

'''simple docstring'''





import math
import sys






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  str:
						lowercase :							Tuple    		=''''''
						try:
												with open(__magic_name__   , '''rb'''						) as binary_file:
																		lowercase :							Dict    		=binary_file.read()
												for dat in data:
																		lowercase :							Any    		=f'''{dat:08b}'''
																		result += curr_byte
												return result
						except OSError:
												print('''File not accessible'''						)
												sys.exit()






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  str:
						lowercase :							int    		={'''0''': '''0''', '''1''': '''1'''}
						lowercase		,	lowercase :							List[str]    		='''''', ''''''
						lowercase :							Union[str, Any]    		=len(__magic_name__						)

						for i in range(len(__magic_name__						)						):
												curr_string += data_bits[i]
												if curr_string not in lexicon:
																		continue

												lowercase :							int    		=lexicon[curr_string]
												result += last_match_id
												lowercase :							List[str]    		=last_match_id + '''0'''

												if math.loga(__magic_name__						).is_integer():
																		lowercase :							int    		={}
																		for curr_key in list(__magic_name__						):
																								lowercase :							int    		=lexicon.pop(__magic_name__						)
																		lowercase :							Any    		=new_lex

												lowercase :							Any    		=last_match_id + '''1'''
												index += 1
												lowercase :							int    		=''''''
						return result






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  None:
						lowercase :							Optional[Any]    		=8
						try:
												with open(__magic_name__   , '''wb'''						) as opened_file:
																		lowercase :							Any    		=[
																		    to_write[i : i + byte_length]
																		    for i in range(0   , len(__magic_name__						)   , __magic_name__						)
																		]

																		if len(result_byte_array[-1]						) % byte_length == 0:
																								result_byte_array.append('''10000000'''						)
																		else:
																								result_byte_array[-1] += "1" + "0" * (
																								    byte_length - len(result_byte_array[-1]						) - 1
																								)

																		for elem in result_byte_array[:-1]:
																								opened_file.write(int(__magic_name__   , 2						).to_bytes(1   , byteorder='''big'''						)						)
						except OSError:
												print('''File not accessible'''						)
												sys.exit()






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  str:
						lowercase :							int    		=0
						for letter in data_bits:
												if letter == "1":
																		break
												counter += 1

						lowercase :							List[str]    		=data_bits[counter:]
						lowercase :							List[Any]    		=data_bits[counter + 1 :]
						return data_bits






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  None:
						lowercase :							Optional[int]    		=read_file_binary(__magic_name__						)
						lowercase :							Dict    		=remove_prefix(__magic_name__						)
						lowercase :							Optional[Any]    		=decompress_data(__magic_name__						)
						write_file_binary(__magic_name__   , __magic_name__						)


if __name__ == "__main__":
							compress(sys.argv[1], sys.argv[2])











'''simple docstring'''







from typing import List, Optional

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


lowercase      :   Optional[Any]      =       logging.get_logger(__name__)

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







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[str]          =					"""autoformer"""
     lowerCAmelCase_  :     int          =					{
         """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							,)      ->				Union[str, Any]:
          # time series specific configuration
          snake_case			:      List[str]        =			prediction_length
          snake_case			:      str        =			context_length if context_length is not None else prediction_length
          snake_case			:      List[Any]        =			distribution_output
          snake_case			:      Any        =			loss
          snake_case			:      Union[str, Any]        =			input_size
          snake_case			:      Optional[int]        =			num_time_features
          snake_case			:      Union[str, Any]        =			lags_sequence
          snake_case			:      Any        =			scaling
          snake_case			:      str        =			num_dynamic_real_features
          snake_case			:      Union[str, Any]        =			num_static_real_features
          snake_case			:      Optional[int]        =			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`"""							)
               snake_case			:      int        =			cardinality
          else:
               snake_case			:      Optional[Any]        =			[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`"""							)
               snake_case			:      int        =			embedding_dimension
          else:
               snake_case			:      List[str]        =			[min(50							,(cat + 1) // 2							) for cat in self.cardinality]
          snake_case			:      Optional[int]        =			num_parallel_samples

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

          snake_case			:      str        =			dropout
          snake_case			:      Union[str, Any]        =			attention_dropout
          snake_case			:      Optional[Any]        =			activation_dropout
          snake_case			:      Tuple        =			encoder_layerdrop
          snake_case			:      Any        =			decoder_layerdrop

          snake_case			:      Any        =			activation_function
          snake_case			:      int        =			init_std

          snake_case			:      Optional[Any]        =			use_cache

          # Autoformer
          snake_case			:      int        =			label_length
          snake_case			:      str        =			moving_average
          snake_case			:      Tuple        =			autocorrelation_factor

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







     @property
     def 			snake_case_      (				self							)      ->				int:
          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
          )



