


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

snake_case__  				=			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 			__SCREAMING_SNAKE_CASE							(					cls	:Union[str, Any]					)							->	Optional[int]:
									return cls
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Union[Tuple, List]   ,    lowercase__	:Optional[Union[List["torch.Generator"], "torch.Generator"]] = None   ,    lowercase__	:Optional["torch.device"] = None   ,    lowercase__	:Optional["torch.dtype"] = None   ,    lowercase__	:Optional["torch.layout"] = None   ,    )							->	List[str]:
						__magic_name__					:				Dict				=   device
						__magic_name__					:				Dict				=   shape[0]

						__magic_name__					:				str				=   layout or torch.strided
						__magic_name__					:				Tuple				=   device or torch.device('cpu'					)

						if generator is not None:
												__magic_name__					:				Dict				=   generator.device.type if not isinstance(lowerCAmelCase__   ,    lowerCAmelCase__					) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		__magic_name__					:				Union[str, Any]				=   """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(lowerCAmelCase__   ,    lowerCAmelCase__					):
												__magic_name__					:				List[Any]				=   (1,) + shape[1:]
												__magic_name__					:				Union[str, Any]				=   [
												    torch.randn(lowerCAmelCase__   ,    generator=generator[i]   ,    device=lowerCAmelCase__   ,    dtype=lowerCAmelCase__   ,    layout=lowerCAmelCase__					)
												    for i in range(lowerCAmelCase__					)
												]
												__magic_name__					:				str				=   torch.cat(lowerCAmelCase__   ,    dim=0					).to(lowerCAmelCase__					)
						else:
												__magic_name__					:				Optional[int]				=   torch.randn(lowerCAmelCase__   ,    generator=lowerCAmelCase__   ,    device=lowerCAmelCase__   ,    dtype=lowerCAmelCase__   ,    layout=lowerCAmelCase__					).to(lowerCAmelCase__					)

						return latents

def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any					)							->	List[str]:





						if is_torch_version('<'   ,    '2.0.0'					) or not hasattr(lowerCAmelCase__   ,    '_dynamo'					):
												return False
						return isinstance(lowerCAmelCase__   ,    torch._dynamo.eval_frame.OptimizedModule					)







'''simple docstring'''
from .hash_table import HashTable



class _A	(     __SCREAMING_SNAKE_CASE     ):





		def __init__(  self			,							*__UpperCAmelCase			,							**__UpperCAmelCase					)							->  List[Any]:

					'''simple docstring'''
					super().__init__(*__UpperCAmelCase			,							**__UpperCAmelCase					)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=None					)							->  str:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  1
					__UpperCAmelCase :   Optional[Any]    					=  self.hash_function(key + i * i					)

					while self.values[new_key] is not None and self.values[new_key] != key:
								i += 1
								__UpperCAmelCase :   str    					=  (
								    self.hash_function(key + i * i					)
								    if not self.balanced_factor() >= self.lim_charge
								    else None
								)

								if new_key is None:
											break

					return new_key




