




"""simple docstring"""

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


__A       						=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 	lowerCamelCase__			(     datasets.BuilderConfig	):
			a__       :					int	    =   1_00_00
			a__       :					Optional[List[str]]	    =   None
			a__       :					Optional[datasets.Features]	    =   None







class 	lowerCamelCase__			(     datasets.ArrowBasedBuilder	):
			a__       :					Optional[Any]	    =   ParquetConfig


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					return datasets.DatasetInfo(features=self.config.features							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					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}'''							)
					snake_case						:  Tuple    	= dl_manager.download_and_extract(self.config.data_files							)
					if isinstance(SCREAMING_SNAKE_CASE__ ,       (str, list, tuple)							):
							snake_case						:  Optional[Any]    	= data_files
							if isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__							):
									snake_case						:  Optional[int]    	= [files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							snake_case						:  Optional[Any]    	= [dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,       gen_kwargs={"files": files}							)]
					snake_case						:  Union[str, Any]    	= []
					for split_name, files in data_files.items():
							if isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__							):
									snake_case						:  List[str]    	= [files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							snake_case						:  Union[str, Any]    	= [dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) 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(SCREAMING_SNAKE_CASE__							):
											with open(SCREAMING_SNAKE_CASE__ ,       "rb"							) as f:
													snake_case						:  Dict    	= datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE__							)							)
											break
							splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__ ,       gen_kwargs={"files": files}							)							)
					return splits


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					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
							snake_case						:  Tuple    	= table_cast(SCREAMING_SNAKE_CASE__ ,       self.info.features.arrow_schema							)
					return pa_table



			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  str    	= 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(SCREAMING_SNAKE_CASE__							)							):
							with open(SCREAMING_SNAKE_CASE__ ,       "rb"							) as f:
									snake_case						:  Optional[int]    	= pq.ParquetFile(SCREAMING_SNAKE_CASE__							)
									try:
											for batch_idx, record_batch in enumerate(
											    parquet_file.iter_batches(batch_size=self.config.batch_size ,       columns=self.config.columns							)							):
													snake_case						:  Optional[Any]    	= 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(SCREAMING_SNAKE_CASE__							)
									except ValueError as e:
											logger.error(F'''Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE__							)}: {e}'''							)
											raise


'''simple docstring'''

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_A			:      List[Any]					      =				299792458

# Symbols
_A      ,     _A      ,     _A      ,     _A			:      Union[str, Any]					      =				symbols('''ct x y z''')







def      UpperCamelCase_						(					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      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

 return 1 / sqrt(1 - beta(snake_case_      ) ** 2      )







def      UpperCamelCase_						(					snake_case_       :       float      )					->     np.ndarray:






 '''simple docstring'''

 return np.array(
     [
         [gamma(snake_case_      ), -gamma(snake_case_      ) * beta(snake_case_      ), 0, 0],
         [-gamma(snake_case_      ) * beta(snake_case_      ), gamma(snake_case_      ), 0, 0],
         [0, 0, 1, 0],
         [0, 0, 0, 1],
     ]      )







def      UpperCamelCase_						(					snake_case_       :       float						,		snake_case_       :       np.ndarray | None = None      )					->     np.ndarray:






 '''simple docstring'''

 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(snake_case_      ) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 _A			:      str					      =				transform(29979245)
 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			:      Any					      =				[four_vector[i].subs(sub_dict) for i in range(4)]

 print(f'\n{numerical_vector}')






