


def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      int(SCREAMING_SNAKE_CASE     )
		if n_element < 1:
				lowercase__            =      ValueError('''a should be a positive number'''     )
				raise my_error

		lowercase__            =      [1]
		lowercase__     ,		lowercase__     ,		lowercase__            =      (0, 0, 0)
		lowercase__            =      1
		while index < n_element:
				while hamming_list[i] * 2 <= hamming_list[-1]:
						i += 1
				while hamming_list[j] * 3 <= hamming_list[-1]:
						j += 1
				while hamming_list[k] * 5 <= hamming_list[-1]:
						k += 1
				hamming_list.append(
				    min(hamming_list[i] * 2						,				hamming_list[j] * 3						,				hamming_list[k] * 5     )     )
				index += 1
		return hamming_list


if __name__ == "__main__":
			lowerCAmelCase       =							input('Enter the last number (nth term) of the Hamming Number Series: ')
			print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
			lowerCAmelCase       =							hamming(int(n))
			print('-----------------------------------------------------')
			print(f"""The list with nth numbers is: {hamming_numbers}""")
			print('-----------------------------------------------------')






import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCAmelCase       =							'<<<<<<< This should probably be modified because it mentions: '

lowerCAmelCase       =							'=======\n>>>>>>>\n'

lowerCAmelCase       =							[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCAmelCase       =							[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'tfds\.core', R'datasets'),
    (R'tf\.io\.gfile\.GFile', R'open'),
    (R'tf\.([\w\d]+)', R'datasets.Value(\'\1\')'),
    (R'tfds\.features\.Text\(\)', R'datasets.Value(\'string\')'),
    (R'tfds\.features\.Text\(', R'datasets.Value(\'string\'),'),
    (R'features\s*=\s*tfds.features.FeaturesDict\(', R'features=datasets.Features('),
    (R'tfds\.features\.FeaturesDict\(', R'dict('),
    (R'The TensorFlow Datasets Authors', R'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (R'tfds\.', R'datasets.'),
    (R'dl_manager\.manual_dir', R'self.config.data_dir'),
    (R'self\.builder_config', R'self.config'),
]
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return ConvertCommand(args.tfds_path						,				args.datasets_directory     )


class        _a							(			UpperCamelCase__			):

							@staticmethod
							def       lowerCamelCase_ (							UpperCamelCase_:	ArgumentParser							)			->   int:





									"""simple docstring"""







									lowercase__            =      parser.add_parser(
									    '''convert'''    ,						help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''    ,						)
									train_parser.add_argument(
									    '''--tfds_path'''    ,						type=UpperCamelCase_    ,						required=UpperCamelCase_    ,						help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''    ,						)
									train_parser.add_argument(
									    '''--datasets_directory'''    ,						type=UpperCamelCase_    ,						required=UpperCamelCase_    ,						help='''Path to the HuggingFace Datasets folder.'''							)
									train_parser.set_defaults(func=UpperCamelCase_							)

							def __init__(							self:	int    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						*UpperCamelCase_:	Optional[int]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      get_logger('''datasets-cli/converting'''							)

									lowercase__            =      tfds_path
									lowercase__            =      datasets_directory


							def       lowerCamelCase_ (							self:	int							)			->   Optional[int]:





									"""simple docstring"""







									if os.path.isdir(self._tfds_path							):
											lowercase__            =      os.path.abspath(self._tfds_path							)
									elif os.path.isfile(self._tfds_path							):
											lowercase__            =      os.path.dirname(self._tfds_path							)
									else:
											raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''							)

									lowercase__            =      os.path.abspath(self._datasets_directory							)

									self._logger.info(f'Converting datasets from {abs_tfds_path} to {abs_datasets_path}'							)

									lowercase__            =      []
									lowercase__            =      []
									lowercase__            =      {}

									if os.path.isdir(self._tfds_path							):
											lowercase__            =      os.listdir(UpperCamelCase_							)
									else:
											lowercase__            =      [os.path.basename(self._tfds_path							)]

									for f_name in file_names:
											self._logger.info(f'Looking at file {f_name}'							)
											lowercase__            =      os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)
											lowercase__            =      os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)

											if not os.path.isfile(UpperCamelCase_							) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
													self._logger.info('''Skipping file'''							)
													continue

											with open(UpperCamelCase_    ,						encoding='''utf-8'''							) as f:
													lowercase__            =      f.readlines()

											lowercase__            =      []
											lowercase__            =      False
											lowercase__            =      False
											lowercase__            =      []
											for line in lines:
													lowercase__            =      line

													# Convert imports
													if "import tensorflow.compat.v2 as tf" in out_line:
															continue
													elif "@tfds.core" in out_line:
															continue
													elif "builder=self" in out_line:
															continue
													elif "import tensorflow_datasets.public_api as tfds" in out_line:
															lowercase__            =      '''import datasets\n'''
													elif "import tensorflow" in out_line:
															# order is important here
															lowercase__            =      ''''''
															continue
													elif "from absl import logging" in out_line:
															lowercase__            =      '''from datasets import logging\n'''
													elif "getLogger" in out_line:
															lowercase__            =      out_line.replace('''getLogger'''    ,						'''get_logger'''							)
													elif any(expression in out_line for expression in TO_HIGHLIGHT							):
															lowercase__            =      True
															lowercase__            =      list(filter(lambda UpperCamelCase_							: e in out_line    ,						UpperCamelCase_							)							)
															out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCamelCase_							) + '''\n'''							)
															out_lines.append(UpperCamelCase_							)
															out_lines.append(UpperCamelCase_							)
															continue
													else:
															for pattern, replacement in TO_CONVERT:
																	lowercase__            =      re.sub(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

                # Take care of saving utilities (to later move them together with main script)
													if "tensorflow_datasets" in out_line:
															lowercase__            =      re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''    ,						UpperCamelCase_							)
															tfds_imports.extend(imp.strip() for imp in match.group(1							).split(''','''							)							)
															lowercase__            =      '''from . import ''' + match.group(1							)

													# Check we have not forget anything
													if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
															raise ValueError(f'Error converting {out_line.strip()}'							)

													if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
															lowercase__            =      True
													out_lines.append(UpperCamelCase_							)

											if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__            =      f_name.replace('''.py'''    ,						''''''							)
													lowercase__            =      os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)
													lowercase__            =      os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)
													os.makedirs(UpperCamelCase_    ,						exist_ok=UpperCamelCase_							)
													self._logger.info(f'Adding directory {output_dir}'							)
													imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}							)
											else:
													# Utilities will be moved at the end
													utils_files.append(UpperCamelCase_							)

											if needs_manual_update:
													with_manual_update.append(UpperCamelCase_							)

											with open(UpperCamelCase_    ,						'''w'''    ,						encoding='''utf-8'''							) as f:
													f.writelines(UpperCamelCase_							)
											self._logger.info(f'Converted in {output_file}'							)

									for utils_file in utils_files:
											try:
													lowercase__            =      os.path.basename(UpperCamelCase_							)
													lowercase__            =      imports_to_builder_map[f_name.replace('''.py'''    ,						''''''							)]
													self._logger.info(f'Moving {dest_folder} to {utils_file}'							)
													shutil.copy(UpperCamelCase_    ,						UpperCamelCase_							)
											except KeyError:
													self._logger.error(f'Cannot find destination folder for {utils_file}. Please copy manually.'							)

									if with_manual_update:
											for file_path in with_manual_update:
													self._logger.warning(
													    f'You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'							)


