

from typing import List, Optional

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


_UpperCAmelCase        =				logging.get_logger(__name__)

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



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   """autoformer"""
       _UpperCamelCase				:					int         =   {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
           """num_hidden_layers""": """encoder_layers""",
       }

       def __init__(		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[int] = None					,   _SCREAMING_SNAKE_CASE: Optional[int] = None					,   _SCREAMING_SNAKE_CASE: str = "student_t"					,   _SCREAMING_SNAKE_CASE: str = "nll"					,   _SCREAMING_SNAKE_CASE: int = 1					,   _SCREAMING_SNAKE_CASE: List[int] = [1, 2, 3, 4, 5, 6, 7]					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: int = 0					,   _SCREAMING_SNAKE_CASE: int = 0					,   _SCREAMING_SNAKE_CASE: int = 0					,   _SCREAMING_SNAKE_CASE: int = 0					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: int = 64					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 32					,   _SCREAMING_SNAKE_CASE: int = 32					,   _SCREAMING_SNAKE_CASE: str = "gelu"					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: int = 100					,   _SCREAMING_SNAKE_CASE: float = 0.02					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: int = 10					,   _SCREAMING_SNAKE_CASE: int = 25					,   _SCREAMING_SNAKE_CASE: int = 3					,   **_SCREAMING_SNAKE_CASE: Dict					,   )       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  prediction_length
              UpperCamelCase_          =  context_length if context_length is not None else prediction_length
              UpperCamelCase_          =  distribution_output
              UpperCamelCase_          =  loss
              UpperCamelCase_          =  input_size
              UpperCamelCase_          =  num_time_features
              UpperCamelCase_          =  lags_sequence
              UpperCamelCase_          =  scaling
              UpperCamelCase_          =  num_dynamic_real_features
              UpperCamelCase_          =  num_static_real_features
              UpperCamelCase_          =  num_static_categorical_features
              if cardinality is not None and num_static_categorical_features > 0:
                     if len(_SCREAMING_SNAKE_CASE		) != num_static_categorical_features:
                            raise ValueError(
                                "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
                     UpperCamelCase_          =  cardinality
              else:
                     UpperCamelCase_          =  [0]
              if embedding_dimension is not None and num_static_categorical_features > 0:
                     if len(_SCREAMING_SNAKE_CASE		) != num_static_categorical_features:
                            raise ValueError(
                                "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
                     UpperCamelCase_          =  embedding_dimension
              else:
                     UpperCamelCase_          =  [min(50					,   (cat + 1) // 2		) for cat in self.cardinality]
              UpperCamelCase_          =  num_parallel_samples

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

              UpperCamelCase_          =  dropout
              UpperCamelCase_          =  attention_dropout
              UpperCamelCase_          =  activation_dropout
              UpperCamelCase_          =  encoder_layerdrop
              UpperCamelCase_          =  decoder_layerdrop

              UpperCamelCase_          =  activation_function
              UpperCamelCase_          =  init_std

              UpperCamelCase_          =  use_cache

              # Autoformer
              UpperCamelCase_          =  label_length
              UpperCamelCase_          =  moving_average
              UpperCamelCase_          =  autocorrelation_factor

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



       @property
       def lowercase (		self: List[str]		)       -> 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
              )








from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCAmelCase        =				{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCAmelCase        =				{
    'facebook/blenderbot_small-90M': 5_1_2,
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					str         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					str         =   BlenderbotSmallTokenizer

       def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=None					,   _SCREAMING_SNAKE_CASE: List[str]=None					,   _SCREAMING_SNAKE_CASE: Optional[int]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[Any]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[str]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: int=True					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> int:




              """simple docstring"""


              super().__init__(
                  ByteLevelBPETokenizer(
                      vocab=_SCREAMING_SNAKE_CASE					,   merges=_SCREAMING_SNAKE_CASE					,   add_prefix_space=_SCREAMING_SNAKE_CASE					,   trim_offsets=_SCREAMING_SNAKE_CASE					,   )					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  add_prefix_space

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Any=None		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  [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 lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [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]

