
"""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

_lowercase											=			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  _snake_case			(						cls				:       Tuple       ):
			return cls






def  _snake_case			(						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		, ):
	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(snake_case__		, snake_case__       ) 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(snake_case__		, snake_case__       ):
		A  				=				(1,) + shape[1:]
		A  				=				[
		    torch.randn(snake_case__		, generator=generator[i]		, device=snake_case__		, dtype=snake_case__		, layout=snake_case__       )
		    for i in range(snake_case__       )
		]
		A  				=				torch.cat(snake_case__		, dim=0       ).to(snake_case__       )
	else:
		A  				=				torch.randn(snake_case__		, generator=snake_case__		, device=snake_case__		, dtype=snake_case__		, layout=snake_case__       ).to(snake_case__       )

	return latents






def  _snake_case			(						snake_case__				:       List[Any]       ):
	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"""


from __future__ import annotations






def  _snake_case			(						snake_case__				:       list[list[int]]		, snake_case__				:       list[list[int]]       ):
	assert len(snake_case__       ) == len(snake_case__       )

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	A  				=				[-1] * n  # who the donor has donated to
	A  				=				[-1] * n  # who the recipient has received from
	A  				=				[0] * n

	while unmatched_donors:
		A  				=				unmatched_donors[0]
		A  				=				donor_pref[donor]
		A  				=				donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		A  				=				recipient_pref[recipient]
		A  				=				rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record