

'''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 							A__    (       _A     ,   _A     ,   _A     ,   unittest.TestCase  ):
  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 						A  (		self      :	Dict )	->    Optional[Any]:





       '''simple docstring'''






       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_00_85							,					beta_end=0.0_12							,					beta_schedule='scaled_linear'							,					clip_sample=UpperCamelCase__							,					set_alpha_to_one=UpperCamelCase__							,					)
       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-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=5002							,					)
       _SCREAMING_SNAKE_CASE							    =CLIPTextModel(UpperCamelCase__ )

       _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 						A  (		self      :	Optional[int]							,					_a      :	int							,					_a      :	Optional[Any]=0 )	->    Any:





       '''simple docstring'''






       if str(UpperCamelCase__ ).startswith('mps' ):
            _SCREAMING_SNAKE_CASE							    =torch.manual_seed(UpperCamelCase__ )
       else:
            _SCREAMING_SNAKE_CASE							    =torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
       _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 						A  (		self      :	Union[str, Any] )	->    int:





       '''simple docstring'''






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





  def 						A  (		self      :	List[Any] )	->    Union[str, Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[Any] )	->    str:





       '''simple docstring'''






       _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-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					vocab_size=5002							,					)
       # TODO: remove after fixing the non-deterministic text encoder
       _SCREAMING_SNAKE_CASE							    =RobertaSeriesModelWithTransformation(UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    =text_encoder

       _SCREAMING_SNAKE_CASE							    =AltDiffusionPipeline(**UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    =alt_pipe.to(UpperCamelCase__ )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

       _SCREAMING_SNAKE_CASE							    =self.get_dummy_inputs(UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    ='A photo of an astronaut'
       _SCREAMING_SNAKE_CASE							    =alt_pipe(**UpperCamelCase__ )
       _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(
           [0.5_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      :	Any )	->    int:





       '''simple docstring'''






       _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=UpperCamelCase__ )
       torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_SNAKE_CASE							    =RobertaSeriesModelWithTransformation(UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    =text_encoder
       _SCREAMING_SNAKE_CASE							    =AltDiffusionPipeline(**UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    =alt_pipe.to(UpperCamelCase__ )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

       _SCREAMING_SNAKE_CASE							    =self.get_dummy_inputs(UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    =alt_pipe(**UpperCamelCase__ )
       _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(
           [0.51_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      :	Union[str, Any] )	->    List[Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Tuple )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'							,					safety_checker=UpperCamelCase__ )
       _SCREAMING_SNAKE_CASE							    =alt_pipe.to(UpperCamelCase__ )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

       _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=UpperCamelCase__							,					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([0.10_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      :	str )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =DDIMScheduler.from_pretrained('BAAI/AltDiffusion'							,					subfolder='scheduler' )

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

       _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=UpperCamelCase__							,					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([0.40_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


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )					)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""",     type=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

  # positional
  parser.add_argument(
      """training_script""",     type=A_,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ),     )

  # rest from the training program
  parser.add_argument("""training_script_args""",     nargs=A_					)

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

  # Import training_script as a module.
  __magic_name__				     =       Path(args.training_script					)
  sys.path.append(str(script_fpath.parent.resolve()					)					)
  __magic_name__				     =       script_fpath.stem
  __magic_name__				     =       importlib.import_module(A_					)

  # Patch sys.argv
  __magic_name__				     =       [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores					)]

  xmp.spawn(mod._mp_fn,     args=(),     nprocs=args.num_cores					)


if __name__ == "__main__":
    main()

