






"""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 							A(    cls						):
         """simple docstring"""





         return cls






def 							A(    snake_case_		,  snake_case_ = None		,  snake_case_ = None		,  snake_case_ = None		,  snake_case_ = None		,  ):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     device
       lowercase__:							Any         =     shape[0]

       lowercase__:							List[str]         =     layout or torch.strided
       lowercase__:							List[Any]         =     device or torch.device("cpu"						)

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

       return latents







def 							A(    snake_case_						):
       """simple docstring"""





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








def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list[int]							)			->							int:

				"""simple docstring"""







				if not isinstance(__UpperCamelCase				,    __UpperCamelCase							) or not all(
				    isinstance(__UpperCamelCase				,    __UpperCamelCase							) and item >= 0 for item in citations							):
								raise ValueError('''The citations should be a list of non negative integers.'''							)

				citations.sort()
				A__	:						str								=				len(__UpperCamelCase							)

				for i in range(__UpperCamelCase							):
								if citations[len_citations - 1 - i] <= i:
												return i

				return len_citations


if __name__ == "__main__":
					import doctest

					doctest.testmod()