
import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A		:   List[Any]         =    get_tests_dir('''fixtures/spiece.model''')



@require_sentencepiece
@require_tokenizers
class       a_   ( _a				, unittest.TestCase						):
			a  : Optional[int]				      =       ErnieMTokenizer
			a  : Optional[int]				      =       False
			a  : Optional[Any]				      =       True
			a  : Optional[Any]				      =       False
			a  : int				      =       False






			def       UpperCamelCase_							(			self     ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_lowercase									=				ErnieMTokenizer(__UpperCamelCase							, unk_token="""<unk>"""							, pad_token="""<pad>"""     )
									tokenizer.save_pretrained(self.tmpdirname     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									_lowercase									=				"""this is a test"""
									_lowercase									=				"""this is a test"""
									return input_text, output_text






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				"""<pad>"""
									_lowercase									=				0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase     )							, __UpperCamelCase     )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase     )							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]							, """<pad>"""     )
									self.assertEqual(vocab_keys[1]							, """<unk>"""     )
									self.assertEqual(vocab_keys[-1]							, """▁eloquent"""     )
									self.assertEqual(len(__UpperCamelCase     )							, 30_000     )






			def       UpperCamelCase_							(			self     ):
									self.assertEqual(self.get_tokenizer().vocab_size							, 30_000     )






			def       UpperCamelCase_							(			self     ):
									if not self.test_rust_tokenizer:
															return

									_lowercase									=				self.get_tokenizer()
									_lowercase									=				self.get_rust_tokenizer()

									_lowercase									=				"""I was born in 92000, and this is falsé."""

									_lowercase									=				tokenizer.tokenize(__UpperCamelCase     )
									_lowercase									=				rust_tokenizer.tokenize(__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )

									_lowercase									=				tokenizer.encode(__UpperCamelCase							, add_special_tokens=__UpperCamelCase     )
									_lowercase									=				rust_tokenizer.encode(__UpperCamelCase							, add_special_tokens=__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )

									_lowercase									=				self.get_rust_tokenizer()
									_lowercase									=				tokenizer.encode(__UpperCamelCase     )
									_lowercase									=				rust_tokenizer.encode(__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ErnieMTokenizer(__UpperCamelCase							, do_lower_case=__UpperCamelCase							, unk_token="""<unk>"""							, pad_token="""<pad>"""     )

									_lowercase									=				tokenizer.tokenize("""This is a test"""     )
									self.assertListEqual(__UpperCamelCase							, ["""▁this""", """▁is""", """▁a""", """▁test"""]     )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase     )							, [48, 25, 21, 1_289]     )

									_lowercase									=				tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
									# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
									self.assertListEqual(
									    __UpperCamelCase							, ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]     )
									_lowercase									=				tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, [31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]     )

									_lowercase									=				tokenizer.convert_ids_to_tokens(__UpperCamelCase     )
									self.assertListEqual(
									    __UpperCamelCase							, ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]							, )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ErnieMTokenizer(__UpperCamelCase							, unk_token="""<unk>"""							, pad_token="""<pad>"""     )

									_lowercase									=				tokenizer.encode("""sequence builders"""     )
									_lowercase									=				tokenizer.encode("""multi-sequence build"""     )

									_lowercase									=				tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     )
									_lowercase									=				tokenizer.build_inputs_with_special_tokens(__UpperCamelCase							, __UpperCamelCase     )

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
									    tokenizer.sep_token_id
									] + text_a + [tokenizer.sep_token_id]






			@slow
			def       UpperCamelCase_							(			self     ):
									# fmt: off
									_lowercase									=				{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=__UpperCamelCase							, model_name="""susnato/ernie-m-base_pytorch"""							, sequences=[
									        """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
									        """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
									        """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
									        """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
									        """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
									        """conditioning on both left and right context in all layers.""",
									        """The quick brown fox jumps over the lazy dog.""",
									    ]							, )



