




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	:						Dict			=     datasets.utils.logging.get_logger(__name__)




@dataclass
class    __lowerCAmelCase       (							datasets.BuilderConfig):
       _a 					=						1_0000
       _a 					=						None
       _a 					=						None







class    __lowerCAmelCase       (							datasets.ArrowBasedBuilder):
       _a 					=						ParquetConfig






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






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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					pa.Table				):
         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
           lowercase    :int      	=						table_cast(_lowerCAmelCase       ,					self.info.features.arrow_schema				)
         return pa_table






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :List[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(_lowerCAmelCase				)				):
           with open(_lowerCAmelCase       ,					"rb"				) as f:
             lowercase    :Optional[Any]      	=						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				)				):
                 lowercase    :Optional[int]      	=						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









def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  lowercase    :Dict      	=						set()

  # Replace all the whitespace in our sentence
  lowercase    :Optional[int]      	=						input_str.replace(" ",			""   )
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()   )
  return len(lowerCamelCase   ) == 26



def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  lowercase    :Optional[Any]      	=						[False] * 26
  for char in input_str:
    if char.islower():
      lowercase    :Union[str, Any]      	=						True
    elif char.isupper():
      lowercase    :int      	=						True
  return all(lowerCamelCase   )



def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  return len({char for char in input_str.lower() if char.isalpha()}   ) == 26



def       UpperCAmelCase__  (     ):
  from timeit import timeit

  lowercase    :Optional[Any]      	=						"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()",			setup=lowerCamelCase   )   )
  print(timeit("is_pangram_faster()",			setup=lowerCamelCase   )   )
  print(timeit("is_pangram_fastest()",			setup=lowerCamelCase   )   )
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



