

'''simple docstring'''



SCREAMING_SNAKE_CASE_    =   {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

SCREAMING_SNAKE_CASE_    =   [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_    =   [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_    =   []

def   lowerCamelCase__       (      a__			,      a__)		->    str:



    """simple docstring"""







    _snake_case : Tuple												=		1
    while stack:
        _snake_case : Dict												=		stack.pop()
        print(a__			,      clothes[current_clothing])
        order += 1

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    List[Any]:



    """simple docstring"""







    _snake_case : Optional[Any]												=		1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(a__			,      a__			,      a__)

    stack.append(a__)

def   lowerCamelCase__       (      a__			,      a__)		->    Tuple:



    """simple docstring"""







    for v in range(len(a__)):
        if not visited[v]:
            depth_first_search(a__			,      a__			,      a__)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)






'''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

SCREAMING_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   lowerCamelCase__       (      cls)		->    Optional[Any]:



       """simple docstring"""







       return cls

def   lowerCamelCase__       (      a__			,      a__ = None			,      a__ = None			,      a__ = None			,      a__ = None			,      )		->    Dict:



    """simple docstring"""







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

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

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

    return latents




def   lowerCamelCase__       (      a__)		->    Optional[Any]:



    """simple docstring"""







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



