






import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ,     unittest.TestCase    ):


						'''simple docstring'''




						A   :      List[str]						 =    IFImgaImgPipeline
						A   :      int						 =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
						A   :      Any						 =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						A   :      List[Any]						 =    PipelineTesterMixin.required_optional_params - {'latents'}






						def 					_lowerCAmelCase				(		self )       -> str:
								return self._get_dummy_components()






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=0 )       -> int:
								if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
										snake_case_ :							Dict         =				torch.manual_seed(_SCREAMING_SNAKE_CASE )
								else:
										snake_case_ :							Optional[Any]         =				torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )

								snake_case_ :							Union[str, Any]         =				floats_tensor((1, 3, 32, 32)						, rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )

								snake_case_ :							Union[str, Any]         =				{
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								self._test_save_load_optional_components()






						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()						, reason="XFormers attention is only available with CUDA and `xformers` installed"						, )
						def 					_lowerCAmelCase				(		self )       -> str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )






						@unittest.skipIf(torch_device != "cuda"						, reason="float16 requires CUDA" )
						def 					_lowerCAmelCase				(		self )       -> Any:
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1e-1 )






						@unittest.skipIf(torch_device != "cuda"						, reason="float16 requires CUDA" )
						def 					_lowerCAmelCase				(		self )       -> str:
								super().test_floataa_inference(expected_max_diff=1e-1 )






						def 					_lowerCAmelCase				(		self )       -> Optional[Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )






						def 					_lowerCAmelCase				(		self )       -> int:
								self._test_save_load_local()






						def 					_lowerCAmelCase				(		self )       -> List[str]:
								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2						, )








from __future__ import annotations

from collections.abc import Generator

def    lowerCAmelCase__		(			_a				:	int			):
		if not isinstance(_a     ,					_a			) or n < 1:
				raise Exception("Sequence only defined for positive integers"			)

		yield n
		while n != 1:
				if n % 2 == 0:
						n //= 2
				else:
						snake_case_ :							List[str]         =				3 * n + 1
				yield n

def    lowerCAmelCase__		(			):
		snake_case_ :							Optional[int]         =				43
		snake_case_ :							Union[str, Any]         =				tuple(collatz_sequence(_a			)			)
		print(_a			)
		print(F'''Collatz sequence from {n} took {len(_a			)} steps.'''			)


if __name__ == "__main__":
							main()
