




"""simple docstring"""


from typing import List, Optional

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


_A													=      logging.get_logger(__name__)

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


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     "autoformer"
   _lowerCamelCase    :Tuple     				=     {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }






   def __init__(		self       :		str   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		str = "student_t"   ,     UpperCamelCase       :		str = "nll"   ,     UpperCamelCase       :		int = 1   ,     UpperCamelCase       :		List[int] = [1, 2, 3, 4, 5, 6, 7]   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		Optional[List[int]] = None   ,     UpperCamelCase       :		Optional[List[int]] = None   ,     UpperCamelCase       :		int = 64   ,     UpperCamelCase       :		int = 2   ,     UpperCamelCase       :		int = 2   ,     UpperCamelCase       :		int = 2   ,     UpperCamelCase       :		int = 2   ,     UpperCamelCase       :		int = 32   ,     UpperCamelCase       :		int = 32   ,     UpperCamelCase       :		str = "gelu"   ,     UpperCamelCase       :		float = 0.1   ,     UpperCamelCase       :		float = 0.1   ,     UpperCamelCase       :		float = 0.1   ,     UpperCamelCase       :		float = 0.1   ,     UpperCamelCase       :		float = 0.1   ,     UpperCamelCase       :		int = 1_00   ,     UpperCamelCase       :		float = 0.02   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Any=True   ,     UpperCamelCase       :		int = 10   ,     UpperCamelCase       :		int = 25   ,     UpperCamelCase       :		int = 3   ,     **UpperCamelCase       :		List[Any]   ,     )			->						List[Any]:

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

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

         lowerCAmelCase__      :			Optional[Any]   =	dropout
         lowerCAmelCase__      :			Optional[int]   =	attention_dropout
         lowerCAmelCase__      :			Optional[int]   =	activation_dropout
         lowerCAmelCase__      :			Optional[Any]   =	encoder_layerdrop
         lowerCAmelCase__      :			Dict   =	decoder_layerdrop

         lowerCAmelCase__      :			Tuple   =	activation_function
         lowerCAmelCase__      :			List[Any]   =	init_std

         lowerCAmelCase__      :			Union[str, Any]   =	use_cache

         # Autoformer
         lowerCAmelCase__      :			int   =	label_length
         lowerCAmelCase__      :			Optional[int]   =	moving_average
         lowerCAmelCase__      :			List[Any]   =	autocorrelation_factor

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






   @property
   def 	_lowerCAmelCase   (		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
         )











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

    # print entropy
      print(f"""{round(-1 * my_fir_sum				):.1f}"""				)

      # two len string
      lowerCAmelCase__      :			Tuple   =	sum(two_char_strings.values()				)
      lowerCAmelCase__      :			str   =	0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  lowerCAmelCase__      :			Optional[int]   =	cha + cha
                  if sequence in two_char_strings:
                        lowerCAmelCase__      :			int   =	two_char_strings[sequence]
                        lowerCAmelCase__      :			str   =	int(__UpperCAmelCase				) / all_sum
                        my_sec_sum += prob * math.loga(__UpperCAmelCase				)

    # print second entropy
      print(f"""{round(-1 * my_sec_sum				):.1f}"""				)

      # print the difference between them
      print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)				):.1f}"""				)



def 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0    ,		len(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    Any:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
  main()





