

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[Any]			=    datasets.utils.logging.get_logger(__name__)



@dataclass
class       __snake_case  (    datasets.BuilderConfig       ):
  __lowerCAmelCase     :	List[Any]						 =  10_000
  __lowerCAmelCase     :	Any						 =  None
  __lowerCAmelCase     :	List[str]						 =  None






class       __snake_case  (    datasets.ArrowBasedBuilder       ):
  __lowerCAmelCase     :	int						 =  ParquetConfig

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

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

  def 						lowerCAmelCase__     (					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
          SCREAMING_SNAKE_CASE_									=						table_cast(UpperCamelCase__						,		self.info.features.arrow_schema)
      return pa_table


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





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int = 200       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[1, 2, 5, 10, 20, 50, 100, 200]
    SCREAMING_SNAKE_CASE_									=						[0] * (pence + 1)
    SCREAMING_SNAKE_CASE_									=						1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(_SCREAMING_SNAKE_CASE ,   pence + 1 ,   1       ):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682


