
'''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

a_      =  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				):
      """simple docstring"""






      return cls

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE = None,						__SCREAMING_SNAKE_CASE = None,						__SCREAMING_SNAKE_CASE = None,						__SCREAMING_SNAKE_CASE = None,						):
    """simple docstring"""






    snake_case_       :			Dict   	=  device
    snake_case_       :			Union[str, Any]   	=  shape[0]

    snake_case_       :			str   	=  layout or torch.strided
    snake_case_       :			Tuple   	=  device or torch.device("cpu"				)

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

    return latents


def   UpperCamelCase_   (		__SCREAMING_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				)









"""simple docstring"""







from itertools import combinations_with_replacement


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase = 100	):
		_lowercase							:      list[int]			=					[*list(range(1					,							21	)	), 25]
		_lowercase							:      list[int]			=					[2 * x for x in range(1					,							21	)] + [50]
		_lowercase							:      list[int]			=					[3 * x for x in range(1					,							21	)]
		_lowercase							:      list[int]			=					singles + doubles + triples + [0]

		_lowercase							:      int			=					0
		_lowercase							:      int
		_lowercase							:      int
		_lowercase							:      int
		_lowercase							:      int

		for double in doubles:
				for throwa, throwa in combinations_with_replacement(__UpperCAmelCase					,							2	):
						_lowercase							:      Union[str, Any]			=					double + throwa + throwa
						if checkout_total < limit:
								num_checkouts += 1

		return num_checkouts


if __name__ == "__main__":
		print(F'{solution() = }')



