





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Any   		=		"deberta-v2"




   def __init__(    self,		__magic_name__=128100,		__magic_name__=1536,		__magic_name__=24,		__magic_name__=24,		__magic_name__=6144,		__magic_name__="gelu",		__magic_name__=0.1,		__magic_name__=0.1,		__magic_name__=512,		__magic_name__=0,		__magic_name__=0.02,		__magic_name__=1E-7,		__magic_name__=False,		__magic_name__=-1,		__magic_name__=0,		__magic_name__=True,		__magic_name__=None,		__magic_name__=0,		__magic_name__="gelu",		**__magic_name__,		) ->      Union[str, Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       UpperCamelCase__							:				Dict          =      hidden_size
       UpperCamelCase__							:				Dict          =      num_hidden_layers
       UpperCamelCase__							:				Any          =      num_attention_heads
       UpperCamelCase__							:				List[Any]          =      intermediate_size
       UpperCamelCase__							:				int          =      hidden_act
       UpperCamelCase__							:				str          =      hidden_dropout_prob
       UpperCamelCase__							:				str          =      attention_probs_dropout_prob
       UpperCamelCase__							:				Optional[int]          =      max_position_embeddings
       UpperCamelCase__							:				List[Any]          =      type_vocab_size
       UpperCamelCase__							:				Dict          =      initializer_range
       UpperCamelCase__							:				Optional[int]          =      relative_attention
       UpperCamelCase__							:				int          =      max_relative_positions
       UpperCamelCase__							:				str          =      pad_token_id
       UpperCamelCase__							:				Tuple          =      position_biased_input

       # Backwards compatibility
       if type(__magic_name__							) == str:
           UpperCamelCase__							:				str          =      [x.strip() for x in pos_att_type.lower().split('''|'''							)]

       UpperCamelCase__							:				Tuple          =      pos_att_type
       UpperCamelCase__							:				Optional[Any]          =      vocab_size
       UpperCamelCase__							:				int          =      layer_norm_eps

       UpperCamelCase__							:				int          =      kwargs.get('''pooler_hidden_size''',		__magic_name__							)
       UpperCamelCase__							:				str          =      pooler_dropout
       UpperCamelCase__							:				Optional[Any]          =      pooler_hidden_act






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task == "multiple-choice":
           UpperCamelCase__							:				Dict          =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
           UpperCamelCase__							:				Any          =      {0: '''batch''', 1: '''sequence'''}
       if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]							)
       else:
           return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]							)




   @property
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       return 12




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = -1,		__magic_name__ = -1,		__magic_name__ = -1,		__magic_name__ = False,		__magic_name__ = None,		__magic_name__ = 3,		__magic_name__ = 40,		__magic_name__ = 40,		__magic_name__ = None,		) ->      Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				str          =      super().generate_dummy_inputs(preprocessor=__magic_name__,		framework=__magic_name__							)
       if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
       return dummy_inputs








from typing import List, Optional, Union

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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    '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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		"informer"
   a						:							Tuple   		=		{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }




   def __init__(    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = "student_t",		__magic_name__ = "nll",		__magic_name__ = 1,		__magic_name__ = None,		__magic_name__ = "mean",		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 64,		__magic_name__ = 32,		__magic_name__ = 32,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = True,		__magic_name__ = "gelu",		__magic_name__ = 0.05,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 100,		__magic_name__ = 0.02,		__magic_name__=True,		__magic_name__ = "prob",		__magic_name__ = 5,		__magic_name__ = True,		**__magic_name__,		) ->      Optional[int]:






       """simple docstring"""
       # time series specific configuration
       UpperCamelCase__							:				List[Any]          =      prediction_length
       UpperCamelCase__							:				Any          =      context_length or prediction_length
       UpperCamelCase__							:				Optional[int]          =      distribution_output
       UpperCamelCase__							:				Union[str, Any]          =      loss
       UpperCamelCase__							:				Optional[Any]          =      input_size
       UpperCamelCase__							:				Dict          =      num_time_features
       UpperCamelCase__							:				Dict          =      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       UpperCamelCase__							:				Optional[int]          =      scaling
       UpperCamelCase__							:				Any          =      num_dynamic_real_features
       UpperCamelCase__							:				Optional[int]          =      num_static_real_features
       UpperCamelCase__							:				Optional[Any]          =      num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The cardinality should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				str          =      cardinality
       else:
           UpperCamelCase__							:				int          =      [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				Any          =      embedding_dimension
       else:
           UpperCamelCase__							:				Union[str, Any]          =      [min(50,		(cat + 1) // 2							) for cat in self.cardinality]

       UpperCamelCase__							:				Optional[Any]          =      num_parallel_samples

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

       UpperCamelCase__							:				Optional[Any]          =      dropout
       UpperCamelCase__							:				List[Any]          =      attention_dropout
       UpperCamelCase__							:				Any          =      activation_dropout
       UpperCamelCase__							:				Optional[int]          =      encoder_layerdrop
       UpperCamelCase__							:				Union[str, Any]          =      decoder_layerdrop

       UpperCamelCase__							:				Tuple          =      activation_function
       UpperCamelCase__							:				List[str]          =      init_std

       UpperCamelCase__							:				int          =      use_cache

       # Informer
       UpperCamelCase__							:				Optional[int]          =      attention_type
       UpperCamelCase__							:				Optional[int]          =      sampling_factor
       UpperCamelCase__							:				Dict          =      distil

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




   @property
   def       UpperCamelCase__  (    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
       )

