"""simple docstring"""




from collections.abc import Generator
from math import sin






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if len(UpperCamelCase__ ) != 32:
														raise ValueError('Input must be of length 32' )

							A__		   =							b''
							for i in [3, 2, 1, 0]:
														little_endian += string_aa[8 * i : 8 * i + 8]
							return little_endian






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if i < 0:
														raise ValueError('Input must be non-negative' )

							A__		   =							format(UpperCamelCase__				,			'08x' )[-8:]
							A__		   =							b''
							for i in [3, 2, 1, 0]:
														little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
							return little_endian_hex






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b''
							for char in message:
														bit_string += format(UpperCamelCase__				,			'08b' ).encode('utf-8' )
							A__		   =							format(len(UpperCamelCase__ )				,			'064b' ).encode('utf-8' )

							# Pad bit_string to a multiple of 512 chars
							bit_string += b"1"
							while len(UpperCamelCase__ ) % 512 != 448:
														bit_string += b"0"
							bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

							return bit_string






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if len(UpperCamelCase__ ) % 512 != 0:
														raise ValueError('Input must have length that\'s a multiple of 512' )

							for pos in range(0				,			len(UpperCamelCase__ )				,			512 ):
														A__		   =							bit_string[pos : pos + 512]
														A__		   =							[]
														for i in range(0				,			512				,			32 ):
																					block_words.append(int(to_little_endian(block[i : i + 32] )				,			2 ) )
														yield block_words






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if i < 0:
														raise ValueError('Input must be non-negative' )

							A__		   =							format(UpperCamelCase__				,			'032b' )
							A__		   =							''
							for c in i_str:
														new_str += "1" if c == "0" else "0"
							return int(UpperCamelCase__				,			2 )






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							return (a + b) % 2**32






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							if i < 0:
														raise ValueError('Input must be non-negative' )
							if shift < 0:
														raise ValueError('Shift must be non-negative' )
							return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							preprocess(UpperCamelCase__ )

							A__		   =							[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

							# Starting states
							A__		   =							0x6_7_4_5_2_3_0_1
							A__		   =							0xe_f_c_d_a_b_8_9
							A__		   =							0x9_8_b_a_d_c_f_e
							A__		   =							0x1_0_3_2_5_4_7_6

							A__		   =							[
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							]

							# Process bit string in chunks, each with 16 32-char words
							for block_words in get_block_words(UpperCamelCase__ ):
														A__		   =							aa
														A__		   =							ba
														A__		   =							ca
														A__		   =							da

														# Hash current chunk
														for i in range(64 ):
																					if i <= 15:
																												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																												A__		   =							d ^ (b & (c ^ d))
																												A__		   =							i
																					elif i <= 31:
																												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																												A__		   =							c ^ (d & (b ^ c))
																												A__		   =							(5 * i + 1) % 16
																					elif i <= 47:
																												A__		   =							b ^ c ^ d
																												A__		   =							(3 * i + 5) % 16
																					else:
																												A__		   =							c ^ (b | not_aa(UpperCamelCase__ ))
																												A__		   =							(7 * i) % 16
																					A__		   =							(f + a + added_consts[i] + block_words[g]) % 2**32
																					A__		   =							d
																					A__		   =							c
																					A__		   =							b
																					A__		   =							sum_aa(UpperCamelCase__				,			left_rotate_aa(UpperCamelCase__				,			shift_amounts[i] ) )

														# Add hashed chunk to running total
														A__		   =							sum_aa(UpperCamelCase__				,			UpperCamelCase__ )
														A__		   =							sum_aa(UpperCamelCase__				,			UpperCamelCase__ )
														A__		   =							sum_aa(UpperCamelCase__				,			UpperCamelCase__ )
														A__		   =							sum_aa(UpperCamelCase__				,			UpperCamelCase__ )

							A__		   =							reformat_hex(UpperCamelCase__ ) + reformat_hex(UpperCamelCase__ ) + reformat_hex(UpperCamelCase__ ) + reformat_hex(UpperCamelCase__ )
							return digest


if __name__ == "__main__":
				import doctest

				doctest.testmod()






"""simple docstring"""




from typing import List, Optional, Union

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						'time_series_transformer'
						lowerCAmelCase__     :  Dict  							=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = "student_t"						,__UpperCAmelCase = "nll"						,__UpperCAmelCase = 1						,__UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]						,__UpperCAmelCase = "mean"						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 32						,__UpperCAmelCase = 32						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = True						,__UpperCAmelCase = "gelu"						,__UpperCAmelCase = 64						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 1_00						,__UpperCAmelCase = 0.0_2						,__UpperCAmelCase=True						,**__UpperCAmelCase						,)    -> List[str]:
													# time series specific configuration
													A__		   =							prediction_length
													A__		   =							context_length or prediction_length
													A__		   =							distribution_output
													A__		   =							loss
													A__		   =							input_size
													A__		   =							num_time_features
													A__		   =							lags_sequence
													A__		   =							scaling
													A__		   =							num_dynamic_real_features
													A__		   =							num_static_real_features
													A__		   =							num_static_categorical_features
													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`'   )
																				A__		   =							cardinality
													else:
																				A__		   =							[0]
													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`'   )
																				A__		   =							embedding_dimension
													else:
																				A__		   =							[min(50						,(cat + 1) // 2   ) for cat in self.cardinality]
													A__		   =							num_parallel_samples

													# Transformer architecture configuration
													A__		   =							input_size * len(__UpperCAmelCase   ) + self._number_of_features
													A__		   =							d_model
													A__		   =							encoder_attention_heads
													A__		   =							decoder_attention_heads
													A__		   =							encoder_ffn_dim
													A__		   =							decoder_ffn_dim
													A__		   =							encoder_layers
													A__		   =							decoder_layers

													A__		   =							dropout
													A__		   =							attention_dropout
													A__		   =							activation_dropout
													A__		   =							encoder_layerdrop
													A__		   =							decoder_layerdrop

													A__		   =							activation_function
													A__		   =							init_std

													A__		   =							use_cache

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





						@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
													)





