


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class        _UpperCamelCase			(   a__      ,				a__      ,				a__      ,				unittest.TestCase						):







							'''simple docstring'''
							_A						=	AltDiffusionPipeline
							_A						=	TEXT_TO_IMAGE_PARAMS
							_A						=	TEXT_TO_IMAGE_BATCH_PARAMS
							_A						=	TEXT_TO_IMAGE_IMAGE_PARAMS
							_A						=	TEXT_TO_IMAGE_IMAGE_PARAMS







							def 		_UpperCAmelCase						(		self	:					List[str]		):
													torch.manual_seed(0		)
													_a     			=      UNetaDConditionModel(
													    block_out_channels=(3_2, 6_4)		,					layers_per_block=2		,					sample_size=3_2		,					in_channels=4		,					out_channels=4		,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,					cross_attention_dim=3_2		,					)
													_a     			=      DDIMScheduler(
													    beta_start=0.00085		,					beta_end=0.012		,					beta_schedule='scaled_linear'		,					clip_sample=lowerCamelCase_		,					set_alpha_to_one=lowerCamelCase_		,					)
													torch.manual_seed(0		)
													_a     			=      AutoencoderKL(
													    block_out_channels=[3_2, 6_4]		,					in_channels=3		,					out_channels=3		,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,					latent_channels=4		,					)

													# TODO: address the non-deterministic text encoder (fails for save-load tests)
													# torch.manual_seed(0)
													# text_encoder_config = RobertaSeriesConfig(
													#     hidden_size=32,
													#     project_dim=32,
													#     intermediate_size=37,
													#     layer_norm_eps=1e-05,
													#     num_attention_heads=4,
													#     num_hidden_layers=5,
													#     vocab_size=5002,
													# )
													# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

													torch.manual_seed(0		)
													_a     			=      CLIPTextConfig(
													    bos_token_id=0		,					eos_token_id=2		,					hidden_size=3_2		,					projection_dim=3_2		,					intermediate_size=3_7		,					layer_norm_eps=1e-0_5		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=5_0_0_2		,					)
													_a     			=      CLIPTextModel(lowerCamelCase_		)

													_a     			=      XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
													_a     			=      7_7

													_a     			=      {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vae': vae,
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'safety_checker': None,
													    'feature_extractor': None,
													}
													return components







							def 		_UpperCAmelCase						(		self	:					List[str]		,					SCREAMING_SNAKE_CASE_	:					List[str]		,					SCREAMING_SNAKE_CASE_	:					str=0		):
													if str(lowerCamelCase_		).startswith('mps'		):
																			_a     			=      torch.manual_seed(lowerCamelCase_		)
													else:
																			_a     			=      torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
													_a     			=      {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'generator': generator,
													    'num_inference_steps': 2,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs







							def 		_UpperCAmelCase						(		self	:					List[Any]		):
													super().test_attention_slicing_forward_pass(expected_max_diff=3e-3		)







							def 		_UpperCAmelCase						(		self	:					Tuple		):
													super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







							def 		_UpperCAmelCase						(		self	:					Any		):
													_a     			=      'cpu'  # ensure determinism for the device-dependent torch.Generator

													_a     			=      self.get_dummy_components()
													torch.manual_seed(0		)
													_a     			=      RobertaSeriesConfig(
													    hidden_size=3_2		,					project_dim=3_2		,					intermediate_size=3_7		,					layer_norm_eps=1e-0_5		,					num_attention_heads=4		,					num_hidden_layers=5		,					vocab_size=5_0_0_2		,					)
													# TODO: remove after fixing the non-deterministic text encoder
													_a     			=      RobertaSeriesModelWithTransformation(lowerCamelCase_		)
													_a     			=      text_encoder

													_a     			=      AltDiffusionPipeline(**lowerCamelCase_		)
													_a     			=      alt_pipe.to(lowerCamelCase_		)
													alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

													_a     			=      self.get_dummy_inputs(lowerCamelCase_		)
													_a     			=      'A photo of an astronaut'
													_a     			=      alt_pipe(**lowerCamelCase_		)
													_a     			=      output.images
													_a     			=      image[0, -3:, -3:, -1]

													assert image.shape == (1, 6_4, 6_4, 3)
													_a     			=      np.array(
													    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]		)

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







							def 		_UpperCAmelCase						(		self	:					List[str]		):
													_a     			=      'cpu'  # ensure determinism for the device-dependent torch.Generator

													_a     			=      self.get_dummy_components()
													_a     			=      PNDMScheduler(skip_prk_steps=lowerCamelCase_		)
													torch.manual_seed(0		)
													_a     			=      RobertaSeriesConfig(
													    hidden_size=3_2		,					project_dim=3_2		,					intermediate_size=3_7		,					layer_norm_eps=1e-0_5		,					num_attention_heads=4		,					num_hidden_layers=5		,					vocab_size=5_0_0_2		,					)
													# TODO: remove after fixing the non-deterministic text encoder
													_a     			=      RobertaSeriesModelWithTransformation(lowerCamelCase_		)
													_a     			=      text_encoder
													_a     			=      AltDiffusionPipeline(**lowerCamelCase_		)
													_a     			=      alt_pipe.to(lowerCamelCase_		)
													alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

