
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    def wrapper(*__lowerCAmelCase     ,		**__lowerCAmelCase				):
        snake_case__    =					timeit.default_timer()
        snake_case__    =					func(*__lowerCAmelCase     ,		**__lowerCAmelCase				)
        snake_case__    =					timeit.default_timer() - starttime
        return delta

    snake_case__    =					func.__name__

    return wrapper





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase=100     ,		__lowerCAmelCase=None				):
    snake_case__    =					[]
    snake_case__    =					seq_shapes or {}
    for i in range(__lowerCAmelCase				):
        snake_case__    =					{}
        for col_id, (k, v) in enumerate(features.items()				):
            if isinstance(__lowerCAmelCase     ,		_ArrayXD				):
                snake_case__    =					np.random.rand(*v.shape				).astype(v.dtype				)
            elif isinstance(__lowerCAmelCase     ,		datasets.Value				):
                if v.dtype == "string":
                    snake_case__    =					"The small grey turtle was surprisingly fast when challenged."
                else:
                    snake_case__    =					np.random.randint(10     ,		size=1				).astype(v.dtype				).item()
            elif isinstance(__lowerCAmelCase     ,		datasets.Sequence				):
                while isinstance(__lowerCAmelCase     ,		datasets.Sequence				):
                    snake_case__    =					v.feature
                snake_case__    =					seq_shapes[k]
                snake_case__    =					np.random.rand(*__lowerCAmelCase				).astype(v.dtype				)
            snake_case__    =					data

        dummy_data.append((i, example)				)

    return dummy_data





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=100     ,		__lowerCAmelCase=None				):
    snake_case__    =					generate_examples(__lowerCAmelCase     ,		num_examples=__lowerCAmelCase     ,		seq_shapes=__lowerCAmelCase				)

    with ArrowWriter(features=__lowerCAmelCase     ,		path=__lowerCAmelCase				) as writer:
        for key, record in dummy_data:
            snake_case__    =					features.encode_example(__lowerCAmelCase				)
            writer.write(__lowerCAmelCase				)

        snake_case__    ,       snake_case__    =					writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""				)

    snake_case__    =					datasets.Dataset.from_file(filename=__lowerCAmelCase     ,		info=datasets.DatasetInfo(features=__lowerCAmelCase				)				)

    return dataset





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__  							=	299_792_458

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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return 1 / sqrt(1 - beta(__lowerCAmelCase				) ** 2				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return np.array(
        [
            [gamma(__lowerCAmelCase				), -gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), 0, 0],
            [-gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), gamma(__lowerCAmelCase				), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]				)





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

    return transformation_matrix(__lowerCAmelCase				) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __magic_name__  							=	transform(29_979_245)
     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
     __magic_name__  							=	{ct: c, x: 1, y: 1, z: 1}
     __magic_name__  							=	[four_vector[i].subs(sub_dict) for i in range(4)]

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



