



'''simple docstring'''

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 		lowerCamelCase__   (    A						, unittest.TestCase  ):




					'''simple docstring'''



					A_         =    UnCLIPPipeline
					A_         =    TEXT_TO_IMAGE_PARAMS - {
					    """negative_prompt""",
					    """height""",
					    """width""",
					    """negative_prompt_embeds""",
					    """guidance_scale""",
					    """prompt_embeds""",
					    """cross_attention_kwargs""",
					}
					A_         =    TEXT_TO_IMAGE_BATCH_PARAMS
					A_         =    [
					    """generator""",
					    """return_dict""",
					    """prior_num_inference_steps""",
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]
					A_         =    False




					@property
					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							str:



						'''simple docstring'''





						return 32




					@property
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							Dict:



						'''simple docstring'''





						return 32




					@property
					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							Dict:



						'''simple docstring'''





						return self.time_input_dim




					@property
					def     __UpperCAmelCase  (	self					:						Optional[int]       )	->							Tuple:



						'''simple docstring'''





						return self.time_input_dim * 4




					@property
					def     __UpperCAmelCase  (	self					:						str       )	->							Any:



						'''simple docstring'''





						return 100




					@property
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							Optional[int]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
						return tokenizer




					@property
					def     __UpperCAmelCase  (	self					:						str       )	->							Dict:



						'''simple docstring'''





						torch.manual_seed(0       )
						_lowercase       :							Union[str, Any]  	=		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=1000  ,	)
						return CLIPTextModelWithProjection(UpperCamelCase_       )




					@property
					def     __UpperCAmelCase  (	self					:						Dict       )	->							List[str]:



						'''simple docstring'''





						torch.manual_seed(0       )

						_lowercase       :							Optional[int]  	=		{
						    'num_attention_heads': 2,
						    'attention_head_dim': 12,
						    'embedding_dim': self.text_embedder_hidden_size,
						    'num_layers': 1,
						}

						_lowercase       :							Dict  	=		PriorTransformer(**UpperCamelCase_       )
						return model




					@property
					def     __UpperCAmelCase  (	self					:						Optional[int]       )	->							int:



						'''simple docstring'''





						torch.manual_seed(0       )

						_lowercase       :							Optional[Any]  	=		{
						    'clip_embeddings_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'cross_attention_dim': self.cross_attention_dim,
						}

						_lowercase       :							List[Any]  	=		UnCLIPTextProjModel(**UpperCamelCase_       )
						return model




					@property
					def     __UpperCAmelCase  (	self					:						List[str]       )	->							List[str]:



						'''simple docstring'''





						torch.manual_seed(0       )

						_lowercase       :							List[Any]  	=		{
						    '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       :							Union[str, Any]  	=		UNetaDConditionModel(**UpperCamelCase_       )
						return model




					@property
					def     __UpperCAmelCase  (	self					:						Optional[int]       )	->							Any:



						'''simple docstring'''





						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					:						str       )	->							Tuple:



						'''simple docstring'''





						torch.manual_seed(0       )

						_lowercase       :							Union[str, Any]  	=		UNetaDModel(**self.dummy_super_res_kwargs       )
						return model




					@property
					def     __UpperCAmelCase  (	self					:						int       )	->							Optional[int]:



						'''simple docstring'''





						torch.manual_seed(1       )

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




					def     __UpperCAmelCase  (	self					:						List[Any]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		self.dummy_prior
						_lowercase       :							Tuple  	=		self.dummy_decoder
						_lowercase       :							List[Any]  	=		self.dummy_text_proj
						_lowercase       :							List[Any]  	=		self.dummy_text_encoder
						_lowercase       :							Any  	=		self.dummy_tokenizer
						_lowercase       :							List[str]  	=		self.dummy_super_res_first
						_lowercase       :							Optional[int]  	=		self.dummy_super_res_last

						_lowercase       :							Any  	=		UnCLIPScheduler(
						    variance_type='fixed_small_log'  ,	prediction_type='sample'  ,	num_train_timesteps=1000  ,	clip_sample_range=5.0  ,	)

