

'''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			):

  '''simple docstring'''





  __lowerCAmelCase       :			Any     	= AltDiffusionPipeline
  __lowerCAmelCase       :			str     	= TEXT_TO_IMAGE_PARAMS
  __lowerCAmelCase       :			Optional[Any]     	= TEXT_TO_IMAGE_BATCH_PARAMS
  __lowerCAmelCase       :			List[Any]     	= TEXT_TO_IMAGE_IMAGE_PARAMS
  __lowerCAmelCase       :			Union[str, Any]     	= TEXT_TO_IMAGE_IMAGE_PARAMS
  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    torch.manual_seed(0  )
    UpperCAmelCase    				=						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 ,			)
    UpperCAmelCase    				=						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  )
    UpperCAmelCase    				=						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  )
    UpperCAmelCase    				=						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 ,			)
    UpperCAmelCase    				=						CLIPTextModel(a_  )

    UpperCAmelCase    				=						XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'  )
    UpperCAmelCase    				=						7_7

    UpperCAmelCase    				=						{
        '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  )				->					Dict:


    """simple docstring"""
    if str(a_  ).startswith('mps'  ):
      UpperCAmelCase    				=						torch.manual_seed(a_  )
    else:
      UpperCAmelCase    				=						torch.Generator(device=a_  ).manual_seed(a_  )
    UpperCAmelCase    				=						{
        '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  )				->					int:


    """simple docstring"""
    super().test_attention_slicing_forward_pass(expected_max_diff=3E-3  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    super().test_inference_batch_single_identical(expected_max_diff=3E-3  )
  def     snake_case_      (  self  )				->					str:


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

    UpperCAmelCase    				=						self.get_dummy_components()
    torch.manual_seed(0  )
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						RobertaSeriesModelWithTransformation(a_  )
    UpperCAmelCase    				=						text_encoder

    UpperCAmelCase    				=						AltDiffusionPipeline(**a_  )
    UpperCAmelCase    				=						alt_pipe.to(a_  )
    alt_pipe.set_progress_bar_config(disable=a_  )

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

    assert image.shape == (1, 6_4, 6_4, 3)
    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Any:


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

    UpperCAmelCase    				=						self.get_dummy_components()
    UpperCAmelCase    				=						PNDMScheduler(skip_prk_steps=a_  )
    torch.manual_seed(0  )
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						RobertaSeriesModelWithTransformation(a_  )
    UpperCAmelCase    				=						text_encoder
    UpperCAmelCase    				=						AltDiffusionPipeline(**a_  )
    UpperCAmelCase    				=						alt_pipe.to(a_  )
    alt_pipe.set_progress_bar_config(disable=a_  )

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

    assert image.shape == (1, 6_4, 6_4, 3)
    UpperCAmelCase    				=						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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Optional[int]:


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


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

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

    UpperCAmelCase    				=						output.images

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Union[str, Any]:


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

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

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

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

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    UpperCAmelCase    				=						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





'''simple docstring'''




from ..utils import DummyObject, requires_backends



class 							lowercase_						(		metaclass=a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= ["keras_nlp"]
  def __init__(  self ,			*a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    requires_backends(self ,			['keras_nlp']  )


