




'''simple docstring'''



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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				1
      _SCREAMING_SNAKE_CASE	=				3
      _SCREAMING_SNAKE_CASE	=				(3_2, 3_2)

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



   @property
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				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  ,				)
      return model



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				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  ,				)
      return model



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

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				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  ,				pad_token_id=1  ,				vocab_size=5_0_0_6  ,				)
      return RobertaSeriesModelWithTransformation(__lowerCamelCase   )



   @property
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      def extract(*__lowerCamelCase :     Optional[Any]  ,				**__lowerCamelCase :     List[str]   ):







         class 		lowercase_   :


            """simple docstring"""

            def __init__(   self :     List[Any]   ):

               """simple docstring"""
               _SCREAMING_SNAKE_CASE	=				torch.ones([0]   )



            def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     Optional[Any]   ):

               """simple docstring"""
               self.pixel_values.to(__lowerCamelCase   )
               return self


         return Out()

      return extract



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"cpu"  # ensure determinism for the device-dependent torch.Generator
      _SCREAMING_SNAKE_CASE	=				self.dummy_cond_unet
      _SCREAMING_SNAKE_CASE	=				PNDMScheduler(skip_prk_steps=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.dummy_vae
      _SCREAMING_SNAKE_CASE	=				self.dummy_text_encoder
      _SCREAMING_SNAKE_CASE	=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"   )
      _SCREAMING_SNAKE_CASE	=				7_7

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

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

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

      _SCREAMING_SNAKE_CASE	=				output.images

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

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

      assert image.shape == (1, 3_2, 3_2, 3)
      _SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.dummy_cond_unet
      _SCREAMING_SNAKE_CASE	=				PNDMScheduler(skip_prk_steps=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.dummy_vae
      _SCREAMING_SNAKE_CASE	=				self.dummy_text_encoder
      _SCREAMING_SNAKE_CASE	=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"   )
      _SCREAMING_SNAKE_CASE	=				7_7

      _SCREAMING_SNAKE_CASE	=				self.dummy_image.to(__lowerCamelCase   )

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

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

      _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=__lowerCamelCase  ,				num_inference_steps=2  ,				output_type="np"  ,				image=__lowerCamelCase  ,				).images

      assert image.shape == (1, 3_2, 3_2, 3)



   @unittest.skipIf(torch_device != "cuda"  ,				"This test requires a GPU"   )
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				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
      _SCREAMING_SNAKE_CASE	=				init_image.resize((7_6_0, 5_0_4)   )

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

      _SCREAMING_SNAKE_CASE	=				"A fantasy landscape, trending on artstation"

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

      _SCREAMING_SNAKE_CASE	=				image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

      assert image.shape == (5_0_4, 7_6_0, 3)
      _SCREAMING_SNAKE_CASE	=				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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

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



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"   )
      _SCREAMING_SNAKE_CASE	=				init_image.resize((7_6_8, 5_1_2)   )
      _SCREAMING_SNAKE_CASE	=				load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"   )

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

      _SCREAMING_SNAKE_CASE	=				"A fantasy landscape, trending on artstation"

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

      assert image.shape == (5_1_2, 7_6_8, 3)
      # img2img is flaky across GPUs even in fp32, so using MAE here
      assert np.abs(expected_image - image   ).max() < 1e-2







'''simple docstring'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_			=						abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Dict:
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(__A  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			str:
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   _SCREAMING_SNAKE_CASE	=				terminalreporter.config.getoption("--make-reports"  )
   if make_reports:
      pytest_terminal_summary_main(__A	,					id=__A  )

