






"""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

lowercase_								=	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					):




         """simple docstring"""







         return cls



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase = None						,			__UpperCamelCase = None						,			__UpperCamelCase = None						,			__UpperCamelCase = None						,			):




  """simple docstring"""







  __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(__UpperCamelCase						,			__UpperCamelCase					) 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(__UpperCamelCase						,			__UpperCamelCase					):
    __A		     =					(1,) + shape[1:]
    __A		     =					[
        torch.randn(__UpperCamelCase						,			generator=generator[i]						,			device=__UpperCamelCase						,			dtype=__UpperCamelCase						,			layout=__UpperCamelCase					)
        for i in range(__UpperCamelCase					)
    ]
    __A		     =					torch.cat(__UpperCamelCase						,			dim=0					).to(__UpperCamelCase					)
  else:
    __A		     =					torch.randn(__UpperCamelCase						,			generator=__UpperCamelCase						,			device=__UpperCamelCase						,			dtype=__UpperCamelCase						,			layout=__UpperCamelCase					).to(__UpperCamelCase					)

  return latents





def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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







'''simple docstring'''

def      __magic_name__							(			A				)      ->		tuple[int, int]:
	# base case
	if len(A				) == 1:
		return a[0], a[0]
	else:
		# split A into half.
		snake_case							=					a[: len(A				) // 2]
		snake_case							=					a[len(A				) // 2 :]

		# 2 sub problems, 1/2 of original size.
		snake_case			,			snake_case							=					max_difference(A				)
		snake_case			,			snake_case							=					max_difference(A				)

		# get min of first and max of second
		# linear time
		snake_case							=					min(A				)
		snake_case							=					max(A				)

		# 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()





