

"""simple docstring"""


from typing import List, Optional

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


a					:Dict    = logging.get_logger(__name__)

a					:str    = {
    "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """autoformer"""
     _SCREAMING_SNAKE_CASE						:Tuple       = {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
         """num_hidden_layers""": """encoder_layers""",
     }

     def __init__(						self	,    _a = None	,    _a = None	,    _a = "student_t"	,    _a = "nll"	,    _a = 1	,    _a = [1, 2, 3, 4, 5, 6, 7]	,    _a = True	,    _a = 0	,    _a = 0	,    _a = 0	,    _a = 0	,    _a = None	,    _a = None	,    _a = 64	,    _a = 2	,    _a = 2	,    _a = 2	,    _a = 2	,    _a = 32	,    _a = 32	,    _a = "gelu"	,    _a = 0.1	,    _a = 0.1	,    _a = 0.1	,    _a = 0.1	,    _a = 0.1	,    _a = 100	,    _a = 0.02	,    _a = True	,    _a=True	,    _a = 10	,    _a = 25	,    _a = 3	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	prediction_length
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	context_length if context_length is not None else prediction_length
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	distribution_output
           SCREAMING_SNAKE_CASE__   :      str      		=	loss
           SCREAMING_SNAKE_CASE__   :      str      		=	input_size
           SCREAMING_SNAKE_CASE__   :      str      		=	num_time_features
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	lags_sequence
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scaling
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_dynamic_real_features
           SCREAMING_SNAKE_CASE__   :      int      		=	num_static_real_features
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_static_categorical_features
           if cardinality is not None 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__   :      Optional[Any]      		=	cardinality
           else:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	[0]
           if embedding_dimension is not None 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__   :      Union[str, Any]      		=	embedding_dimension
           else:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	[min(50	,    (cat + 1) // 2  ) for cat in self.cardinality]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	num_parallel_samples

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

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

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	activation_function
           SCREAMING_SNAKE_CASE__   :      Dict      		=	init_std

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_cache

           # Autoformer
           SCREAMING_SNAKE_CASE__   :      Dict      		=	label_length
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	moving_average
           SCREAMING_SNAKE_CASE__   :      Any      		=	autocorrelation_factor

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





     @property
     def 					_a			(						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
           )







"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bool:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							) + 1
      SCREAMING_SNAKE_CASE__   :      int      		=	len(__lowerCAmelCase							) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[[0 for i in range(__lowerCAmelCase							)] for j in range(__lowerCAmelCase							)]

      # since string of zero length match pattern of zero length
      SCREAMING_SNAKE_CASE__   :      Dict      		=	1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      int      		=	dp[0][j - 2] if pattern[j - 1] == """*""" else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1  ,					__lowerCAmelCase							):
            for j in range(1  ,					__lowerCAmelCase							):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        SCREAMING_SNAKE_CASE__   :      Any      		=	dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              SCREAMING_SNAKE_CASE__   :      List[str]      		=	1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dp[i - 1][j]
                        else:
                              SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
                  else:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	0

      return bool(dp[-1][-1]							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # inputing the strings
  # input_string = input("input a string :")
  # pattern = input("input a pattern :")

  a					:Any    = "aab"
  a					:Optional[Any]    = "c*a*b"

  # using function to check whether given string matches the given pattern
  if match_pattern(input_string, pattern):
    print(f'{input_string} matches the given pattern {pattern}')
  else:
    print(f'{input_string} does not match with the given pattern {pattern}')




