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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[Any]      =			'informer'
   __UpperCAmelCase : List[Any]      =			{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
       'num_hidden_layers': 'encoder_layers',
   }


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						str = "student_t"       ,  __UpperCAmelCase     :						str = "nll"       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						List[int] = None       ,  __UpperCAmelCase     :						Optional[Union[str, bool]] = "mean"       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						int = 6_4       ,  __UpperCAmelCase     :						int = 3_2       ,  __UpperCAmelCase     :						int = 3_2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						str = "gelu"       ,  __UpperCAmelCase     :						float = 0.05       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						int = 1_0_0       ,  __UpperCAmelCase     :						float = 0.02       ,  __UpperCAmelCase     :						Any=True       ,  __UpperCAmelCase     :						str = "prob"       ,  __UpperCAmelCase     :						int = 5       ,  __UpperCAmelCase     :						bool = True       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				Dict:
         """simple docstring"""



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

         # set cardinality
         if cardinality and num_static_categorical_features > 0:
               if len(__UpperCAmelCase		) != 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]

         # set embedding_dimension
         if embedding_dimension and num_static_categorical_features > 0:
               if len(__UpperCAmelCase		) != 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(5_0       ,  (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

         # Informer
         UpperCAmelCase__								=  attention_type
         UpperCAmelCase__								=  sampling_factor
         UpperCAmelCase__								=  distil

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





   @property
   def 			lowercase_      (self     :						Union[str, 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
         )




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      UpperCamelCase__											=					np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

      # Create two fuzzy sets by defining any membership function
      # (trapmf(), gbellmf(), gaussmf(), etc).
      UpperCamelCase__											=					[0, 2_5, 5_0]
      UpperCamelCase__											=					[2_5, 5_0, 7_5]
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      UpperCamelCase__											=					np.ones(7_5)
      UpperCamelCase__											=					np.zeros((7_5,))
      # 1. Union = max(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      UpperCamelCase__											=					fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      UpperCamelCase__											=					young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      UpperCamelCase__											=					young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

      # max-min composition
      # max-product composition

      # Plot each set A, set B and each operation result using plot() and subplot().
      from matplotlib import pyplot as plt

      plt.figure()

      plt.subplot(4, 3, 1)
      plt.plot(X, young)
      plt.title('Young')
      plt.grid(True)

      plt.subplot(4, 3, 2)
      plt.plot(X, middle_aged)
      plt.title('Middle aged')
      plt.grid(True)

      plt.subplot(4, 3, 3)
      plt.plot(X, union)
      plt.title('union')
      plt.grid(True)

      plt.subplot(4, 3, 4)
      plt.plot(X, intersection)
      plt.title('intersection')
      plt.grid(True)

      plt.subplot(4, 3, 5)
      plt.plot(X, complement_a)
      plt.title('complement_a')
      plt.grid(True)

      plt.subplot(4, 3, 6)
      plt.plot(X, difference)
      plt.title('difference a/b')
      plt.grid(True)

      plt.subplot(4, 3, 7)
      plt.plot(X, alg_sum)
      plt.title('alg_sum')
      plt.grid(True)

      plt.subplot(4, 3, 8)
      plt.plot(X, alg_product)
      plt.title('alg_product')
      plt.grid(True)

      plt.subplot(4, 3, 9)
      plt.plot(X, bdd_sum)
      plt.title('bdd_sum')
      plt.grid(True)

      plt.subplot(4, 3, 1_0)
      plt.plot(X, bdd_difference)
      plt.title('bdd_difference')
      plt.grid(True)

      plt.subplots_adjust(hspace=0.5)
      plt.show()



