



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							lowerCAmelCase_		(		UpperCAmelCase__							,		UpperCAmelCase__							,		unittest.TestCase     ):
		SCREAMING_SNAKE_CASE_       :			str					       =	StableDiffusionXLPipeline
		SCREAMING_SNAKE_CASE_       :			Dict					       =	TEXT_TO_IMAGE_PARAMS
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	TEXT_TO_IMAGE_BATCH_PARAMS
		SCREAMING_SNAKE_CASE_       :			Optional[Any]					       =	TEXT_TO_IMAGE_IMAGE_PARAMS
		SCREAMING_SNAKE_CASE_       :			int					       =	TEXT_TO_IMAGE_IMAGE_PARAMS







		def 					a_  (    self  :   Any					)       ->		int:


						'''simple docstring'''







						torch.manual_seed(0					)
						_UpperCAmelCase						:  Tuple     					=	UNetaDConditionModel(
						    block_out_channels=(32, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=4       ,		out_channels=4       ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=__lowercase       ,		addition_embed_type='''text_time'''       ,		addition_time_embed_dim=8       ,		transformer_layers_per_block=(1, 2)       ,		projection_class_embeddings_input_dim=80       ,		cross_attention_dim=64       ,		)
						_UpperCAmelCase						:  Any     					=	EulerDiscreteScheduler(
						    beta_start=0.00_085       ,		beta_end=0.012       ,		steps_offset=1       ,		beta_schedule='''scaled_linear'''       ,		timestep_spacing='''leading'''       ,		)
						torch.manual_seed(0					)
						_UpperCAmelCase						:  Any     					=	AutoencoderKL(
						    block_out_channels=[32, 64]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,		latent_channels=4       ,		sample_size=128       ,		)
						torch.manual_seed(0					)
						_UpperCAmelCase						:  Dict     					=	CLIPTextConfig(
						    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=1000       ,		hidden_act='''gelu'''       ,		projection_dim=32       ,		)
						_UpperCAmelCase						:  str     					=	CLIPTextModel(__lowercase					)
						_UpperCAmelCase						:  List[str]     					=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,		local_files_only=__lowercase					)

						_UpperCAmelCase						:  Tuple     					=	CLIPTextModelWithProjection(__lowercase					)
						_UpperCAmelCase						:  Optional[Any]     					=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,		local_files_only=__lowercase					)

						_UpperCAmelCase						:  int     					=	{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''text_encoder_2''': text_encoder_a,
						    '''tokenizer_2''': tokenizer_a,
						    # "safety_checker": None,
						    # "feature_extractor": None,
						}
						return components







		def 					a_  (    self  :   int       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   List[str]=0					)       ->		Dict:


						'''simple docstring'''







						if str(__lowercase					).startswith('''mps'''					):
										_UpperCAmelCase						:  Dict     					=	torch.manual_seed(__lowercase					)
						else:
										_UpperCAmelCase						:  Dict     					=	torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
						_UpperCAmelCase						:  Dict     					=	{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 5.0,
						    '''output_type''': '''numpy''',
						}
						return inputs







		def 					a_  (    self  :   int					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						_UpperCAmelCase						:  Any     					=	self.get_dummy_components()
						_UpperCAmelCase						:  Optional[int]     					=	StableDiffusionXLPipeline(**__lowercase					)
						_UpperCAmelCase						:  Optional[int]     					=	sd_pipe.to(__lowercase					)
						sd_pipe.set_progress_bar_config(disable=__lowercase					)

						_UpperCAmelCase						:  Dict     					=	self.get_dummy_inputs(__lowercase					)
						_UpperCAmelCase						:  str     					=	sd_pipe(**__lowercase					).images
						_UpperCAmelCase						:  Any     					=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						_UpperCAmelCase						:  str     					=	np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747]					)

