
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


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




@dataclass
class        _SCREAMING_SNAKE_CASE	(   datasets.BuilderConfig			):
      _A    :		int     						=  1_00_00
      _A    :		Optional[List[str]]     						=  None
      _A    :		Optional[datasets.Features]     						=  None





class        _SCREAMING_SNAKE_CASE	(   datasets.ArrowBasedBuilder			):
      _A    :		List[str]     						=  ParquetConfig
      def A_      (      self			):
          return datasets.DatasetInfo(features=self.config.features			)
      def A_      (      self		,						lowerCamelCase			):
          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__    =					dl_manager.download_and_extract(self.config.data_files			)
          if isinstance(lowerCamelCase		,						(str, list, tuple)			):
              snake_case__    =					data_files
              if isinstance(lowerCamelCase		,						lowerCamelCase			):
                  snake_case__    =					[files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              snake_case__    =					[dl_manager.iter_files(lowerCamelCase			) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,						gen_kwargs={"files": files}			)]
          snake_case__    =					[]
          for split_name, files in data_files.items():
              if isinstance(lowerCamelCase		,						lowerCamelCase			):
                  snake_case__    =					[files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              snake_case__    =					[dl_manager.iter_files(lowerCamelCase			) 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(lowerCamelCase			):
                      with open(lowerCamelCase		,						"rb"			) as f:
                          snake_case__    =					datasets.Features.from_arrow_schema(pq.read_schema(lowerCamelCase			)			)
                      break
              splits.append(datasets.SplitGenerator(name=lowerCamelCase		,						gen_kwargs={"files": files}			)			)
          return splits
      def A_      (      self		,						lowerCamelCase			):
          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__    =					table_cast(lowerCamelCase		,						self.info.features.arrow_schema			)
          return pa_table



      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					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(lowerCamelCase			)			):
              with open(lowerCamelCase		,						"rb"			) as f:
                  snake_case__    =					pq.ParquetFile(lowerCamelCase			)
                  try:
                      for batch_idx, record_batch in enumerate(
                          parquet_file.iter_batches(batch_size=self.config.batch_size		,						columns=self.config.columns			)			):
                          snake_case__    =					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(lowerCamelCase			)
                  except ValueError as e:
                      logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase			)}: {e}"""			)
                      raise





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}''')