						_lowercase       :							List[Any]  	=		UnCLIPScheduler(
						    variance_type='learned_range'  ,	prediction_type='epsilon'  ,	num_train_timesteps=1000  ,	)

						_lowercase       :							Any  	=		UnCLIPScheduler(
						    variance_type='fixed_small_log'  ,	prediction_type='epsilon'  ,	num_train_timesteps=1000  ,	)

						_lowercase       :							int  	=		{
						    '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					:						Any  ,	UpperCamelCase_					:						List[str]  ,	UpperCamelCase_					:						List[str]=0       )	->							Tuple:



						'''simple docstring'''





						if str(UpperCamelCase_       ).startswith('mps'       ):
							_lowercase       :							Tuple  	=		torch.manual_seed(UpperCamelCase_       )
						else:
							_lowercase       :							Optional[int]  	=		torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
						_lowercase       :							Union[str, Any]  	=		{
						    '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					:						int       )	->							Any:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		'cpu'

						_lowercase       :							Union[str, Any]  	=		self.get_dummy_components()

						_lowercase       :							int  	=		self.pipeline_class(**UpperCamelCase_       )
						_lowercase       :							Any  	=		pipe.to(UpperCamelCase_       )

						pipe.set_progress_bar_config(disable=UpperCamelCase_       )

						_lowercase       :							str  	=		pipe(**self.get_dummy_inputs(UpperCamelCase_       )       )
						_lowercase       :							int  	=		output.images

						_lowercase       :							Optional[int]  	=		pipe(
						    **self.get_dummy_inputs(UpperCamelCase_       )  ,	return_dict=UpperCamelCase_  ,	)[0]

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

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

						_lowercase       :							Union[str, Any]  	=		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					:						Dict       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Any  	=		torch.device('cpu'       )



						class 		lowerCamelCase__   :




											'''simple docstring'''



											A_         =    1






						_lowercase       :							Dict  	=		self.get_dummy_components()

						_lowercase       :							Union[str, Any]  	=		self.pipeline_class(**UpperCamelCase_       )
						_lowercase       :							Union[str, Any]  	=		pipe.to(UpperCamelCase_       )

						_lowercase       :							Union[str, Any]  	=		components['prior']
						_lowercase       :							str  	=		components['decoder']
						_lowercase       :							int  	=		components['super_res_first']
						_lowercase       :							Tuple  	=		components['tokenizer']
						_lowercase       :							Union[str, Any]  	=		components['text_encoder']

						_lowercase       :							Dict  	=		torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
						_lowercase       :							str  	=		prior.dtype
						_lowercase       :							Any  	=		1

						_lowercase       :							Tuple  	=		(batch_size, prior.config.embedding_dim)
						_lowercase       :							Any  	=		pipe.prepare_latents(
						    UpperCamelCase_  ,	dtype=UpperCamelCase_  ,	device=UpperCamelCase_  ,	generator=UpperCamelCase_  ,	latents=UpperCamelCase_  ,	scheduler=DummyScheduler()       )
						_lowercase       :							Any  	=		(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
						_lowercase       :							int  	=		pipe.prepare_latents(
						    UpperCamelCase_  ,	dtype=UpperCamelCase_  ,	device=UpperCamelCase_  ,	generator=UpperCamelCase_  ,	latents=UpperCamelCase_  ,	scheduler=DummyScheduler()       )

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

						pipe.set_progress_bar_config(disable=UpperCamelCase_       )

						_lowercase       :							Dict  	=		'this is a prompt example'

						_lowercase       :							Optional[int]  	=		torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
						_lowercase       :							List[Any]  	=		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       :							List[Any]  	=		output.images

						_lowercase       :							Tuple  	=		tokenizer(
						    UpperCamelCase_  ,	padding='max_length'  ,	max_length=tokenizer.model_max_length  ,	return_tensors='pt'  ,	)
						_lowercase       :							str  	=		text_encoder(text_inputs.input_ids       )
						_lowercase       :							Union[str, Any]  	=		text_inputs.attention_mask

