



'''simple docstring'''

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase     		=   2_9979_2458

# Symbols
UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase     		=   symbols('ct x y z')






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							)      ->						float:







	"""simple docstring"""
	if velocity > c:
		raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""							)
	elif velocity < 1:
		# Usually the speed should be much higher than 1 (c order of magnitude)
		raise ValueError("""Speed must be greater than or equal to 1!"""							)

	return velocity / c






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							)      ->						float:







	"""simple docstring"""
	return 1 / sqrt(1 - beta(_SCREAMING_SNAKE_CASE							) ** 2							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							)      ->						np.ndarray:







	"""simple docstring"""
	return np.array(
	    [
	        [gamma(_SCREAMING_SNAKE_CASE							), -gamma(_SCREAMING_SNAKE_CASE							) * beta(_SCREAMING_SNAKE_CASE							), 0, 0],
	        [-gamma(_SCREAMING_SNAKE_CASE							) * beta(_SCREAMING_SNAKE_CASE							), gamma(_SCREAMING_SNAKE_CASE							), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						np.ndarray | None = None							)      ->						np.ndarray:







	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		lowerCAmelCase   =     np.array([ct, x, y, z]							)  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(_SCREAMING_SNAKE_CASE							) @ event


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# Example of symbolic vector:
			UpperCAmelCase     		=   transform(2997_9245)
			print('Example of four vector: ')
			print(F'''ct\' = {four_vector[0]}''')
			print(F'''x\' = {four_vector[1]}''')
			print(F'''y\' = {four_vector[2]}''')
			print(F'''z\' = {four_vector[3]}''')

			# Substitute symbols with numerical values
			UpperCAmelCase     		=   {ct: c, x: 1, y: 1, z: 1}
			UpperCAmelCase     		=   [four_vector[i].subs(sub_dict) for i in range(4)]

			print(F'''\n{numerical_vector}''')




'''simple docstring'''

from typing import List, Optional, Union

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    '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 __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[Any]									=		"time_series_transformer"
						UpperCAmelCase							:   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_ = "mean"     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = 32     ,	A_ = 32     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = True     ,	A_ = "gelu"     ,	A_ = 64     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 100     ,	A_ = 0.0_2     ,	A_=True     ,	**A_     ,	)     ->     Optional[Any]:
							# time series specific configuration
							lowerCAmelCase   =     prediction_length
							lowerCAmelCase   =     context_length or prediction_length
							lowerCAmelCase   =     distribution_output
							lowerCAmelCase   =     loss
							lowerCAmelCase   =     input_size
							lowerCAmelCase   =     num_time_features
							lowerCAmelCase   =     lags_sequence
							lowerCAmelCase   =     scaling
							lowerCAmelCase   =     num_dynamic_real_features
							lowerCAmelCase   =     num_static_real_features
							lowerCAmelCase   =     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`"""				)
								lowerCAmelCase   =     cardinality
							else:
								lowerCAmelCase   =     [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`"""				)
								lowerCAmelCase   =     embedding_dimension
							else:
								lowerCAmelCase   =     [min(50     ,	(cat + 1) // 2				) for cat in self.cardinality]
							lowerCAmelCase   =     num_parallel_samples

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

							lowerCAmelCase   =     dropout
							lowerCAmelCase   =     attention_dropout
							lowerCAmelCase   =     activation_dropout
							lowerCAmelCase   =     encoder_layerdrop
							lowerCAmelCase   =     decoder_layerdrop

							lowerCAmelCase   =     activation_function
							lowerCAmelCase   =     init_std

							lowerCAmelCase   =     use_cache

							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
							)