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class       a_   ( _a				, unittest.TestCase						):
			a  : List[str]				      =       UnCLIPPipeline
			a  : Optional[int]				      =       TEXT_TO_IMAGE_PARAMS - {
			    '''negative_prompt''',
			    '''height''',
			    '''width''',
			    '''negative_prompt_embeds''',
			    '''guidance_scale''',
			    '''prompt_embeds''',
			    '''cross_attention_kwargs''',
			}
			a  : Optional[int]				      =       TEXT_TO_IMAGE_BATCH_PARAMS
			a  : List[Any]				      =       [
			    '''generator''',
			    '''return_dict''',
			    '''prior_num_inference_steps''',
			    '''decoder_num_inference_steps''',
			    '''super_res_num_inference_steps''',
			]
			a  : Dict				      =       False






			@property
			def       UpperCamelCase_							(			self     ):
									return 32






			@property
			def       UpperCamelCase_							(			self     ):
									return 32






			@property
			def       UpperCamelCase_							(			self     ):
									return self.time_input_dim






			@property
			def       UpperCamelCase_							(			self     ):
									return self.time_input_dim * 4






			@property
			def       UpperCamelCase_							(			self     ):
									return 100






			@property
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
									return tokenizer






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				CLIPTextConfig(
									    bos_token_id=0							, eos_token_id=2							, hidden_size=self.text_embedder_hidden_size							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1_000							, )
									return CLIPTextModelWithProjection(__UpperCamelCase     )






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 12,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									_lowercase									=				PriorTransformer(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """clip_embeddings_dim""": self.text_embedder_hidden_size,
									    """time_embed_dim""": self.time_embed_dim,
									    """cross_attention_dim""": self.cross_attention_dim,
									}

									_lowercase									=				UnCLIPTextProjModel(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """sample_size""": 32,
									    # RGB in channels
									    """in_channels""": 3,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 6,
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": """identity""",
									}

									_lowercase									=				UNetaDConditionModel(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									return {
									    "sample_size": 64,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				UNetaDModel(**self.dummy_super_res_kwargs     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									# seeded differently to get different unet than `self.dummy_super_res_first`
									torch.manual_seed(1     )

									_lowercase									=				UNetaDModel(**self.dummy_super_res_kwargs     )
									return model






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.dummy_prior
									_lowercase									=				self.dummy_decoder
									_lowercase									=				self.dummy_text_proj
									_lowercase									=				self.dummy_text_encoder
									_lowercase									=				self.dummy_tokenizer
									_lowercase									=				self.dummy_super_res_first
									_lowercase									=				self.dummy_super_res_last

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""fixed_small_log"""							, prediction_type="""sample"""							, num_train_timesteps=1_000							, clip_sample_range=5.0							, )

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""learned_range"""							, prediction_type="""epsilon"""							, num_train_timesteps=1_000							, )

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""fixed_small_log"""							, prediction_type="""epsilon"""							, num_train_timesteps=1_000							, )

									_lowercase									=				{
									    """prior""": prior,
									    """decoder""": decoder,
									    """text_proj""": text_proj,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """super_res_first""": super_res_first,
									    """super_res_last""": super_res_last,
									    """prior_scheduler""": prior_scheduler,
									    """decoder_scheduler""": decoder_scheduler,
									    """super_res_scheduler""": super_res_scheduler,
									}

									return components






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )
									_lowercase									=				{
									    """prompt""": """horse""",
									    """generator""": generator,
									    """prior_num_inference_steps""": 2,
									    """decoder_num_inference_steps""": 2,
									    """super_res_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				"""cpu"""

									_lowercase									=				self.get_dummy_components()

									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									_lowercase									=				pipe.to(__UpperCamelCase     )

									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				pipe(**self.get_dummy_inputs(__UpperCamelCase     )     )
									_lowercase									=				output.images

									_lowercase									=				pipe(
									    **self.get_dummy_inputs(__UpperCamelCase     )							, return_dict=__UpperCamelCase							, )[0]

