




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE:     Optional[Any]									= logging.get_logger(__name__)


SCREAMING_SNAKE_CASE:     List[Any]									= TypeVar('''DatasetType''', Dataset, IterableDataset)


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None    ,						lowerCAmelCase = None    ,						lowerCAmelCase = None    ,						lowerCAmelCase = None    ,						lowerCAmelCase = "first_exhausted"    ,						)->	DatasetType:
		from .arrow_dataset import Dataset
		from .iterable_dataset import IterableDataset

		if not datasets:
				raise ValueError('Unable to interleave an empty list of datasets.' )
		for i, dataset in enumerate(lowerCAmelCase ):
				if not isinstance(lowerCAmelCase    ,						(Dataset, IterableDataset) ):
						if isinstance(lowerCAmelCase    ,						(DatasetDict, IterableDatasetDict) ):
								if not dataset:
										raise ValueError(
										    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
										    'is an empty dataset dictionary.' )
								raise ValueError(
								    F'''Dataset at position {i} has at least one split: {list(lowerCAmelCase )}\n'''
								    F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCAmelCase ) )}\']''' )
						raise ValueError(
						    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCAmelCase ).__name__}.''' )
				if i == 0:
						SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							(
						    (Dataset, IterableDataset) if isinstance(lowerCAmelCase    ,						lowerCAmelCase ) else (IterableDataset, Dataset)
						)
				elif not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
						raise ValueError(
						    F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
		if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
				raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
		if dataset_type is Dataset:
				return _interleave_map_style_datasets(
				    lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						info=lowerCAmelCase    ,						split=lowerCAmelCase    ,						stopping_strategy=lowerCAmelCase )
		else:
				return _interleave_iterable_datasets(
				    lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						info=lowerCAmelCase    ,						split=lowerCAmelCase    ,						stopping_strategy=lowerCAmelCase )




def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None    ,						lowerCAmelCase = None    ,						lowerCAmelCase = 0    ,						)->	DatasetType:

		if not dsets:
				raise ValueError('Unable to concatenate an empty list of datasets.' )
		for i, dataset in enumerate(lowerCAmelCase ):
				if not isinstance(lowerCAmelCase    ,						(Dataset, IterableDataset) ):
						if isinstance(lowerCAmelCase    ,						(DatasetDict, IterableDatasetDict) ):
								if not dataset:
										raise ValueError(
										    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
										    'is an empty dataset dictionary.' )
								raise ValueError(
								    F'''Dataset at position {i} has at least one split: {list(lowerCAmelCase )}\n'''
								    F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCAmelCase ) )}\']''' )
						raise ValueError(
						    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCAmelCase ).__name__}.''' )
				if i == 0:
						SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							(
						    (Dataset, IterableDataset) if isinstance(lowerCAmelCase    ,						lowerCAmelCase ) else (IterableDataset, Dataset)
						)
				elif not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
						raise ValueError(
						    F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
		if dataset_type is Dataset:
				return _concatenate_map_style_datasets(lowerCAmelCase    ,						info=lowerCAmelCase    ,						split=lowerCAmelCase    ,						axis=lowerCAmelCase )
		else:
				return _concatenate_iterable_datasets(lowerCAmelCase    ,						info=lowerCAmelCase    ,						split=lowerCAmelCase    ,						axis=lowerCAmelCase )





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	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 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_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:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						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
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

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