

import numpy

# List of input, output pairs
A__						=							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
A__						=							(((515, 22, 13), 555), ((61, 35, 49), 150))
A__						=							[2, 4, 1, 5]
A__						=							len(train_data)
A__						=							0.009




def 					_lowercase     (				a_				:					Dict					,a_				:					List[Any]="train"				)				->	int:




					'''simple docstring'''







					return calculate_hypothesis_value(_lowerCamelCase					,_lowerCamelCase				) - output(
					    _lowerCamelCase					,_lowerCamelCase				)




def 					_lowercase     (				a_				:					Optional[int]				)				->	Dict:




					'''simple docstring'''







					__magic_name__							=    0
					for i in range(len(_lowerCamelCase				) - 1				):
										hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
					hyp_val += parameter_vector[0]
					return hyp_val




def 					_lowercase     (				a_				:					List[str]					,a_				:					Tuple				)				->	Optional[int]:




					'''simple docstring'''







					if data_set == "train":
										return train_data[example_no][1]
					elif data_set == "test":
										return test_data[example_no][1]
					return None




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					List[str]				)				->	Optional[int]:




					'''simple docstring'''







					if data_set == "train":
										return _hypothesis_value(train_data[example_no][0]				)
					elif data_set == "test":
										return _hypothesis_value(test_data[example_no][0]				)
					return None




def 					_lowercase     (				a_				:					Optional[int]					,a_				:					Union[str, Any]=m				)				->	Optional[int]:




					'''simple docstring'''







					__magic_name__							=    0
					for i in range(_lowerCamelCase				):
										if index == -1:
															summation_value += _error(_lowerCamelCase				)
										else:
															summation_value += _error(_lowerCamelCase				) * train_data[i][0][index]
					return summation_value




def 					_lowercase     (				a_				:					List[str]				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    summation_of_cost_derivative(_lowerCamelCase					,_lowerCamelCase				) / m
					return cost_derivative_value




def 					_lowercase     (				)				->	int:




					'''simple docstring'''







					global parameter_vector
					# Tune these values to set a tolerance value for predicted output
					__magic_name__							=    0.000002
					__magic_name__							=    0
					__magic_name__							=    0
					while True:
										j += 1
										__magic_name__							=    [0, 0, 0, 0]
										for i in range(0					,len(_lowerCamelCase				)				):
															__magic_name__							=    get_cost_derivative(i - 1				)
															__magic_name__							=    (
															    parameter_vector[i] - LEARNING_RATE * cost_derivative
															)
										if numpy.allclose(
										    _lowerCamelCase					,_lowerCamelCase					,atol=_lowerCamelCase					,rtol=_lowerCamelCase					,):
															break
										__magic_name__							=    temp_parameter_vector
					print(('Number of iterations:', j)				)




def 					_lowercase     (				)				->	Dict:




					'''simple docstring'''







					for i in range(len(_lowerCamelCase				)				):
										print(('Actual output value:', output(_lowerCamelCase					,'test'				))				)
										print(('Hypothesis output:', calculate_hypothesis_value(_lowerCamelCase					,'test'				))				)


if __name__ == "__main__":
						run_gradient_descent()
						print("\nTesting gradient descent for a linear hypothesis function.\n")
						test_gradient_descent()







from typing import List, Optional, Union

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


A__						=							logging.get_logger(__name__)

A__						=							{
    "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				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "time_series_transformer"
	_lowercase  :   Dict       				=  {
	    "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: Optional[Union[str, bool]] = "mean"  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: bool = True  , __UpperCamelCase: str = "gelu"  , __UpperCamelCase: int = 64  , __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: Any=True  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    prediction_length
						__magic_name__							=    context_length or prediction_length
						__magic_name__							=    distribution_output
						__magic_name__							=    loss
						__magic_name__							=    input_size
						__magic_name__							=    num_time_features
						__magic_name__							=    lags_sequence
						__magic_name__							=    scaling
						__magic_name__							=    num_dynamic_real_features
						__magic_name__							=    num_static_real_features
						__magic_name__							=    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`'      )
											__magic_name__							=    cardinality
						else:
											__magic_name__							=    [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`'      )
											__magic_name__							=    embedding_dimension
						else:
											__magic_name__							=    [min(50  , (cat + 1) // 2      ) for cat in self.cardinality]
						__magic_name__							=    num_parallel_samples

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

						__magic_name__							=    dropout
						__magic_name__							=    attention_dropout
						__magic_name__							=    activation_dropout
						__magic_name__							=    encoder_layerdrop
						__magic_name__							=    decoder_layerdrop

						__magic_name__							=    activation_function
						__magic_name__							=    init_std

						__magic_name__							=    use_cache

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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''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
						)




