


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					(       lowercase__      , lowercase__      , lowercase__      , unittest.TestCase    ):
			lowercase						= AltDiffusionPipeline
			lowercase						= TEXT_TO_IMAGE_PARAMS
			lowercase						= TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase						= TEXT_TO_IMAGE_IMAGE_PARAMS
			lowercase						= TEXT_TO_IMAGE_IMAGE_PARAMS




			def   lowerCamelCase_					(						self							:		str						)     ->   int:







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					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       ,       )
						UpperCamelCase         =					DDIMScheduler(
						    beta_start=0.00_085       ,       beta_end=0.012       ,       beta_schedule="""scaled_linear"""       ,       clip_sample=__magic_name__       ,       set_alpha_to_one=__magic_name__       ,       )
						torch.manual_seed(0						)
						UpperCamelCase         =					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						)
						UpperCamelCase         =					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       ,       )
						UpperCamelCase         =					CLIPTextModel(__magic_name__						)

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

						UpperCamelCase         =					{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components




			def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[int]=0						)     ->   str:







						"""simple docstring"""







						if str(__magic_name__						).startswith("""mps"""						):
									UpperCamelCase         =					torch.manual_seed(__magic_name__						)
						else:
									UpperCamelCase         =					torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
						UpperCamelCase         =					{
						    """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							:		int						)     ->   Optional[Any]:







						"""simple docstring"""







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




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







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		List[Any]						)     ->   Optional[Any]:







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

						UpperCamelCase         =					self.get_dummy_components()
						torch.manual_seed(0						)
						UpperCamelCase         =					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
						UpperCamelCase         =					RobertaSeriesModelWithTransformation(__magic_name__						)
						UpperCamelCase         =					text_encoder

						UpperCamelCase         =					AltDiffusionPipeline(**__magic_name__						)
						UpperCamelCase         =					alt_pipe.to(__magic_name__						)
						alt_pipe.set_progress_bar_config(disable=__magic_name__						)

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

						assert image.shape == (1, 6_4, 6_4, 3)
						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Union[str, Any]						)     ->   Dict:







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

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

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

						assert image.shape == (1, 6_4, 6_4, 3)
						UpperCamelCase         =					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    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Dict						)     ->   int:







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		int						)     ->   List[str]:







						"""simple docstring"""







						UpperCamelCase         =					AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,       safety_checker=__magic_name__						)
						UpperCamelCase         =					alt_pipe.to(__magic_name__						)
						alt_pipe.set_progress_bar_config(disable=__magic_name__						)

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

						UpperCamelCase         =					output.images

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Dict						)     ->   List[str]:







						"""simple docstring"""







						UpperCamelCase         =					DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""       ,       subfolder="""scheduler"""						)

						UpperCamelCase         =					AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,       scheduler=__magic_name__       ,       safety_checker=__magic_name__						)
						UpperCamelCase         =					alt_pipe.to(__magic_name__						)
						alt_pipe.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					"""A painting of a squirrel eating a burger"""
						UpperCamelCase         =					torch.manual_seed(0						)

						UpperCamelCase         =					alt_pipe([prompt]       ,       generator=__magic_name__       ,       num_inference_steps=2       ,       output_type="""numpy"""						)
						UpperCamelCase         =					output.images

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						UpperCamelCase         =					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








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




