






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




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







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  1
           UpperCamelCase					       =  3
           UpperCamelCase					       =  (32, 32)

           UpperCamelCase					       =  floats_tensor((batch_size, num_channels) + sizes   ,						rng=random.Random(0 ) ).to(_SCREAMING_SNAKE_CASE )
           return image







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  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   ,						)
           return model







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  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   ,						)
           return model







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  RobertaSeriesConfig(
               hidden_size=32   ,						project_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=5006   ,						)
           return RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE )







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           def extract(*_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):





                 class 					A__     :

                      '''simple docstring'''






                      def __init__( self      :    int ):






                            """simple docstring"""




                            UpperCamelCase					       =  torch.ones([0] )







                      def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] ):






                            """simple docstring"""




                            self.pixel_values.to(_SCREAMING_SNAKE_CASE )
                            return self

                 return Out()

           return extract







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.dummy_cond_unet
           UpperCamelCase					       =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.dummy_vae
           UpperCamelCase					       =  self.dummy_text_encoder
           UpperCamelCase					       =  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta' )
           UpperCamelCase					       =  77

           UpperCamelCase					       =  self.dummy_image.to(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           UpperCamelCase					       =  AltDiffusionImgaImgPipeline(
               unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						feature_extractor=self.dummy_extractor   ,						)
           UpperCamelCase					       =  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,						do_normalize=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  alt_pipe.to(_SCREAMING_SNAKE_CASE )
           alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  'A painting of a squirrel eating a burger'
           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
           UpperCamelCase					       =  alt_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=6.0   ,						num_inference_steps=2   ,						output_type='np'   ,						image=_SCREAMING_SNAKE_CASE   ,						)

           UpperCamelCase					       =  output.images

           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
           UpperCamelCase					       =  alt_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=6.0   ,						num_inference_steps=2   ,						output_type='np'   ,						image=_SCREAMING_SNAKE_CASE   ,						return_dict=_SCREAMING_SNAKE_CASE   ,						)[0]

           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           UpperCamelCase					       =  np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9] )

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3







     @unittest.skipIf(torch_device != 'cuda'   ,						'This test requires a GPU' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  self.dummy_cond_unet
           UpperCamelCase					       =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.dummy_vae
           UpperCamelCase					       =  self.dummy_text_encoder
           UpperCamelCase					       =  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta' )
           UpperCamelCase					       =  77

           UpperCamelCase					       =  self.dummy_image.to(_SCREAMING_SNAKE_CASE )

           # put models in fp16
           UpperCamelCase					       =  unet.half()
           UpperCamelCase					       =  vae.half()
           UpperCamelCase					       =  bert.half()

           # make sure here that pndm scheduler skips prk
           UpperCamelCase					       =  AltDiffusionImgaImgPipeline(
               unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						feature_extractor=self.dummy_extractor   ,						)
           UpperCamelCase					       =  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,						do_normalize=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  alt_pipe.to(_SCREAMING_SNAKE_CASE )
           alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  'A painting of a squirrel eating a burger'
           UpperCamelCase					       =  torch.manual_seed(0 )
           UpperCamelCase					       =  alt_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=2   ,						output_type='np'   ,						image=_SCREAMING_SNAKE_CASE   ,						).images

           assert image.shape == (1, 32, 32, 3)







     @unittest.skipIf(torch_device != 'cuda'   ,						'This test requires a GPU' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg' )
           # resize to resolution that is divisible by 8 but not 16 or 32
           UpperCamelCase					       =  init_image.resize((760, 504) )

           UpperCamelCase					       =  'BAAI/AltDiffusion'
           UpperCamelCase					       =  AltDiffusionImgaImgPipeline.from_pretrained(
               _SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						)
           pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
           pipe.enable_attention_slicing()

           UpperCamelCase					       =  'A fantasy landscape, trending on artstation'

           UpperCamelCase					       =  torch.manual_seed(0 )
           UpperCamelCase					       =  pipe(
               prompt=_SCREAMING_SNAKE_CASE   ,						image=_SCREAMING_SNAKE_CASE   ,						strength=0.7_5   ,						guidance_scale=7.5   ,						generator=_SCREAMING_SNAKE_CASE   ,						output_type='np'   ,						)
           UpperCamelCase					       =  output.images[0]

           UpperCamelCase					       =  image[255:258, 383:386, -1]

           assert image.shape == (504, 760, 3)
           UpperCamelCase					       =  np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0] )

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





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

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg' )
           UpperCamelCase					       =  init_image.resize((768, 512) )
           UpperCamelCase					       =  load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy' )

           UpperCamelCase					       =  'BAAI/AltDiffusion'
           UpperCamelCase					       =  AltDiffusionImgaImgPipeline.from_pretrained(
               _SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						)
           pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
           pipe.enable_attention_slicing()

           UpperCamelCase					       =  'A fantasy landscape, trending on artstation'

           UpperCamelCase					       =  torch.manual_seed(0 )
           UpperCamelCase					       =  pipe(
               prompt=_SCREAMING_SNAKE_CASE   ,						image=_SCREAMING_SNAKE_CASE   ,						strength=0.7_5   ,						guidance_scale=7.5   ,						generator=_SCREAMING_SNAKE_CASE   ,						output_type='np'   ,						)
           UpperCamelCase					       =  output.images[0]

           assert image.shape == (512, 768, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image ).max() < 1E-2











def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			bool:
      """simple docstring"""

      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()



