'''simple docstring'''


snake_case__					:      Union[str, Any]				      =  '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

snake_case__					:      Dict				      =  [{'type': 'code', 'content': INSTALL_CONTENT}]
snake_case__					:      str				      =  {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}







'''simple docstring'''


from typing import List, Optional

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


snake_case__					:      List[Any]				      =  logging.get_logger(__name__)

snake_case__					:      Optional[Any]				      =  {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		'''autoformer'''
  lowerCamelCase_		:Optional[Any]						   =		{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }


  def __init__(			self				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = "student_t"				,		snake_case_ = "nll"				,		snake_case_ = 1				,		snake_case_ = [1, 2, 3, 4, 5, 6, 7]				,		snake_case_ = True				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = 6_4				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 3_2				,		snake_case_ = 3_2				,		snake_case_ = "gelu"				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 1_0_0				,		snake_case_ = 0.02				,		snake_case_ = True				,		snake_case_=True				,		snake_case_ = 1_0				,		snake_case_ = 2_5				,		snake_case_ = 3				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				prediction_length
       UpperCAmelCase_							:       List[str]       =				context_length if context_length is not None else prediction_length
       UpperCAmelCase_							:       Optional[int]       =				distribution_output
       UpperCAmelCase_							:       Optional[int]       =				loss
       UpperCAmelCase_							:       Union[str, Any]       =				input_size
       UpperCAmelCase_							:       int       =				num_time_features
       UpperCAmelCase_							:       List[str]       =				lags_sequence
       UpperCAmelCase_							:       Any       =				scaling
       UpperCAmelCase_							:       Any       =				num_dynamic_real_features
       UpperCAmelCase_							:       int       =				num_static_real_features
       UpperCAmelCase_							:       Optional[Any]       =				num_static_categorical_features
       if cardinality is not None and num_static_categorical_features > 0:
            if len(snake_case_						) != num_static_categorical_features:
                 raise ValueError(
                     'The cardinality should be a list of the same length as `num_static_categorical_features`'						)
            UpperCAmelCase_							:       List[Any]       =				cardinality
       else:
            UpperCAmelCase_							:       Optional[int]       =				[0]
       if embedding_dimension is not None and num_static_categorical_features > 0:
            if len(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_							:       List[str]       =				embedding_dimension
       else:
            UpperCAmelCase_							:       List[Any]       =				[min(5_0				,		(cat + 1) // 2						) for cat in self.cardinality]
       UpperCAmelCase_							:       List[str]       =				num_parallel_samples

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

       UpperCAmelCase_							:       str       =				dropout
       UpperCAmelCase_							:       Optional[int]       =				attention_dropout
       UpperCAmelCase_							:       Tuple       =				activation_dropout
       UpperCAmelCase_							:       Any       =				encoder_layerdrop
       UpperCAmelCase_							:       Tuple       =				decoder_layerdrop

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

       UpperCAmelCase_							:       Union[str, Any]       =				use_cache

       # Autoformer
       UpperCAmelCase_							:       Any       =				label_length
       UpperCAmelCase_							:       Union[str, Any]       =				moving_average
       UpperCAmelCase_							:       Tuple       =				autocorrelation_factor

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






  @property
  def _UpperCamelCase					(			self						):




       '''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
       )