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







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


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	self.get_dummy_components()
						_UpperCAmelCase						:  List[Any]     					=	StableDiffusionXLPipeline(**__lowercase					)
						_UpperCAmelCase						:  List[Any]     					=	sd_pipe.to(__lowercase					)
						_UpperCAmelCase						:  int     					=	sd_pipe.to(__lowercase					)
						sd_pipe.set_progress_bar_config(disable=__lowercase					)

						# forward without prompt embeds
						_UpperCAmelCase						:  Optional[Any]     					=	self.get_dummy_inputs(__lowercase					)
						_UpperCAmelCase						:  List[str]     					=	3 * ['''this is a negative prompt''']
						_UpperCAmelCase						:  Union[str, Any]     					=	negative_prompt
						_UpperCAmelCase						:  Union[str, Any]     					=	3 * [inputs['''prompt''']]

						_UpperCAmelCase						:  str     					=	sd_pipe(**__lowercase					)
						_UpperCAmelCase						:  Any     					=	output.images[0, -3:, -3:, -1]

						# forward with prompt embeds
						_UpperCAmelCase						:  Tuple     					=	self.get_dummy_inputs(__lowercase					)
						_UpperCAmelCase						:  Dict     					=	3 * ['''this is a negative prompt''']
						_UpperCAmelCase						:  Dict     					=	3 * [inputs.pop('''prompt'''					)]

						(
						    _UpperCAmelCase	
						)						:  str     					=	sd_pipe.encode_prompt(__lowercase       ,		negative_prompt=__lowercase					)

						_UpperCAmelCase						:  List[Any]     					=	sd_pipe(
						    **__lowercase       ,		prompt_embeds=__lowercase       ,		negative_prompt_embeds=__lowercase       ,		pooled_prompt_embeds=__lowercase       ,		negative_pooled_prompt_embeds=__lowercase       ,		)
						_UpperCAmelCase						:  int     					=	output.images[0, -3:, -3:, -1]

						# make sure that it's equal
						assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()					).max() < 1E-4







		def 					a_  (    self  :   Union[str, Any]					)       ->		List[str]:


						'''simple docstring'''







						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)







		def 					a_  (    self  :   Tuple					)       ->		Tuple:


						'''simple docstring'''







						super().test_inference_batch_single_identical(expected_max_diff=3E-3					)





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







		def 					a_  (    self  :   Any					)       ->		int:


						'''simple docstring'''







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







		def 					a_  (    self  :   Optional[int]       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   List[str]="cpu"       ,		UpperCAmelCase_  :   int=torch.floataa       ,		UpperCAmelCase_  :   List[Any]=0					)       ->		Dict:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
						_UpperCAmelCase						:  str     					=	np.random.RandomState(__lowercase					).standard_normal((1, 4, 64, 64)					)
						_UpperCAmelCase						:  List[str]     					=	torch.from_numpy(__lowercase					).to(device=__lowercase       ,		dtype=__lowercase					)
						_UpperCAmelCase						:  Optional[Any]     					=	{
						    '''prompt''': '''a photograph of an astronaut riding a horse''',
						    '''latents''': latents,
						    '''generator''': generator,
						    '''num_inference_steps''': 3,
						    '''guidance_scale''': 7.5,
						    '''output_type''': '''numpy''',
						}
						return inputs







		def 					a_  (    self  :   Tuple					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''					)
						pipe.to(__lowercase					)
						pipe.set_progress_bar_config(disable=__lowercase					)

						_UpperCAmelCase						:  Any     					=	self.get_inputs(__lowercase					)
						_UpperCAmelCase						:  List[str]     					=	pipe(**__lowercase					).images
						_UpperCAmelCase						:  Dict     					=	image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						_UpperCAmelCase						:  Optional[int]     					=	np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]					)
						assert np.abs(image_slice - expected_slice					).max() < 7E-3




from transformers import PretrainedConfig







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     Dict:
										lowerCAmelCase_			:   List[Any]     					=					attribute
										super().__init__(**__lowercase				)







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					attribute