						_lowercase       :							Optional[int]  	=		torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
						_lowercase       :							List[str]  	=		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					:						List[Any]       )	->							Tuple:



						'''simple docstring'''





						_lowercase       :							str  	=		torch_device == 'cpu'

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




					@skip_mps
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						_lowercase       :							Any  	=		torch_device == 'cpu'
						_lowercase       :							List[str]  	=		True
						_lowercase       :							List[str]  	=		[
						    '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					:						Optional[Any]       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		[
						    '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       :							Tuple  	=		[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					:						int       )	->							Tuple:



						'''simple docstring'''





						return super().test_dict_tuple_outputs_equivalent()




					@skip_mps
					def     __UpperCAmelCase  (	self					:						Any       )	->							Tuple:



						'''simple docstring'''





						return super().test_save_load_local()




					@skip_mps
					def     __UpperCAmelCase  (	self					:						Optional[int]       )	->							List[str]:



						'''simple docstring'''





						return super().test_save_load_optional_components()






@nightly
class 		lowerCamelCase__   (    unittest.TestCase  ):




					'''simple docstring'''



					def     __UpperCAmelCase  (	self					:						Optional[Any]       )	->							Tuple:



						'''simple docstring'''





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




					def     __UpperCAmelCase  (	self					:						Dict       )	->							str:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/unclip/karlo_v1_alpha_horse_cpu.npy'       )

						_lowercase       :							Union[str, Any]  	=		UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'       )
						pipeline.set_progress_bar_config(disable=UpperCamelCase_       )

						_lowercase       :							Optional[Any]  	=		torch.manual_seed(0       )
						_lowercase       :							Union[str, Any]  	=		pipeline(
						    'horse'  ,	num_images_per_prompt=1  ,	generator=UpperCamelCase_  ,	output_type='np'  ,	)

						_lowercase       :							List[str]  	=		output.images[0]

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






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




					'''simple docstring'''



					def     __UpperCAmelCase  (	self					:						List[Any]       )	->							int:



						'''simple docstring'''





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




					def     __UpperCAmelCase  (	self					:						Dict       )	->							Dict:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/unclip/karlo_v1_alpha_horse_fp16.npy'       )

						_lowercase       :							int  	=		UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'  ,	torch_dtype=torch.floataa       )
						_lowercase       :							int  	=		pipeline.to(UpperCamelCase_       )
						pipeline.set_progress_bar_config(disable=UpperCamelCase_       )

						_lowercase       :							int  	=		torch.Generator(device='cpu'       ).manual_seed(0       )
						_lowercase       :							int  	=		pipeline(
						    'horse'  ,	generator=UpperCamelCase_  ,	output_type='np'  ,	)

						_lowercase       :							Optional[Any]  	=		output.images[0]

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

						assert_mean_pixel_difference(UpperCamelCase_  ,	UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							Dict:



						'''simple docstring'''





						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						_lowercase       :							Union[str, Any]  	=		UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'  ,	torch_dtype=torch.floataa       )
						_lowercase       :							str  	=		pipe.to(UpperCamelCase_       )
						pipe.set_progress_bar_config(disable=UpperCamelCase_       )
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

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





'''simple docstring'''

import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    CustomConfig




					def __init__(	self					:						int  ,	UpperCamelCase_					:						List[str]       )	->							int:



						'''simple docstring'''





						super().__init__(UpperCamelCase_       )
						_lowercase       :							List[Any]  	=		torch.nn.Linear(config.hidden_size  ,	config.hidden_size       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Any       )	->							Union[str, Any]:



						'''simple docstring'''





						return self.linear(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							str:



						'''simple docstring'''





						pass






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    NoSuperInitConfig




					def __init__(	self					:						Optional[int]  ,	UpperCamelCase_					:						Dict       )	->							Optional[Any]:



						'''simple docstring'''





						super().__init__(UpperCamelCase_       )
						_lowercase       :							List[str]  	=		torch.nn.Linear(config.attribute  ,	config.attribute       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						return self.linear(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int       )	->							List[str]:



						'''simple docstring'''





						pass
