





"""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  _SCREAMING_SNAKE_CASE(       snake_case_   ,     snake_case_   ,     snake_case_   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  AltDiffusionPipeline
       SCREAMING_SNAKE_CASE_       :				Any     						=  TEXT_TO_IMAGE_PARAMS
       SCREAMING_SNAKE_CASE_       :				Any     						=  TEXT_TO_IMAGE_BATCH_PARAMS
       SCREAMING_SNAKE_CASE_       :				Any     						=  TEXT_TO_IMAGE_IMAGE_PARAMS
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  TEXT_TO_IMAGE_IMAGE_PARAMS




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  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       :Optional[Any]		    =  DDIMScheduler(
                 beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,beta_schedule='''scaled_linear'''		,clip_sample=_A		,set_alpha_to_one=_A		,)
             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :Union[str, 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							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  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		,)
             __SCREAMING_SNAKE_CASE       :Any		    =  CLIPTextModel(_A							)

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

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  {
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }
             return components




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)      ->				Dict:





             """simple docstring"""






             if str(_A							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.manual_seed(_A							)
             else:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.Generator(device=_A							).manual_seed(_A							)
             __SCREAMING_SNAKE_CASE       :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    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  'cpu'  # ensure determinism for the device-dependent torch.Generator

             __SCREAMING_SNAKE_CASE       :Any		    =  self.get_dummy_components()
             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :Any		    =  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
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  RobertaSeriesModelWithTransformation(_A							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  text_encoder

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  AltDiffusionPipeline(**_A							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  alt_pipe.to(_A							)
             alt_pipe.set_progress_bar_config(disable=_A							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_dummy_inputs(_A							)
             __SCREAMING_SNAKE_CASE       :int		    =  'A photo of an astronaut'
             __SCREAMING_SNAKE_CASE       :Tuple		    =  alt_pipe(**_A							)
             __SCREAMING_SNAKE_CASE       :Any		    =  output.images
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __SCREAMING_SNAKE_CASE       :Any		    =  np.array(
                 [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  'cpu'  # ensure determinism for the device-dependent torch.Generator

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

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

             assert image.shape == (1, 64, 64, 3)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  np.array(
                 [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2






@slow
@require_torch_gpu
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''		,safety_checker=_A							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  alt_pipe.to(_A							)
             alt_pipe.set_progress_bar_config(disable=_A							)

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

             __SCREAMING_SNAKE_CASE       :Any		    =  output.images

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_12, 5_12, 3)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''		,subfolder='''scheduler'''							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''		,scheduler=_A		,safety_checker=_A							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  alt_pipe.to(_A							)
             alt_pipe.set_progress_bar_config(disable=_A							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  'A painting of a squirrel eating a burger'
             __SCREAMING_SNAKE_CASE       :int		    =  torch.manual_seed(0							)

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

             __SCREAMING_SNAKE_CASE       :Dict		    =  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_12, 5_12, 3)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2






"""simple docstring"""
from torch import nn






class  _SCREAMING_SNAKE_CASE(       nn.Module  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  class_size
             __SCREAMING_SNAKE_CASE       :str		    =  embed_size
             # self.mlp1 = nn.Linear(embed_size, embed_size)
             # self.mlp2 = (nn.Linear(embed_size, class_size))
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  nn.Linear(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.mlp(SCREAMING_SNAKE_CASE__							)
             return logits