



"""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 			snake_case_						(						a_     ,a_     ,a_     ,unittest.TestCase				):
							__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 snake_case_  (   self      ):
								torch.manual_seed(0      )
								a_     :      List[Any] 						=     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_     :      Union[str, Any] 						=     DDIMScheduler(
								    beta_start=0.00_085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=a_			,     set_alpha_to_one=a_			,     )
								torch.manual_seed(0      )
								a_     :      int 						=     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_     :      List[str] 						=     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			,     )
								a_     :      List[str] 						=     CLIPTextModel(a_      )

								a_     :      int 						=     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"      )
								a_     :      List[Any] 						=     7_7

								a_     :      Union[str, Any] 						=     {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
								if str(a_      ).startswith("mps"      ):
									a_     :      int 						=     torch.manual_seed(a_      )
								else:
									a_     :      Tuple 						=     torch.Generator(device=a_      ).manual_seed(a_      )
								a_     :      List[Any] 						=     {
								    "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 snake_case_  (   self      ):
								super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )






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






							def snake_case_  (   self      ):
								a_     :      Any 						=     "cpu"  # ensure determinism for the device-dependent torch.Generator

								a_     :      List[str] 						=     self.get_dummy_components()
								torch.manual_seed(0      )
								a_     :      List[str] 						=     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
								a_     :      Optional[int] 						=     RobertaSeriesModelWithTransformation(a_      )
								a_     :      List[str] 						=     text_encoder

								a_     :      int 						=     AltDiffusionPipeline(**a_      )
								a_     :      Dict 						=     alt_pipe.to(a_      )
								alt_pipe.set_progress_bar_config(disable=a_      )

								a_     :      List[str] 						=     self.get_dummy_inputs(a_      )
								a_     :      Union[str, Any] 						=     "A photo of an astronaut"
								a_     :      Optional[Any] 						=     alt_pipe(**a_      )
								a_     :      Dict 						=     output.images
								a_     :      List[Any] 						=     image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								a_     :      Optional[int] 						=     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 snake_case_  (   self      ):
								a_     :      List[str] 						=     "cpu"  # ensure determinism for the device-dependent torch.Generator

								a_     :      Tuple 						=     self.get_dummy_components()
								a_     :      str 						=     PNDMScheduler(skip_prk_steps=a_      )
								torch.manual_seed(0      )
								a_     :      int 						=     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
								a_     :      Dict 						=     RobertaSeriesModelWithTransformation(a_      )
								a_     :      Union[str, Any] 						=     text_encoder
								a_     :      int 						=     AltDiffusionPipeline(**a_      )
								a_     :      Optional[int] 						=     alt_pipe.to(a_      )
								alt_pipe.set_progress_bar_config(disable=a_      )

								a_     :      List[Any] 						=     self.get_dummy_inputs(a_      )
								a_     :      Tuple 						=     alt_pipe(**a_      )
								a_     :      int 						=     output.images
								a_     :      Any 						=     image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								a_     :      Optional[int] 						=     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 			snake_case_						(						unittest.TestCase				):






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






							def snake_case_  (   self      ):
								# make sure here that pndm scheduler skips prk
								a_     :      Union[str, Any] 						=     AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"			,     safety_checker=a_      )
								a_     :      Optional[int] 						=     alt_pipe.to(a_      )
								alt_pipe.set_progress_bar_config(disable=a_      )

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

								a_     :      Optional[int] 						=     output.images

								a_     :      List[str] 						=     image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								a_     :      Optional[Any] 						=     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 snake_case_  (   self      ):
								a_     :      Any 						=     DDIMScheduler.from_pretrained("BAAI/AltDiffusion"			,     subfolder="scheduler"      )

								a_     :      int 						=     AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"			,     scheduler=a_			,     safety_checker=a_      )
								a_     :      Tuple 						=     alt_pipe.to(a_      )
								alt_pipe.set_progress_bar_config(disable=a_      )

								a_     :      List[Any] 						=     "A painting of a squirrel eating a burger"
								a_     :      str 						=     torch.manual_seed(0      )

								a_     :      str 						=     alt_pipe([prompt]			,     generator=a_			,     num_inference_steps=2			,     output_type="numpy"      )
								a_     :      Dict 						=     output.images

								a_     :      int 						=     image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								a_     :      Union[str, Any] 						=     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




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()