CamembertTokenizer


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





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     super().__init__(
         a      ,					tokenizer_file=a      ,					bos_token=a      ,					eos_token=a      ,					sep_token=a      ,					cls_token=a      ,					unk_token=a      ,					pad_token=a      ,					mask_token=a      ,					additional_special_tokens=a      ,					**a      ,					)

     lowercase__           =  vocab_file
     lowercase__           =  False if not self.vocab_file else True


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowercase__           =  [self.cls_token_id]
     lowercase__           =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'	)

     if not os.path.isdir(a	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
     lowercase__           =  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,)


