
def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						generate_pascal_triangle(lowerCAmelCase__		)
					for row_idx in range(lowerCAmelCase__		):
										# Print left spaces
										for _ in range(num_rows - row_idx - 1		):
															print(end=''' '''		)
										# Print row values
										for col_idx in range(row_idx + 1		):
															if col_idx != row_idx:
																				print(triangle[row_idx][col_idx]			,      end=''' '''		)
															else:
																				print(triangle[row_idx][col_idx]			,      end=''''''		)
										print()



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''

					if not isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
										raise TypeError('''The input value of \'num_rows\' should be \'int\''''		)

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    '''The input value of \'num_rows\' should be greater than or equal to 0'''		)

					lowercase						     =						[]
					for current_row_idx in range(lowerCAmelCase__		):
										lowercase						     =						populate_current_row(lowerCAmelCase__			,      lowerCAmelCase__		)
										triangle.append(lowerCAmelCase__		)
					return triangle



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[-1] * (current_row_idx + 1)
					# first and last elements of current row are equal to 1
					lowercase     ,						lowercase						     =						1, 1
					for current_col_idx in range(1			,      lowerCAmelCase__		):
										calculate_current_element(
										    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)
					return current_row



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      ):
					'''simple docstring'''
					lowercase						     =						triangle[current_row_idx - 1][current_col_idx - 1]
					lowercase						     =						triangle[current_row_idx - 1][current_col_idx]
					lowercase						     =						above_to_left_elt + above_to_right_elt



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''

					if not isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
										raise TypeError('''The input value of \'num_rows\' should be \'int\''''		)

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    '''The input value of \'num_rows\' should be greater than or equal to 0'''		)

					lowercase						     =						[[1]]

					for row_index in range(1			,      lowerCAmelCase__		):
										lowercase						     =						[0] + result[-1] + [0]
										lowercase						     =						row_index + 1
										# Calculate the number of distinct elements in a row
										lowercase						     =						sum(divmod(lowerCAmelCase__			,      2		)		)
										lowercase						     =						[
										    temp_row[i - 1] + temp_row[i] for i in range(1			,      distinct_elements + 1		)
										]
										lowercase						     =						row_first_half[: (row_index + 1) // 2]
										row_second_half.reverse()
										lowercase						     =						row_first_half + row_second_half
										result.append(lowerCAmelCase__		)

					return result



def 	UpperCamelCase   (							):
					'''simple docstring'''
					from collections.abc import Callable
					from timeit import timeit

					def benchmark_a_function(lowerCAmelCase__			,      lowerCAmelCase__		) -> None:
										lowercase						     =						f'{func.__name__}({value})'
										lowercase						     =						timeit(f'__main__.{call}'			,      setup='''import __main__'''		)
										# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
										print(f'{call:38} -- {timing:.4f} seconds'		)

					for value in range(15		):  # (1, 7, 14):
										for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
															benchmark_a_function(lowerCAmelCase__			,      lowerCAmelCase__		)
										print()


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()






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 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						np.inf

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

					_visit(lowerCAmelCase__			,      lowerCAmelCase__		)

					return None if batch_size is np.inf else batch_size







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							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__       ,)
												lowercase						     =						path_or_paths if isinstance(A__       ,A__) else {self.split: path_or_paths}
												lowercase						     =						_PACKAGED_DATASETS_MODULES['''parquet'''][1]
												lowercase						     =						Parquet(
												    cache_dir=A__       ,data_files=A__       ,features=A__       ,hash=A__       ,**A__       ,)





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

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







class     lowercase		:




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




							def 			A__				(    self):
												lowercase						     =						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:
																						lowercase						     =						self._write(file_obj=A__       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												else:
																	lowercase						     =						self._write(file_obj=self.path_or_buf       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												return written





							def 			A__				(    self       ,A__       ,A__       ,**A__):
												lowercase						     =						0
												lowercase						     =						parquet_writer_kwargs.pop('''path_or_buf'''       ,A__)
												lowercase						     =						self.dataset.features.arrow_schema

												lowercase						     =						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'''       ,):
																	lowercase						     =						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




