


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 :				str						    =       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 							UpperCamelCase_  (				cls			:						str     )->      Dict:




           '''simple docstring'''
           return cls
def 							UpperCamelCase_  (				__snake_case			:						Union[Tuple, List]	,			__snake_case			:						Optional[Union[List["torch.Generator"], "torch.Generator"]] = None	,			__snake_case			:						Optional["torch.device"] = None	,			__snake_case			:						Optional["torch.dtype"] = None	,			__snake_case			:						Optional["torch.layout"] = None	,			)->      int:




      '''simple docstring'''
      __magic_name__ 		=			device
      __magic_name__ 		=			shape[0]

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

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

      return latents




def 							UpperCamelCase_  (				__snake_case			:						List[str]     )->      List[Any]:




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





def 							UpperCamelCase_  (				__snake_case			:						list[int]     )->      tuple[int, int]:




						'''simple docstring'''
						# base case
						if len(__snake_case     ) == 1:
												return a[0], a[0]
						else:
												# split A into half.
												__magic_name__ 		=			a[: len(__snake_case     ) // 2]
												__magic_name__ 		=			a[len(__snake_case     ) // 2 :]

												# 2 sub problems, 1/2 of original size.
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )

												# get min of first and max of second
												# linear time
												__magic_name__ 		=			min(__snake_case     )
												__magic_name__ 		=			max(__snake_case     )

												# 3 cases, either (small1, big1),
												# (min_first, max_second), (small2, big2)
												# constant comparisons
												if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																		return smalla, biga
												elif biga - smalla > max_second - min_first:
																		return smalla, biga
												else:
																		return min_first, max_second


if __name__ == "__main__":
					import doctest

					doctest.testmod()

