

'''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 					lowercase						(  a_,					a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Dict=   AltDiffusionPipeline
							_lowerCamelCase		:      Optional[Any]=   TEXT_TO_IMAGE_PARAMS
							_lowerCamelCase		:      str=   TEXT_TO_IMAGE_BATCH_PARAMS
							_lowerCamelCase		:      List[str]=   TEXT_TO_IMAGE_IMAGE_PARAMS
							_lowerCamelCase		:      Dict=   TEXT_TO_IMAGE_IMAGE_PARAMS


							def 						_snake_case							(			self)							->				List[Any]:
								torch.manual_seed(0)
								UpperCAmelCase_		:      List[Any]       	=					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    ,			)
								UpperCAmelCase_		:      str       	=					DDIMScheduler(
								    beta_start=0.00_085    ,			beta_end=0.012    ,			beta_schedule='scaled_linear'    ,			clip_sample=_snake_case    ,			set_alpha_to_one=_snake_case    ,			)
								torch.manual_seed(0)
								UpperCAmelCase_		:      Optional[Any]       	=					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)
								UpperCAmelCase_		:      List[str]       	=					CLIPTextConfig(
								    bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=32    ,			projection_dim=32    ,			intermediate_size=37    ,			layer_norm_eps=1e-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=5002    ,			)
								UpperCAmelCase_		:      Any       	=					CLIPTextModel(_snake_case)

								UpperCAmelCase_		:      Dict       	=					XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
								UpperCAmelCase_		:      str       	=					77

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


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=0)							->				Dict:
								if str(_snake_case).startswith('mps'):
									UpperCAmelCase_		:      List[Any]       	=					torch.manual_seed(_snake_case)
								else:
									UpperCAmelCase_		:      Optional[int]       	=					torch.Generator(device=_snake_case).manual_seed(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					{
								    '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)							->				Dict:
								super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)


							def 						_snake_case							(			self)							->				Tuple:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3)


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Union[str, Any]       	=					'cpu'  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase_		:      List[str]       	=					self.get_dummy_components()
								torch.manual_seed(0)
								UpperCAmelCase_		:      Dict       	=					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    ,			)
								# TODO: remove after fixing the non-deterministic text encoder
								UpperCAmelCase_		:      Optional[int]       	=					RobertaSeriesModelWithTransformation(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					text_encoder

								UpperCAmelCase_		:      Any       	=					AltDiffusionPipeline(**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					alt_pipe.to(_snake_case)
								alt_pipe.set_progress_bar_config(disable=_snake_case)

								UpperCAmelCase_		:      str       	=					self.get_dummy_inputs(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					'A photo of an astronaut'
								UpperCAmelCase_		:      List[Any]       	=					alt_pipe(**_snake_case)
								UpperCAmelCase_		:      Any       	=					output.images
								UpperCAmelCase_		:      List[Any]       	=					image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase_		:      str       	=					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)							->				Any:
								UpperCAmelCase_		:      int       	=					'cpu'  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase_		:      Dict       	=					self.get_dummy_components()
								UpperCAmelCase_		:      str       	=					PNDMScheduler(skip_prk_steps=_snake_case)
								torch.manual_seed(0)
								UpperCAmelCase_		:      str       	=					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    ,			)
								# TODO: remove after fixing the non-deterministic text encoder
								UpperCAmelCase_		:      Any       	=					RobertaSeriesModelWithTransformation(_snake_case)
								UpperCAmelCase_		:      int       	=					text_encoder
								UpperCAmelCase_		:      Dict       	=					AltDiffusionPipeline(**_snake_case)
								UpperCAmelCase_		:      str       	=					alt_pipe.to(_snake_case)
								alt_pipe.set_progress_bar_config(disable=_snake_case)

								UpperCAmelCase_		:      Optional[Any]       	=					self.get_dummy_inputs(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					alt_pipe(**_snake_case)
								UpperCAmelCase_		:      Any       	=					output.images
								UpperCAmelCase_		:      str       	=					image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase_		:      Union[str, Any]       	=					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 					lowercase						(  unittest.TestCase ):


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


							def 						_snake_case							(			self)							->				Union[str, Any]:
								# make sure here that pndm scheduler skips prk
								UpperCAmelCase_		:      Optional[int]       	=					AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'    ,			safety_checker=_snake_case)
								UpperCAmelCase_		:      Dict       	=					alt_pipe.to(_snake_case)
								alt_pipe.set_progress_bar_config(disable=_snake_case)

								UpperCAmelCase_		:      Dict       	=					'A painting of a squirrel eating a burger'
								UpperCAmelCase_		:      Tuple       	=					torch.manual_seed(0)
								UpperCAmelCase_		:      Tuple       	=					alt_pipe([prompt]    ,			generator=_snake_case    ,			guidance_scale=6.0    ,			num_inference_steps=20    ,			output_type='np')

								UpperCAmelCase_		:      Optional[int]       	=					output.images

								UpperCAmelCase_		:      Tuple       	=					image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase_		:      Union[str, 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)							->				List[str]:
								UpperCAmelCase_		:      str       	=					DDIMScheduler.from_pretrained('BAAI/AltDiffusion'    ,			subfolder='scheduler')

								UpperCAmelCase_		:      List[Any]       	=					AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'    ,			scheduler=_snake_case    ,			safety_checker=_snake_case)
								UpperCAmelCase_		:      Tuple       	=					alt_pipe.to(_snake_case)
								alt_pipe.set_progress_bar_config(disable=_snake_case)

								UpperCAmelCase_		:      Tuple       	=					'A painting of a squirrel eating a burger'
								UpperCAmelCase_		:      Any       	=					torch.manual_seed(0)

								UpperCAmelCase_		:      List[str]       	=					alt_pipe([prompt]    ,			generator=_snake_case    ,			num_inference_steps=2    ,			output_type='numpy')
								UpperCAmelCase_		:      List[Any]       	=					output.images

								UpperCAmelCase_		:      str       	=					image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase_		:      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'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






