





"""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 	__lowercase  (     cls						:						Optional[Any]		)     ->int:



           '''simple docstring'''







           return cls







def 	__lowercase  (     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'''







    __A					:		Dict						=  device
    __A					:		List[Any]						=  shape[0]

    __A					:		int						=  layout or torch.strided
    __A					:		Tuple						=  device or torch.device('''cpu'''		)

    if generator is not None:
        __A					:		int						=  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":
            __A					:		Tuple						=  '''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_		):
        __A					:		List[Any]						=  (1,) + shape[1:]
        __A					:		Union[str, Any]						=  [
            torch.randn(snake_case_       ,generator=generator[i]       ,device=snake_case_       ,dtype=snake_case_       ,layout=snake_case_		)
            for i in range(snake_case_		)
        ]
        __A					:		List[Any]						=  torch.cat(snake_case_       ,dim=0		).to(snake_case_		)
    else:
        __A					:		Optional[Any]						=  torch.randn(snake_case_       ,generator=snake_case_       ,device=snake_case_       ,dtype=snake_case_       ,layout=snake_case_		).to(snake_case_		)

    return latents







def 	__lowercase  (     snake_case_						:						Union[str, Any]		)     ->Tuple:



    '''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		)











"""simple docstring"""



def 	__lowercase  (     snake_case_						:						list		)     ->list:



    '''simple docstring'''







    for insert_index, insert_value in enumerate(collection[1:]		):
        __A					:		Any						=  insert_index
        while insert_index >= 0 and insert_value < collection[insert_index]:
            __A					:		List[Any]						=  collection[insert_index]
            insert_index -= 1
        if insert_index != temp_index:
            __A					:		str						=  insert_value
    return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       a_				=    input("""Enter numbers separated by a comma:\n""").strip()
       a_				=    [int(item) for item in user_input.split(""",""")]
       print(f'''{insertion_sort(unsorted) = }''')




