


'''simple docstring'''






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

__UpperCAmelCase    				=							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 					_snake_case   (     cls       )						->    Optional[int]:
									return cls


def 					_snake_case   (     A ,    A = None ,    A = None ,    A = None ,    A = None ,    )						->    Dict:
							lowerCAmelCase__          =	device
							lowerCAmelCase__          =	shape[0]

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

							if generator is not None:
														lowerCAmelCase__          =	generator.device.type if not isinstance(A ,    A       ) else generator[0].device.type
														if gen_device_type != device.type and gen_device_type == "cpu":
																					lowerCAmelCase__          =	'''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       ):
														lowerCAmelCase__          =	(1,) + shape[1:]
														lowerCAmelCase__          =	[
														    torch.randn(A ,    generator=generator[i] ,    device=A ,    dtype=A ,    layout=A       )
														    for i in range(A       )
														]
														lowerCAmelCase__          =	torch.cat(A ,    dim=0       ).to(A       )
							else:
														lowerCAmelCase__          =	torch.randn(A ,    generator=A ,    device=A ,    dtype=A ,    layout=A       ).to(A       )

							return latents






def 					_snake_case   (     A       )						->    Optional[Any]:
							if is_torch_version('''<''' ,    '''2.0.0'''       ) or not hasattr(A ,    '''_dynamo'''       ):
														return False
							return isinstance(A ,    torch._dynamo.eval_frame.OptimizedModule       )



'''simple docstring'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 		a__				(							a__  ):





							'''simple docstring'''






							def __init__(		self					,    *lowerCamelCase_					,    **lowerCamelCase_   )       ->							Optional[int]:
														super().__init__(*lowerCamelCase_					,    **lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[int]:
														lowerCAmelCase__          =	(
														    next_prime(value % self.size_table   )
														    if not is_prime(value % self.size_table   )
														    else value % self.size_table
														)  # gt = bigger than
														return next_prime_gt - (data % next_prime_gt)







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[int]:
														return (increment * self.__hash_function_a(lowerCamelCase_					,    lowerCamelCase_   )) % self.size_table







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=None   )       ->							Any:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	self.hash_function(lowerCamelCase_   )

														while self.values[new_key] is not None and self.values[new_key] != key:
																					lowerCAmelCase__          =	(
																					    self.__hash_double_function(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )
																					    if self.balanced_factor() >= self.lim_charge
																					    else None
																					)
																					if new_key is None:
																												break
																					else:
																												i += 1

														return new_key