
from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.openai.modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTModel,
		)



class       a_   :






			def __init__(			self							, __UpperCamelCase							, ):
									_lowercase									=				parent
									_lowercase									=				13
									_lowercase									=				7
									_lowercase									=				True
									_lowercase									=				True
									_lowercase									=				True
									_lowercase									=				True
									_lowercase									=				True
									_lowercase									=				99
									_lowercase									=				32
									_lowercase									=				2
									_lowercase									=				4
									_lowercase									=				37
									_lowercase									=				"""gelu"""
									_lowercase									=				0.1
									_lowercase									=				0.1
									_lowercase									=				512
									_lowercase									=				16
									_lowercase									=				2
									_lowercase									=				0.02
									_lowercase									=				3
									_lowercase									=				4
									_lowercase									=				None
									_lowercase									=				self.vocab_size - 1






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size     )

									_lowercase									=				None
									if self.use_input_mask:
															_lowercase									=				random_attention_mask([self.batch_size, self.seq_length]     )

									_lowercase									=				None
									if self.use_token_type_ids:
															_lowercase									=				ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size     )

									_lowercase									=				None
									if self.use_mc_token_ids:
															_lowercase									=				ids_tensor([self.batch_size, self.num_choices]							, self.seq_length     )

									_lowercase									=				None
									_lowercase									=				None
									_lowercase									=				None
									if self.use_labels:
															_lowercase									=				ids_tensor([self.batch_size]							, self.type_sequence_label_size     )
															_lowercase									=				ids_tensor([self.batch_size, self.seq_length]							, self.num_labels     )
															_lowercase									=				ids_tensor([self.batch_size]							, self.num_choices     )

									_lowercase									=				OpenAIGPTConfig(
									    vocab_size=self.vocab_size							, n_embd=self.hidden_size							, n_layer=self.num_hidden_layers							, n_head=self.num_attention_heads							, n_positions=self.max_position_embeddings							, pad_token_id=self.pad_token_id							, )

									_lowercase									=				ids_tensor([self.num_hidden_layers, self.num_attention_heads]							, 2     )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, *__UpperCamelCase     ):
									_lowercase									=				TFOpenAIGPTModel(config=__UpperCamelCase     )
									_lowercase									=				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									_lowercase									=				model(__UpperCamelCase     )

									_lowercase									=				[input_ids, input_mask]
									_lowercase									=				model(__UpperCamelCase     )

									_lowercase									=				model(__UpperCamelCase     )

									self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, *__UpperCamelCase     ):
									_lowercase									=				TFOpenAIGPTLMHeadModel(config=__UpperCamelCase     )
									_lowercase									=				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									_lowercase									=				model(__UpperCamelCase     )
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, *__UpperCamelCase     ):
									_lowercase									=				TFOpenAIGPTDoubleHeadsModel(config=__UpperCamelCase     )

									_lowercase									=				tf.tile(tf.expand_dims(__UpperCamelCase							, 1     )							, (1, self.num_choices, 1)     )
									_lowercase									=				tf.tile(tf.expand_dims(__UpperCamelCase							, 1     )							, (1, self.num_choices, 1)     )
									_lowercase									=				tf.tile(tf.expand_dims(__UpperCamelCase							, 1     )							, (1, self.num_choices, 1)     )

									_lowercase									=				{
									    """input_ids""": multiple_choice_inputs_ids,
									    """mc_token_ids""": mc_token_ids,
									    """attention_mask""": multiple_choice_input_mask,
									    """token_type_ids""": multiple_choice_token_type_ids,
									}
									_lowercase									=				model(__UpperCamelCase     )
									self.parent.assertEqual(
									    result.logits.shape							, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)     )
									self.parent.assertEqual(result.mc_logits.shape							, (self.batch_size, self.num_choices)     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, *__UpperCamelCase     ):
									_lowercase									=				self.num_labels
									_lowercase									=				ids_tensor([self.batch_size]							, self.type_sequence_label_size     )
									_lowercase									=				{
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									    """labels""": sequence_labels,
									}
									_lowercase									=				TFOpenAIGPTForSequenceClassification(__UpperCamelCase     )
									_lowercase									=				model(__UpperCamelCase     )
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.prepare_config_and_inputs()

									(
									    (
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,	(
									    _lowercase	
									)					,		
									)									=				config_and_inputs

									_lowercase									=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
									return config, inputs_dict







@require_tf
class       a_   ( _a				, _a				, unittest.TestCase						):
			a  : List[Any]				      =       (
			    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
			    if is_tf_available()
			    else ()
			)
			a  : Optional[int]				      =       (
			    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
			a  : str				      =       (
			    {
			        '''feature-extraction''': TFOpenAIGPTModel,
			        '''text-classification''': TFOpenAIGPTForSequenceClassification,
			        '''text-generation''': TFOpenAIGPTLMHeadModel,
			        '''zero-shot''': TFOpenAIGPTForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			a  : List[str]				      =       False
			a  : Optional[Any]				      =       False






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ):
									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
															# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
															# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
															# tiny config could not be created.
															return True

									return False






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				TFOpenAIGPTModelTester(self     )
									_lowercase									=				ConfigTester(self							, config_class=__UpperCamelCase							, n_embd=37     )






			def       UpperCamelCase_							(			self     ):
									self.config_tester.run_common_tests()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_model(*__UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_lm_head(*__UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_double_head(*__UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__UpperCamelCase     )






			@slow
			def       UpperCamelCase_							(			self     ):
									for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase									=				TFOpenAIGPTModel.from_pretrained(__UpperCamelCase     )
															self.assertIsNotNone(__UpperCamelCase     )







@require_tf
class       a_   ( unittest.TestCase						):






			@slow
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				TFOpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""     )
									_lowercase									=				tf.convert_to_tensor([[481, 4_735, 544]]							, dtype=tf.intaa     )  # the president is
									_lowercase									=				[
									    481,
									    4_735,
									    544,
									    246,
									    963,
									    870,
									    762,
									    239,
									    244,
									    40_477,
									    244,
									    249,
									    719,
									    881,
									    487,
									    544,
									    240,
									    244,
									    603,
									    481,
									]  # the president is a very good man. " \n " i\'m sure he is, " said the

									_lowercase									=				model.generate(__UpperCamelCase							, do_sample=__UpperCamelCase     )
									self.assertListEqual(output_ids[0].numpy().tolist()							, __UpperCamelCase     )



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