



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 			A_      (	__a     :				Features		):

				"""simple docstring"""
				a__				      =		np.inf

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

				_visit(__a ,		__a		)

				return None if batch_size is np.inf else batch_size

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = False  ,  a_ = False  ,  a_ = None  ,  **a_  ,  ):
											super().__init__(
											    a_  ,  split=a_  ,  features=a_  ,  cache_dir=a_  ,  keep_in_memory=a_  ,  streaming=a_  ,  num_proc=a_  ,  **a_  ,  )
											a__				      =		path_or_paths if isinstance(a_  ,  a_		) else {self.split: path_or_paths}
											a__				      =		_PACKAGED_DATASETS_MODULES["""parquet"""][1]
											a__				      =		Parquet(
											    cache_dir=a_  ,  data_files=a_  ,  features=a_  ,  hash=a_  ,  **a_  ,  )







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

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




class  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		dataset
											a__				      =		path_or_buf
											a__				      =		batch_size or get_writer_batch_size(dataset.features		)
											a__				      =		parquet_writer_kwargs


							def _a      (							self		):
											a__				      =		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:
																			a__				      =		self._write(file_obj=a_  ,  batch_size=a_  ,  **self.parquet_writer_kwargs		)
											else:
															a__				      =		self._write(file_obj=self.path_or_buf  ,  batch_size=a_  ,  **self.parquet_writer_kwargs		)
											return written







							def _a      (							self  ,  a_  ,  a_  ,  **a_		):
											a__				      =		0
											a__				      =		parquet_writer_kwargs.pop("""path_or_buf"""  ,  a_		)
											a__				      =		self.dataset.features.arrow_schema

											a__				      =		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"""  ,  ):
															a__				      =		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






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

