

"""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      __SCREAMING_SNAKE_CASE		(		cls   ):
						"""simple docstring"""
						return cls
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = 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(lowercase__    ,							lowercase__   ) 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(lowercase__    ,							lowercase__   ):
										A    =      (1,) + shape[1:]
										A    =      [
										    torch.randn(lowercase__    ,							generator=generator[i]    ,							device=lowercase__    ,							dtype=lowercase__    ,							layout=lowercase__   )
										    for i in range(lowercase__   )
										]
										A    =      torch.cat(lowercase__    ,							dim=0   ).to(lowercase__   )
					else:
										A    =      torch.randn(lowercase__    ,							generator=lowercase__    ,							device=lowercase__    ,							dtype=lowercase__    ,							layout=lowercase__   ).to(lowercase__   )

					return latents
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if is_torch_version("<"    ,							"2.0.0"   ) or not hasattr(lowercase__    ,							"_dynamo"   ):
										return False
					return isinstance(lowercase__    ,							torch._dynamo.eval_frame.OptimizedModule   )








"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if not isinstance(lowercase__    ,							lowercase__   ):
										A    =      (
										    "operation can not be conducted on a object of type "
										    F"""{type(lowercase__   ).__name__}"""
										)
										raise TypeError(lowercase__   )
					A    =      ""
					for _ in range(0    ,							32   ):
										bit_string += str(number % 2   )
										A    =      number >> 1
					return bit_string
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if number < 0:
										raise ValueError("the value of input must be positive"   )
					elif isinstance(lowercase__    ,							lowercase__   ):
										raise TypeError("Input value must be a 'int' type"   )
					elif isinstance(lowercase__    ,							lowercase__   ):
										raise TypeError("'<' not supported between instances of 'str' and 'int'"   )
					A    =      0
					# iterator over [1 to 32],since we are dealing with 32 bit integer
					for _ in range(1    ,							33   ):
										# left shift the bits by unity
										A    =      result << 1
										# get the end bit
										A    =      number % 2
										# right shift the bits by unity
										A    =      number >> 1
										# add that bit to our ans
										A    =      result | end_bit
					return get_reverse_bit_string(lowercase__   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





