




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       :	Union[str, Any]			)			->					Optional[Any]:
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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       ,     )
										SCREAMING_SNAKE_CASE           =					DDIMScheduler(
										    beta_start=0.0_0085       ,     beta_end=0.012       ,     beta_schedule="""scaled_linear"""       ,     clip_sample=a       ,     set_alpha_to_one=a       ,     )
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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			)
										SCREAMING_SNAKE_CASE           =					CLIPTextConfig(
										    bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     projection_dim=32       ,     intermediate_size=37       ,     layer_norm_eps=1E-0_5       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=5_002       ,     )
										SCREAMING_SNAKE_CASE           =					CLIPTextModel(a			)

										SCREAMING_SNAKE_CASE           =					XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""			)
										SCREAMING_SNAKE_CASE           =					77

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





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	int       ,     a       :	int=0			)			->					Optional[Any]:
										if str(a			).startswith("""mps"""			):
													SCREAMING_SNAKE_CASE           =					torch.manual_seed(a			)
										else:
													SCREAMING_SNAKE_CASE           =					torch.Generator(device=a			).manual_seed(a			)
										SCREAMING_SNAKE_CASE           =					{
										    """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       :	Any			)			->					List[str]:
										super().test_attention_slicing_forward_pass(expected_max_diff=3E-3			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[Any]:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

										SCREAMING_SNAKE_CASE           =					self.get_dummy_components()
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					RobertaSeriesConfig(
										    hidden_size=32       ,     project_dim=32       ,     intermediate_size=37       ,     layer_norm_eps=1E-0_5       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     vocab_size=5_002       ,     )
										# TODO: remove after fixing the non-deterministic text encoder
										SCREAMING_SNAKE_CASE           =					RobertaSeriesModelWithTransformation(a			)
										SCREAMING_SNAKE_CASE           =					text_encoder

										SCREAMING_SNAKE_CASE           =					AltDiffusionPipeline(**a			)
										SCREAMING_SNAKE_CASE           =					alt_pipe.to(a			)
										alt_pipe.set_progress_bar_config(disable=a			)

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

										assert image.shape == (1, 64, 64, 3)
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

										SCREAMING_SNAKE_CASE           =					self.get_dummy_components()
										SCREAMING_SNAKE_CASE           =					PNDMScheduler(skip_prk_steps=a			)
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					RobertaSeriesConfig(
										    hidden_size=32       ,     project_dim=32       ,     intermediate_size=37       ,     layer_norm_eps=1E-0_5       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     vocab_size=5_002       ,     )
										# TODO: remove after fixing the non-deterministic text encoder
										SCREAMING_SNAKE_CASE           =					RobertaSeriesModelWithTransformation(a			)
										SCREAMING_SNAKE_CASE           =					text_encoder
										SCREAMING_SNAKE_CASE           =					AltDiffusionPipeline(**a			)
										SCREAMING_SNAKE_CASE           =					alt_pipe.to(a			)
										alt_pipe.set_progress_bar_config(disable=a			)

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

										assert image.shape == (1, 64, 64, 3)
										SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



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





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[Any]:
										# make sure here that pndm scheduler skips prk
										SCREAMING_SNAKE_CASE           =					AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,     safety_checker=a			)
										SCREAMING_SNAKE_CASE           =					alt_pipe.to(a			)
										alt_pipe.set_progress_bar_config(disable=a			)

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

										SCREAMING_SNAKE_CASE           =					output.images

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

										assert image.shape == (1, 512, 512, 3)
										SCREAMING_SNAKE_CASE           =					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       :	str			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""       ,     subfolder="""scheduler"""			)

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

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

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

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

										assert image.shape == (1, 512, 512, 3)
										SCREAMING_SNAKE_CASE           =					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












from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






