
"""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


UpperCamelCase_					:						Optional[int]				= datasets.utils.logging.get_logger(__name__)



@dataclass
class __lowerCAmelCase				(					datasets.BuilderConfig      ):




   """simple docstring"""







   snake_case	      =				1_00_00
   snake_case	      =				None
   snake_case	      =				None





class __lowerCAmelCase				(					datasets.ArrowBasedBuilder      ):




   """simple docstring"""







   snake_case	      =				ParquetConfig




   def      lowerCamelCase__     (				self						:					List[Any]	)      -> Dict:







        """simple docstring"""
        return datasets.DatasetInfo(features=self.config.features	)




   def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Any	)      -> Optional[int]:







        """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}'	)
        A_				 =						dl_manager.download_and_extract(self.config.data_files	)
        if isinstance(_snake_case       ,   (str, list, tuple)	):
             A_				 =						data_files
             if isinstance(_snake_case       ,   _snake_case	):
                  A_				 =						[files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             A_				 =						[dl_manager.iter_files(_snake_case	) 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(_snake_case       ,   _snake_case	):
                  A_				 =						[files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             A_				 =						[dl_manager.iter_files(_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(_snake_case	):
                       with open(_snake_case       ,   "rb"	) as f:
                            A_				 =						datasets.Features.from_arrow_schema(pq.read_schema(_snake_case	)	)
                       break
             splits.append(datasets.SplitGenerator(name=_snake_case       ,   gen_kwargs={"files": files}	)	)
        return splits




   def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					pa.Table	)      -> pa.Table:







        """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
             A_				 =						table_cast(_snake_case       ,   self.info.features.arrow_schema	)
        return pa_table






   def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					Dict	)      -> Optional[Any]:







        """simple docstring"""
        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(_snake_case	)	):
             with open(_snake_case       ,   "rb"	) as f:
                  A_				 =						pq.ParquetFile(_snake_case	)
                  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(_snake_case	)
                  except ValueError as e:
                       logger.error(F'Failed to read file \'{file}\' with error {type(_snake_case	)}: {e}'	)
                       raise






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




