

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
A_       : str         =			2_9979_2458

# Symbols
A_       ,			A_       ,			A_       ,			A_       : List[Any]         =			symbols('ct x y z')

def 						UpperCamelCase					(lowercase_: float					)     ->      float:
	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 						UpperCamelCase					(lowercase_: float					)     ->      float:
	return 1 / sqrt(1 - beta(lowercase_					) ** 2					)

def 						UpperCamelCase					(lowercase_: float					)     ->      np.ndarray:
	return np.array(
	    [
	        [gamma(lowercase_					), -gamma(lowercase_					) * beta(lowercase_					), 0, 0],
	        [-gamma(lowercase_					) * beta(lowercase_					), gamma(lowercase_					), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]					)

def 						UpperCamelCase					(lowercase_: float    ,      lowercase_: np.ndarray | None = None					)     ->      np.ndarray:
	# Ensure event is not empty
	if event is None:
		A__							:    Tuple        =		np.array([ct, x, y, z]					)  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(lowercase_					) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	A_       : int         =			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
	A_       : int         =			{ct: c, x: 1, y: 1, z: 1}
	A_       : List[str]         =			[four_vector[i].subs(sub_dict) for i in range(4)]

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






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       : Any         =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		DatasetInfosDict(
	    {
	        """default""": DatasetInfo(
	            features=Features(
	                {
	                    """tokens""": Sequence(Value("""string"""					)					),
	                    """ner_tags""": Sequence(
	                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]					)					),
	                    """langs""": Sequence(Value("""string"""					)					),
	                    """spans""": Sequence(Value("""string"""					)					),
	                }					)    ,      splits=[
	                {
	                    """name""": """train""",
	                    """num_bytes""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes    ,      expected[split].num_bytes					)
		else:
			result == expected