													_a     			=      self.get_dummy_inputs(lowerCamelCase_		)
													_a     			=      alt_pipe(**lowerCamelCase_		)
													_a     			=      output.images
													_a     			=      image[0, -3:, -3:, -1]

													assert image.shape == (1, 6_4, 6_4, 3)
													_a     			=      np.array(
													    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]		)

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




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







							'''simple docstring'''







							def 		_UpperCAmelCase						(		self	:					Optional[Any]		):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







							def 		_UpperCAmelCase						(		self	:					Optional[int]		):
													# make sure here that pndm scheduler skips prk
													_a     			=      AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'		,					safety_checker=lowerCamelCase_		)
													_a     			=      alt_pipe.to(lowerCamelCase_		)
													alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

													_a     			=      'A painting of a squirrel eating a burger'
													_a     			=      torch.manual_seed(0		)
													_a     			=      alt_pipe([prompt]		,					generator=lowerCamelCase_		,					guidance_scale=6.0		,					num_inference_steps=2_0		,					output_type='np'		)

													_a     			=      output.images

													_a     			=      image[0, -3:, -3:, -1]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)
													_a     			=      np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]		)

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







							def 		_UpperCAmelCase						(		self	:					Tuple		):
													_a     			=      DDIMScheduler.from_pretrained('BAAI/AltDiffusion'		,					subfolder='scheduler'		)

													_a     			=      AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'		,					scheduler=lowerCamelCase_		,					safety_checker=lowerCamelCase_		)
													_a     			=      alt_pipe.to(lowerCamelCase_		)
													alt_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

													_a     			=      'A painting of a squirrel eating a burger'
													_a     			=      torch.manual_seed(0		)

													_a     			=      alt_pipe([prompt]		,					generator=lowerCamelCase_		,					num_inference_steps=2		,					output_type='numpy'		)
													_a     			=      output.images

													_a     			=      image[0, -3:, -3:, -1]

													assert image.shape == (1, 5_1_2, 5_1_2, 3)
													_a     			=      np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]		)

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






'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 						a__							(				a__							,							a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= AltDiffusionPipeline
							lowercase__     :  Dict      		= TEXT_TO_IMAGE_PARAMS
							lowercase__     :  str      		= TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase__     :  Any      		= TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__     :  List[str]      		= TEXT_TO_IMAGE_IMAGE_PARAMS







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      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''')   ,       cross_attention_dim=32   ,       )
														lowerCAmelCase__			  =      DDIMScheduler(
														    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=lowerCamelCase_   ,       set_alpha_to_one=lowerCamelCase_   ,       )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      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   ,       )

														# TODO: address the non-deterministic text encoder (fails for save-load tests)
														# torch.manual_seed(0)
														# text_encoder_config = RobertaSeriesConfig(
														#     hidden_size=32,
														#     project_dim=32,
														#     intermediate_size=37,
														#     layer_norm_eps=1e-05,
														#     num_attention_heads=4,
														#     num_hidden_layers=5,
														#     vocab_size=5002,
														# )
														# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

														torch.manual_seed(0  )
														lowerCAmelCase__			  =      CLIPTextConfig(
														    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=50_02   ,       )
														lowerCAmelCase__			  =      CLIPTextModel(lowerCamelCase_  )

														lowerCAmelCase__			  =      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
														lowerCAmelCase__			  =      77

														lowerCAmelCase__			  =      {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=0  )			->				List[str]:
														if str(lowerCamelCase_  ).startswith('''mps'''  ):
																					lowerCAmelCase__			  =      torch.manual_seed(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

														lowerCAmelCase__			  =      self.get_dummy_components()
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      RobertaSeriesConfig(
														    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=50_02   ,       )
														# TODO: remove after fixing the non-deterministic text encoder
														lowerCAmelCase__			  =      RobertaSeriesModelWithTransformation(lowerCamelCase_  )
														lowerCAmelCase__			  =      text_encoder

														lowerCAmelCase__			  =      AltDiffusionPipeline(**lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_dummy_inputs(lowerCamelCase_  )
														lowerCAmelCase__			  =      '''A photo of an astronaut'''
														lowerCAmelCase__			  =      alt_pipe(**lowerCamelCase_  )
														lowerCAmelCase__			  =      output.images
														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCAmelCase__			  =      np.array(
														    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

														lowerCAmelCase__			  =      self.get_dummy_components()
														lowerCAmelCase__			  =      PNDMScheduler(skip_prk_steps=lowerCamelCase_  )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      RobertaSeriesConfig(
														    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=50_02   ,       )
														# TODO: remove after fixing the non-deterministic text encoder
														lowerCAmelCase__			  =      RobertaSeriesModelWithTransformation(lowerCamelCase_  )
														lowerCAmelCase__			  =      text_encoder
														lowerCAmelCase__			  =      AltDiffusionPipeline(**lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_dummy_inputs(lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe(**lowerCamelCase_  )
														lowerCAmelCase__			  =      output.images
														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCAmelCase__			  =      np.array(
														    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]  )

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




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





							'''simple docstring'''






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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# make sure here that pndm scheduler skips prk
														lowerCAmelCase__			  =      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      alt_pipe([prompt]   ,       generator=lowerCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''  )

														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''  )

														lowerCAmelCase__			  =      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=lowerCamelCase_   ,       safety_checker=lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )

														lowerCAmelCase__			  =      alt_pipe([prompt]   ,       generator=lowerCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''  )
														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]  )

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