




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     SCREAMING_SNAKE_CASE__		(_a    ,					_a    ,					_a    ,					unittest.TestCase			):
     lowercase_ :      int				= AltDiffusionPipeline
     lowercase_ :      Union[str, Any]				= TEXT_TO_IMAGE_PARAMS
     lowercase_ :      Dict				= TEXT_TO_IMAGE_BATCH_PARAMS
     lowercase_ :      List[str]				= TEXT_TO_IMAGE_IMAGE_PARAMS
     lowercase_ :      Any				= TEXT_TO_IMAGE_IMAGE_PARAMS
     def        A__      (						self	:		str						):



       """simple docstring"""
       torch.manual_seed(0						)
       lowerCAmelCase__										=      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       ,						)
       lowerCAmelCase__										=      DDIMScheduler(
           beta_start=0.0_0085       ,						beta_end=0.012       ,						beta_schedule='''scaled_linear'''       ,						clip_sample=__lowerCamelCase       ,						set_alpha_to_one=__lowerCamelCase       ,						)
       torch.manual_seed(0						)
       lowerCAmelCase__										=      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						)
       lowerCAmelCase__										=      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=50_02       ,						)
       lowerCAmelCase__										=      CLIPTextModel(__lowerCamelCase						)

       lowerCAmelCase__										=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''						)
       lowerCAmelCase__										=      77

       lowerCAmelCase__										=      {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		List[str]=0						):



       """simple docstring"""
       if str(__lowerCamelCase						).startswith('''mps'''						):
         lowerCAmelCase__										=      torch.manual_seed(__lowerCamelCase						)
       else:
         lowerCAmelCase__										=      torch.Generator(device=__lowerCamelCase						).manual_seed(__lowerCamelCase						)
       lowerCAmelCase__										=      {
           '''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	:		Dict						):



       """simple docstring"""
       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=3e-3						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       lowerCAmelCase__										=      self.get_dummy_components()
       torch.manual_seed(0						)
       lowerCAmelCase__										=      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=50_02       ,						)
       # TODO: remove after fixing the non-deterministic text encoder
       lowerCAmelCase__										=      RobertaSeriesModelWithTransformation(__lowerCamelCase						)
       lowerCAmelCase__										=      text_encoder

       lowerCAmelCase__										=      AltDiffusionPipeline(**__lowerCamelCase						)
       lowerCAmelCase__										=      alt_pipe.to(__lowerCamelCase						)
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

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

       assert image.shape == (1, 64, 64, 3)
       lowerCAmelCase__										=      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        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       lowerCAmelCase__										=      self.get_dummy_components()
       lowerCAmelCase__										=      PNDMScheduler(skip_prk_steps=__lowerCamelCase						)
       torch.manual_seed(0						)
       lowerCAmelCase__										=      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=50_02       ,						)
       # TODO: remove after fixing the non-deterministic text encoder
       lowerCAmelCase__										=      RobertaSeriesModelWithTransformation(__lowerCamelCase						)
       lowerCAmelCase__										=      text_encoder
       lowerCAmelCase__										=      AltDiffusionPipeline(**__lowerCamelCase						)
       lowerCAmelCase__										=      alt_pipe.to(__lowerCamelCase						)
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

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

       assert image.shape == (1, 64, 64, 3)
       lowerCAmelCase__										=      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     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     def        A__      (						self	:		Any						):



       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
     def        A__      (						self	:		str						):



       """simple docstring"""
       # make sure here that pndm scheduler skips prk
       lowerCAmelCase__										=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''       ,						safety_checker=__lowerCamelCase						)
       lowerCAmelCase__										=      alt_pipe.to(__lowerCamelCase						)
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

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

       lowerCAmelCase__										=      output.images

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

       assert image.shape == (1, 5_12, 5_12, 3)
       lowerCAmelCase__										=      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        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''       ,						subfolder='''scheduler'''						)

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

       lowerCAmelCase__										=      '''A painting of a squirrel eating a burger'''
       lowerCAmelCase__										=      torch.manual_seed(0						)

       lowerCAmelCase__										=      alt_pipe([prompt]       ,						generator=__lowerCamelCase       ,						num_inference_steps=2       ,						output_type='''numpy'''						)
       lowerCAmelCase__										=      output.images

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

       assert image.shape == (1, 5_12, 5_12, 3)
       lowerCAmelCase__										=      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







import numpy as np
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      int(np.ceil((x_end - xa) / h				)				)
  lowerCAmelCase__										=      np.zeros((n + 1,)				)
  lowerCAmelCase__										=      ya
  lowerCAmelCase__										=      xa

  for k in range(__lowerCAmelCase				):
    lowerCAmelCase__										=      f(__lowerCAmelCase   , y[k]				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + h   , y[k] + h * ka				)
    lowerCAmelCase__										=      y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
    x += h

  return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()

