


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 ):
							UpperCamelCase_   :							str       =AltDiffusionPipeline
							UpperCamelCase_   :							List[str]       =TEXT_TO_IMAGE_PARAMS
							UpperCamelCase_   :							Any       =TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCamelCase_   :							List[str]       =TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase_   :							Optional[int]       =TEXT_TO_IMAGE_IMAGE_PARAMS


							def 			_A       (self					):
									torch.manual_seed(0					)
									__lowercase=			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      ,				)
									__lowercase=			DDIMScheduler(
									    beta_start=0.0_00_85      ,				beta_end=0.0_12      ,				beta_schedule='scaled_linear'      ,				clip_sample=lowerCAmelCase      ,				set_alpha_to_one=lowerCAmelCase      ,				)
									torch.manual_seed(0					)
									__lowercase=			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					)
									__lowercase=			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      ,				)
									__lowercase=			CLIPTextModel(lowerCAmelCase					)

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

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


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=0					):
									if str(lowerCAmelCase					).startswith('mps'					):
											__lowercase=			torch.manual_seed(lowerCAmelCase					)
									else:
											__lowercase=			torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
									__lowercase=			{
									    '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 			_A       (self					):
									super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)


							def 			_A       (self					):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3					)


							def 			_A       (self					):
									__lowercase=			'cpu'  # ensure determinism for the device-dependent torch.Generator

									__lowercase=			self.get_dummy_components()
									torch.manual_seed(0					)
									__lowercase=			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
									__lowercase=			RobertaSeriesModelWithTransformation(lowerCAmelCase					)
									__lowercase=			text_encoder

									__lowercase=			AltDiffusionPipeline(**lowerCAmelCase					)
									__lowercase=			alt_pipe.to(lowerCAmelCase					)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

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

									assert image.shape == (1, 6_4, 6_4, 3)
									__lowercase=			np.array(
									    [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]					)

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







							def 			_A       (self					):
									__lowercase=			'cpu'  # ensure determinism for the device-dependent torch.Generator

									__lowercase=			self.get_dummy_components()
									__lowercase=			PNDMScheduler(skip_prk_steps=lowerCAmelCase					)
									torch.manual_seed(0					)
									__lowercase=			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
									__lowercase=			RobertaSeriesModelWithTransformation(lowerCAmelCase					)
									__lowercase=			text_encoder
									__lowercase=			AltDiffusionPipeline(**lowerCAmelCase					)
									__lowercase=			alt_pipe.to(lowerCAmelCase					)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

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

									assert image.shape == (1, 6_4, 6_4, 3)
									__lowercase=			np.array(
									    [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]					)

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





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


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


							def 			_A       (self					):
									# make sure here that pndm scheduler skips prk
									__lowercase=			AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'      ,				safety_checker=lowerCAmelCase					)
									__lowercase=			alt_pipe.to(lowerCAmelCase					)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

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

									__lowercase=			output.images

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

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									__lowercase=			np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]					)

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







							def 			_A       (self					):
									__lowercase=			DDIMScheduler.from_pretrained('BAAI/AltDiffusion'      ,				subfolder='scheduler'					)

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

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

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

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

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									__lowercase=			np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]					)

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










from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			2
		__lowercase=			[]
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.append(lowercase__	)
		if n > 1:
				factors.append(lowercase__	)
		return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()






