import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Optional[int]:
    __lowerCamelCase          =  np.inf

    def set_batch_size(UpperCamelCase__    ) -> None:
        nonlocal batch_size
        if isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
            __lowerCamelCase          =  min(UpperCamelCase__    ,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
        elif isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
            __lowerCamelCase          =  min(UpperCamelCase__    ,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
        elif isinstance(UpperCamelCase__    ,  UpperCamelCase__    ) and feature.dtype == "binary":
            __lowerCamelCase          =  min(UpperCamelCase__    ,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

    _visit(UpperCamelCase__    ,  UpperCamelCase__    )

    return None if batch_size is np.inf else batch_size




class    a__				(       UpperCAmelCase__			):






     def __init__(			self		:		Any  ,    a		:		NestedDataStructureLike[PathLike]  ,    a		:		Optional[NamedSplit] = None  ,    a		:		Optional[Features] = None  ,    a		:		str = None  ,    a		:		bool = False  ,    a		:		bool = False  ,    a		:		Optional[int] = None  ,    **a		:		str  ,    ):





         """simple docstring"""



         super().__init__(
             a  ,    split=a  ,    features=a  ,    cache_dir=a  ,    keep_in_memory=a  ,    streaming=a  ,    num_proc=a  ,    **a  ,    )
         __lowerCamelCase          =  path_or_paths if isinstance(a  ,    a   ) else {self.split: path_or_paths}
         __lowerCamelCase          =  _PACKAGED_DATASETS_MODULES['''parquet'''][1]
         __lowerCamelCase          =  Parquet(
             cache_dir=a  ,    data_files=a  ,    features=a  ,    hash=a  ,    **a  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         if self.streaming:
             __lowerCamelCase          =  self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None

             self.builder.download_and_prepare(
                 download_config=a  ,    download_mode=a  ,    verification_mode=a  ,    base_path=a  ,    num_proc=self.num_proc  ,    )
             __lowerCamelCase          =  self.builder.as_dataset(
                 split=self.split  ,    verification_mode=a  ,    in_memory=self.keep_in_memory   )
         return dataset




class    a__				:






     def __init__(			self		:		str  ,    a		:		Dataset  ,    a		:		Union[PathLike, BinaryIO]  ,    a		:		Optional[int] = None  ,    **a		:		Optional[int]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  dataset
         __lowerCamelCase          =  path_or_buf
         __lowerCamelCase          =  batch_size or get_writer_batch_size(dataset.features   )
         __lowerCamelCase          =  parquet_writer_kwargs






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf  ,    (str, bytes, os.PathLike)   ):
             with open(self.path_or_buf  ,    '''wb+'''   ) as buffer:
                 __lowerCamelCase          =  self._write(file_obj=a  ,    batch_size=a  ,    **self.parquet_writer_kwargs   )
         else:
             __lowerCamelCase          =  self._write(file_obj=self.path_or_buf  ,    batch_size=a  ,    **self.parquet_writer_kwargs   )
         return written






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		BinaryIO  ,    a		:		int  ,    **a		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  0
         __lowerCamelCase          =  parquet_writer_kwargs.pop('''path_or_buf'''  ,    a   )
         __lowerCamelCase          =  self.dataset.features.arrow_schema

         __lowerCamelCase          =  pq.ParquetWriter(a  ,    schema=a  ,    **a   )

         for offset in logging.tqdm(
             range(0  ,    len(self.dataset   )  ,    a   )  ,    unit='''ba'''  ,    disable=not logging.is_progress_bar_enabled()  ,    desc='''Creating parquet from Arrow format'''  ,    ):
             __lowerCamelCase          =  query_table(
                 table=self.dataset._data  ,    key=slice(a  ,    offset + batch_size   )  ,    indices=self.dataset._indices if self.dataset._indices is not None else None  ,    )
             writer.write_table(a   )
             written += batch.nbytes
         writer.close()
         return written

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   float:
    if principal <= 0:
        raise Exception('''Principal borrowed must be > 0'''    )
    if rate_per_annum < 0:
        raise Exception('''Rate of interest must be >= 0'''    )
    if years_to_repay <= 0 or not isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
        raise Exception('''Years to repay must be an integer > 0'''    )

    # Yearly rate is divided by 12 to get monthly rate
    __lowerCamelCase          =  rate_per_annum / 12

    # Years to repay is multiplied by 12 to get number of payments as payment is monthly
    __lowerCamelCase          =  years_to_repay * 12

    return (
        principal
        * rate_per_month
        * (1 + rate_per_month) ** number_of_payments
        / ((1 + rate_per_month) ** number_of_payments - 1)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
