



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


UpperCAmelCase							 =					datasets.utils.logging.get_logger(__name__)

@dataclass
class  __snake_case      (   datasets.BuilderConfig):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					1_0_0_0_0
							UpperCamelCase__				:      Optional[List[str]]         =					None
							UpperCamelCase__				:      Optional[datasets.Features]         =					None




class  __snake_case      (   datasets.ArrowBasedBuilder):







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					ParquetConfig


							def _a      (							self		):
											return datasets.DatasetInfo(features=self.config.features		)


							def _a      (							self  ,  a_		):
											if not self.config.data_files:
															raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''		)
											a__				      =		dl_manager.download_and_extract(self.config.data_files		)
											if isinstance(a_  ,  (str, list, tuple)		):
															a__				      =		data_files
															if isinstance(a_  ,  a_		):
																			a__				      =		[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a__				      =		[dl_manager.iter_files(a_		) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,  gen_kwargs={"""files""": files}		)]
											a__				      =		[]
											for split_name, files in data_files.items():
															if isinstance(a_  ,  a_		):
																			a__				      =		[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a__				      =		[dl_manager.iter_files(a_		) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																			for file in itertools.chain.from_iterable(a_		):
																							with open(a_  ,  """rb"""		) as f:
																											a__				      =		datasets.Features.from_arrow_schema(pq.read_schema(a_		)		)
																							break
															splits.append(datasets.SplitGenerator(name=a_  ,  gen_kwargs={"""files""": files}		)		)
											return splits


							def _a      (							self  ,  a_		):
											if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															a__				      =		table_cast(a_  ,  self.info.features.arrow_schema		)
											return pa_table







							def _a      (							self  ,  a_		):
											a__				      =		self.info.features.arrow_schema if self.info.features is not None else None
											if self.info.features is not None and self.config.columns is not None:
															if sorted(field.name for field in schema		) != sorted(self.config.columns		):
																			raise ValueError(
																			    F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''		)
											for file_idx, file in enumerate(itertools.chain.from_iterable(a_		)		):
															with open(a_  ,  """rb"""		) as f:
																			a__				      =		pq.ParquetFile(a_		)
																			try:
																							for batch_idx, record_batch in enumerate(
																							    parquet_file.iter_batches(batch_size=self.config.batch_size  ,  columns=self.config.columns		)		):
																											a__				      =		pa.Table.from_batches([record_batch]		)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield F'''{file_idx}_{batch_idx}''', self._cast_table(a_		)
																			except ValueError as e:
																							logger.error(F'''Failed to read file \'{file}\' with error {type(a_		)}: {e}'''		)
																							raise






from math import sqrt

import numpy as np
from sympy import symbols

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

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

def 			A_      (	__a     :				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 			A_      (	__a     :				float		):

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

def 			A_      (	__a     :				float		):

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

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

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

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					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
	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}""")

