"""simple docstring"""







from typing import List, Optional, Union

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


_a					:					Any=			logging.get_logger(__name__)

_a					:					List[Any]=			{
    "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      (   lowercase						):
				UpperCAmelCase    :				str   =		"""time_series_transformer"""
				UpperCAmelCase    :				List[Any]   =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}



				def __init__(self    :				Dict     ,  _A    :				Optional[int] = None     ,  _A    :				Optional[int] = None     ,  _A    :				str = "student_t"     ,  _A    :				str = "nll"     ,  _A    :				int = 1     ,  _A    :				List[int] = [1, 2, 3, 4, 5, 6, 7]     ,  _A    :				Optional[Union[str, bool]] = "mean"     ,  _A    :				int = 0     ,  _A    :				int = 0     ,  _A    :				int = 0     ,  _A    :				int = 0     ,  _A    :				Optional[List[int]] = None     ,  _A    :				Optional[List[int]] = None     ,  _A    :				int = 32     ,  _A    :				int = 32     ,  _A    :				int = 2     ,  _A    :				int = 2     ,  _A    :				int = 2     ,  _A    :				int = 2     ,  _A    :				bool = True     ,  _A    :				str = "gelu"     ,  _A    :				int = 64     ,  _A    :				float = 0.1     ,  _A    :				float = 0.1     ,  _A    :				float = 0.1     ,  _A    :				float = 0.1     ,  _A    :				float = 0.1     ,  _A    :				int = 1_00     ,  _A    :				float = 0.02     ,  _A    :				str=True     ,  **_A    :				List[Any]     ,  )   ->     int:
											# time series specific configuration
											__snake_case      :							int						=			prediction_length
											__snake_case      :							int						=			context_length or prediction_length
											__snake_case      :							Dict						=			distribution_output
											__snake_case      :							Tuple						=			loss
											__snake_case      :							Any						=			input_size
											__snake_case      :							Tuple						=			num_time_features
											__snake_case      :							List[Any]						=			lags_sequence
											__snake_case      :							Optional[Any]						=			scaling
											__snake_case      :							Any						=			num_dynamic_real_features
											__snake_case      :							List[Any]						=			num_static_real_features
											__snake_case      :							Tuple						=			num_static_categorical_features
											if cardinality 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      :							Union[str, Any]						=			[0]
											if embedding_dimension 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      :							List[Any]						=			embedding_dimension
											else:
																		__snake_case      :							List[Any]						=			[min(50     ,  (cat + 1) // 2) for cat in self.cardinality]
											__snake_case      :							int						=			num_parallel_samples

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

											__snake_case      :							str						=			dropout
											__snake_case      :							Optional[Any]						=			attention_dropout
											__snake_case      :							Union[str, Any]						=			activation_dropout
											__snake_case      :							List[Any]						=			encoder_layerdrop
											__snake_case      :							Any						=			decoder_layerdrop

											__snake_case      :							str						=			activation_function
											__snake_case      :							List[str]						=			init_std

											__snake_case      :							int						=			use_cache

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






				@property
				def  _lowercase					(self    :				Optional[int])   ->     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
											)






"""simple docstring"""







import numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''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   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''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   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			0
	for i in range(UpperCAmelCase_  ):
		if index == -1:
			summation_value += _error(UpperCAmelCase_  )
		else:
			summation_value += _error(UpperCAmelCase_  ) * train_data[i][0][index]
	return summation_value
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





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


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





