





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					(					lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		AltDiffusionPipeline
			lowerCAmelCase_							  =		TEXT_TO_IMAGE_PARAMS
			lowerCAmelCase_							  =		TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCAmelCase_							  =		TEXT_TO_IMAGE_IMAGE_PARAMS
			lowerCAmelCase_							  =		TEXT_TO_IMAGE_IMAGE_PARAMS





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__              =	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 ,						)
				lowerCamelCase__              =	DDIMScheduler(
				    beta_start=0.0_0085 ,						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=[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							)
				lowerCamelCase__              =	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-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=5_0_0_2 ,						)
				lowerCamelCase__              =	CLIPTextModel(__lowerCAmelCase							)

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

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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=0							):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

				lowerCamelCase__              =	self.get_dummy_components()
				torch.manual_seed(0							)
				lowerCamelCase__              =	RobertaSeriesConfig(
				    hidden_size=3_2 ,						project_dim=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						vocab_size=5_0_0_2 ,						)
				# 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, 6_4, 6_4, 3)
				lowerCamelCase__              =	np.array(
				    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]							)

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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=3_2 ,						project_dim=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						vocab_size=5_0_0_2 ,						)
				# 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, 6_4, 6_4, 3)
				lowerCamelCase__              =	np.array(
				    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]							)

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






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

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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=2_0 ,						output_type='''np'''							)

				lowerCamelCase__              =	output.images

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				lowerCamelCase__              =	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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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_1_2, 5_1_2, 3)
				lowerCamelCase__              =	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










# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_a						=   open  # noqa: we just need to have a builtin inside this module to test it properly



