




'''simple docstring'''


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import jax
	import jaxlib

_lowercase  =			get_logger()

_lowercase  =			None
class 						a_				(			TensorFormatter[Mapping, '''jax.Array''', Mapping]						):




	def __init__(       self     :  Dict       ,		__lowerCAmelCase     :  Dict=None       ,		__lowerCAmelCase     :  Dict=None       ,		**__lowerCAmelCase     :  Any      ):
							super().__init__(features=__lowerCAmelCase      )
							import jax
							from jaxlib.xla_client import Device

							if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ):
													raise ValueError(
													    F'Expected {device} to be a `str` not {type(__lowerCAmelCase      )}, as `jaxlib.xla_extension.Device` '
													    'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
													    'the device with `str()` to get its string identifier that will be internally mapped '
													    'to the actual `jaxlib.xla_extension.Device`.'      )
							__snake_case						 =   device if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ) else str(jax.devices()[0]      )
							# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
							# with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
													__snake_case						 =   self._map_devices_to_str()
							if self.device not in list(DEVICE_MAPPING.keys()      ):
													logger.warning(
													    F'Device with string identifier {self.device} not listed among the available '
													    F'devices: {list(DEVICE_MAPPING.keys()      )}, so falling back to the default '
													    F'device: {str(jax.devices()[0]      )}.'      )
													__snake_case						 =   str(jax.devices()[0]      )
							__snake_case						 =   jnp_array_kwargs




	@staticmethod
	def   lowercase__   (       ):
							import jax

							return {str(__lowerCAmelCase      ): device for device in jax.devices()}




	def   lowercase__   (       self     :  Dict       ,		__lowerCAmelCase     :  int      ):
							import jax
							import jax.numpy as jnp

							if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ) and column:
													if all(
													    isinstance(__lowerCAmelCase       ,		jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
																			return jnp.stack(__lowerCAmelCase       ,		axis=0      )
							return column




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]      ):
							import jax
							import jax.numpy as jnp

							if isinstance(__lowerCAmelCase       ,		(str, bytes, type(__lowerCAmelCase      ))      ):
													return value
							elif isinstance(__lowerCAmelCase       ,		(np.character, np.ndarray)      ) and np.issubdtype(value.dtype       ,		np.character      ):
													return value.tolist()

							__snake_case						 =   {}

							if isinstance(__lowerCAmelCase       ,		(np.number, np.ndarray)      ) and np.issubdtype(value.dtype       ,		np.integer      ):
													# the default int precision depends on the jax config
													# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
													if jax.config.jax_enable_xaa:
																			__snake_case						 =   {'dtype': jnp.intaa}
													else:
																			__snake_case						 =   {'dtype': jnp.intaa}
							elif isinstance(__lowerCAmelCase       ,		(np.number, np.ndarray)      ) and np.issubdtype(value.dtype       ,		np.floating      ):
													__snake_case						 =   {'dtype': jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
													import PIL.Image

													if isinstance(__lowerCAmelCase       ,		PIL.Image.Image      ):
																			__snake_case						 =   np.asarray(__lowerCAmelCase      )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
													__snake_case						 =   self._map_devices_to_str()

							with jax.default_device(DEVICE_MAPPING[self.device]      ):
													# calling jnp.array on a np.ndarray does copy the data
													# see https://github.com/google/jax/issues/4486
													return jnp.array(__lowerCAmelCase       ,		**{**default_dtype, **self.jnp_array_kwargs}      )




	def   lowercase__   (       self     :  Dict       ,		__lowerCAmelCase     :  Optional[int]      ):
							import jax

							# support for torch, tf, jax etc.
							if config.TORCH_AVAILABLE and "torch" in sys.modules:
													import torch

													if isinstance(__lowerCAmelCase       ,		torch.Tensor      ):
																			return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
							if hasattr(__lowerCAmelCase       ,		'__array__'      ) and not isinstance(__lowerCAmelCase       ,		jax.Array      ):
													__snake_case						 =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(__lowerCAmelCase       ,		np.ndarray      ):
													if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																			return self._consolidate([self.recursive_tensorize(__lowerCAmelCase      ) for substruct in data_struct]      )
							elif isinstance(__lowerCAmelCase       ,		(list, tuple)      ):
													return self._consolidate([self.recursive_tensorize(__lowerCAmelCase      ) for substruct in data_struct]      )
							return self._tensorize(__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict       ,		__lowerCAmelCase     :  dict      ):
							return map_nested(self._recursive_tensorize       ,		__lowerCAmelCase       ,		map_list=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  pa.Table      ):
							__snake_case						 =   self.numpy_arrow_extractor().extract_row(__lowerCAmelCase      )
							__snake_case						 =   self.python_features_decoder.decode_row(__lowerCAmelCase      )
							return self.recursive_tensorize(__lowerCAmelCase      )




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  pa.Table      ):
							__snake_case						 =   self.numpy_arrow_extractor().extract_column(__lowerCAmelCase      )
							__snake_case						 =   self.python_features_decoder.decode_column(__lowerCAmelCase       ,		pa_table.column_names[0]      )
							__snake_case						 =   self.recursive_tensorize(__lowerCAmelCase      )
							__snake_case						 =   self._consolidate(__lowerCAmelCase      )
							return column




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  pa.Table      ):
							__snake_case						 =   self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase      )
							__snake_case						 =   self.python_features_decoder.decode_batch(__lowerCAmelCase      )
							__snake_case						 =   self.recursive_tensorize(__lowerCAmelCase      )
							for column_name in batch:
													__snake_case						 =   self._consolidate(batch[column_name]      )
							return batch







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   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__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