									_lowercase									=				image[0, -3:, -3:, -1]
									_lowercase									=				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									_lowercase									=				np.array(
									    [
									        0.99_97,
									        0.99_88,
									        0.00_28,
									        0.99_97,
									        0.99_84,
									        0.99_65,
									        0.00_29,
									        0.99_86,
									        0.00_25,
									    ]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch.device("""cpu"""     )



									class       a_   :
												a  : int				      =       1







									_lowercase									=				self.get_dummy_components()

									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									_lowercase									=				pipe.to(__UpperCamelCase     )

									_lowercase									=				components["""prior"""]
									_lowercase									=				components["""decoder"""]
									_lowercase									=				components["""super_res_first"""]
									_lowercase									=				components["""tokenizer"""]
									_lowercase									=				components["""text_encoder"""]

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				prior.dtype
									_lowercase									=				1

									_lowercase									=				(batch_size, prior.config.embedding_dim)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )
									_lowercase									=				(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )

									_lowercase									=				(
									    batch_size,
									    super_res_first.config.in_channels // 2,
									    super_res_first.config.sample_size,
									    super_res_first.config.sample_size,
									)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )

									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				"""this is a prompt example"""

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				pipe(
									    [prompt]							, generator=__UpperCamelCase							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, prior_latents=__UpperCamelCase							, decoder_latents=__UpperCamelCase							, super_res_latents=__UpperCamelCase							, output_type="""np"""							, )
									_lowercase									=				output.images

									_lowercase									=				tokenizer(
									    __UpperCamelCase							, padding="""max_length"""							, max_length=tokenizer.model_max_length							, return_tensors="""pt"""							, )
									_lowercase									=				text_encoder(text_inputs.input_ids     )
									_lowercase									=				text_inputs.attention_mask

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				pipe(
									    generator=__UpperCamelCase							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, prior_latents=__UpperCamelCase							, decoder_latents=__UpperCamelCase							, super_res_latents=__UpperCamelCase							, text_model_output=__UpperCamelCase							, text_attention_mask=__UpperCamelCase							, output_type="""np"""							, )[0]

									# make sure passing text embeddings manually is identical
									assert np.abs(image - image_from_text     ).max() < 1E-4






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase							, expected_max_diff=0.01     )






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch_device == """cpu"""
									_lowercase									=				True
									_lowercase									=				[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=__UpperCamelCase							, relax_max_difference=__UpperCamelCase							, additional_params_copy_to_batched_inputs=__UpperCamelCase							, )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									if torch_device == "mps":
															# TODO: MPS errors with larger batch sizes
															_lowercase									=				[2, 3]
															self._test_inference_batch_consistent(
															    batch_sizes=__UpperCamelCase							, additional_params_copy_to_batched_inputs=__UpperCamelCase							, )
									else:
															self._test_inference_batch_consistent(
															    additional_params_copy_to_batched_inputs=__UpperCamelCase     )






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_save_load_local()






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_save_load_optional_components()







@nightly
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_cpu.npy"""     )

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""     )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.manual_seed(0     )
									_lowercase									=				pipeline(
									    """horse"""							, num_images_per_prompt=1							, generator=__UpperCamelCase							, output_type="""np"""							, )

									_lowercase									=				output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image     ).max() < 1E-1







@slow
@require_torch_gpu
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_fp16.npy"""     )

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							, torch_dtype=torch.floataa     )
									_lowercase									=				pipeline.to(__UpperCamelCase     )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.Generator(device="""cpu"""     ).manual_seed(0     )
									_lowercase									=				pipeline(
									    """horse"""							, generator=__UpperCamelCase							, output_type="""np"""							, )

									_lowercase									=				output.images[0]

									assert image.shape == (256, 256, 3)

									assert_mean_pixel_difference(__UpperCamelCase							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							, torch_dtype=torch.floataa     )
									_lowercase									=				pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									_lowercase									=				pipe(
									    """horse"""							, num_images_per_prompt=1							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, output_type="""np"""							, )

									_lowercase									=				torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9