



import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Tuple      =			JukeboxTokenizer
			lowercase__      :  Union[str, Any]      =			{
			    """artist""": """Zac Brown Band""",
			    """genres""": """Country""",
			    """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
			}





			@require_torch
			def 		snake_case__			(				self	:       Union[str, Any]   )						->					str:
									"""simple docstring"""







									import torch

									__lowercase					=				JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""   )
									__lowercase					=				tokenizer(**self.metas   )["""input_ids"""]
									# fmt: off
									__lowercase					=				[
									    torch.tensor([[
									        0, 0, 0, 7_169, 507, 9, 76, 39, 31, 46, 76, 27,
									        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
									        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
									        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
									        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
									        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
									        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
									        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
									        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
									        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
									        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
									        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
									        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
									        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
									        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
									        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
									        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
									        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
									        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
									        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
									        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
									        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
									        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
									        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
									        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
									        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
									        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
									        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
									        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
									        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
									        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
									        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
									        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
									        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
									        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
									        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
									        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
									        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
									        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
									        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
									        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
									        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
									        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
									        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
									        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
									        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
									        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
									        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
									        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
									        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
									        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
									        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
									        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
									        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
									        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
									        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
									        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
									        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
									        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
									        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
									        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
									        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
									        76, 76]]   ),
									    torch.tensor([[0, 0, 0, 1_069, 11]]   ),
									    torch.tensor([[0, 0, 0, 1_069, 11]]   ),
									]
									# fmt: on
									self.assertTrue(torch.allclose(tokens[0]			,     EXPECTED_OUTPUT[0]   )   )
									self.assertTrue(torch.allclose(tokens[1]			,     EXPECTED_OUTPUT[1]   )   )
									self.assertTrue(torch.allclose(tokens[2]			,     EXPECTED_OUTPUT[2]   )   )







			@require_torch
			def 		snake_case__			(				self	:       str   )						->					List[str]:
									"""simple docstring"""







									import torch

									__lowercase					=				JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""   )
									__lowercase					=				tokenizer(**self.metas   )["""input_ids"""]
									# fmt: off
									__lowercase					=				[
									    torch.tensor([[
									        0, 0, 0, 1_069, 11, -1, -1, -1, -1, 9, 77, 39,
									        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
									        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
									        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
									        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
									        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
									        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
									        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
									        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
									        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
									        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
									        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
									        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
									        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
									        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
									        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
									        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
									        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
									        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
									        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
									        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
									        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
									        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
									        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
									        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
									        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
									        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
									        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
									        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
									        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
									        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
									        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
									        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
									        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
									        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
									        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
									        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
									        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
									        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
									        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
									        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
									        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
									        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
									        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
									        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
									        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
									        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
									        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
									        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
									        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
									        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
									        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
									        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
									        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
									        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
									        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
									        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
									        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
									        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
									        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
									        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
									        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
									        77, 77, 77, 77, 77, 77]]   ),
									    torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]   ),
									    torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]   ),
									]
									# fmt: on
									self.assertTrue(torch.allclose(tokens[0]			,     EXPECTED_OUTPUT[0]   )   )
									self.assertTrue(torch.allclose(tokens[1]			,     EXPECTED_OUTPUT[1]   )   )
									self.assertTrue(torch.allclose(tokens[2]			,     EXPECTED_OUTPUT[2]   )   )










import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			IFImgaImgSuperResolutionPipeline
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
			lowercase__      :  Tuple      =			PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}

									return inputs





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,     )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self._test_save_load_optional_components()





			@unittest.skipIf(torch_device != """cuda"""			,     reason="""float16 requires CUDA"""   )
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict:
									"""simple docstring"""







									super().test_save_load_floataa(expected_max_diff=1E-1   )





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2			,     )





