
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
					import torch

A  :			Union[str, Any]     				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
					from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


					def 					__lowerCAmelCase    (    cls						) ->						Dict:
									return cls


def 					__lowerCAmelCase    (    a__		,					a__ = None		,					a__ = None		,					a__ = None		,					a__ = None		,					) ->						Any:
				__a    							=					device
				__a    							=					shape[0]

				__a    							=					layout or torch.strided
				__a    							=					device or torch.device('''cpu'''						)

				if generator is not None:
								__a    							=					generator.device.type if not isinstance(a__		,					a__						) else generator[0].device.type
								if gen_device_type != device.type and gen_device_type == "cpu":
												__a    							=					'''cpu'''
												if device != "mps":
																logger.info(
																    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																    F""" slighly speed up this function by passing a generator that was created on the {device} device."""						)
								elif gen_device_type != device.type and gen_device_type == "cuda":
												raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""						)

				if isinstance(a__		,					a__						):
								__a    							=					(1,) + shape[1:]
								__a    							=					[
								    torch.randn(a__		,					generator=generator[i]		,					device=a__		,					dtype=a__		,					layout=a__						)
								    for i in range(a__						)
								]
								__a    							=					torch.cat(a__		,					dim=0						).to(a__						)
				else:
								__a    							=					torch.randn(a__		,					generator=a__		,					device=a__		,					dtype=a__		,					layout=a__						).to(a__						)

				return latents






def 					__lowerCAmelCase    (    a__						) ->						int:
				if is_torch_version('''<'''		,					'''2.0.0'''						) or not hasattr(a__		,					'''_dynamo'''						):
								return False
				return isinstance(a__		,					torch._dynamo.eval_frame.OptimizedModule						)

from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()