

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ImageGPTImageProcessor







class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def __init__(			self    : Optional[int]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Dict=7				,				UpperCAmelCase__    : List[str]=3				,				UpperCAmelCase__    : Union[str, Any]=1_8				,				UpperCAmelCase__    : Any=3_0				,				UpperCAmelCase__    : Optional[int]=4_0_0				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : Tuple=True				,				)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   size if size is not None else {"""height""": 1_8, """width""": 1_8}
        SCREAMING_SNAKE_CASE :      List[str]       =   parent
        SCREAMING_SNAKE_CASE :      Optional[int]       =   batch_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_channels
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   min_resolution
        SCREAMING_SNAKE_CASE :      Tuple       =   max_resolution
        SCREAMING_SNAKE_CASE :      str       =   do_resize
        SCREAMING_SNAKE_CASE :      int       =   size
        SCREAMING_SNAKE_CASE :      Tuple       =   do_normalize




  def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
        """simple docstring"""







        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                    [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                ]		),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }







@require_torch
@require_vision
class        a__       (   UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[Any]			       =ImageGPTImageProcessor if is_vision_available() else None


  def  _lowercase      (			self    : Any		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   ImageGPTImageProcessingTester(self		)


  @property
  def  _lowercase      (			self    : Any		)					->Optional[int]:
        """simple docstring"""







        return self.image_processor_tester.prepare_image_processor_dict()


  def  _lowercase      (			self    : Optional[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""clusters"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""do_resize"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""size"""		)		)
        self.assertTrue(hasattr(UpperCAmelCase__				,				"""do_normalize"""		)		)


  def  _lowercase      (			self    : Union[str, Any]		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size				,				{"""height""": 1_8, """width""": 1_8}		)

        SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2		)
        self.assertEqual(image_processor.size				,				{"""height""": 4_2, """width""": 4_2}		)


  def  _lowercase      (			self    : Tuple		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   self.image_processing_class(**self.image_processor_dict		)
        SCREAMING_SNAKE_CASE :      int       =   json.loads(image_processor.to_json_string()		)
        for key, value in self.image_processor_dict.items():
              if key == "clusters":
                    self.assertTrue(np.array_equal(UpperCAmelCase__				,				obj[key]		)		)
              else:
                    self.assertEqual(obj[key]				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : str		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.image_processing_class(**self.image_processor_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE :      Dict       =   os.path.join(UpperCAmelCase__				,				"""image_processor.json"""		)
              image_processor_first.to_json_file(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Optional[int]       =   self.image_processing_class.from_json_file(UpperCAmelCase__		).to_dict()

        SCREAMING_SNAKE_CASE :      Tuple       =   image_processor_first.to_dict()
        for key, value in image_processor_first.items():
              if key == "clusters":
                    self.assertTrue(np.array_equal(UpperCAmelCase__				,				image_processor_second[key]		)		)
              else:
                    self.assertEqual(image_processor_first[key]				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : List[str]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class(**self.image_processor_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
              image_processor_first.save_pretrained(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      str       =   self.image_processing_class.from_pretrained(UpperCAmelCase__		).to_dict()

        SCREAMING_SNAKE_CASE :      str       =   image_processor_first.to_dict()
        for key, value in image_processor_first.items():
              if key == "clusters":
                    self.assertTrue(np.array_equal(UpperCAmelCase__				,				image_processor_second[key]		)		)
              else:
                    self.assertEqual(image_processor_first[key]				,				UpperCAmelCase__		)




  @unittest.skip("""ImageGPT requires clusters at initialization"""		)
  def  _lowercase      (			self    : Optional[int]		)					->Optional[int]:
        """simple docstring"""







        pass






def 					__lowercase      (     )     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      str       =   load_dataset("""hf-internal-testing/fixtures_image_utils"""						,    split="""test"""			)

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   Image.open(dataset[4]["""file"""]			)
      SCREAMING_SNAKE_CASE :      int       =   Image.open(dataset[5]["""file"""]			)

      SCREAMING_SNAKE_CASE :      List[str]       =   [imagea, imagea]

      return images







@require_vision
@require_torch
class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  @slow
  def  _lowercase      (			self    : str		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""		)

        SCREAMING_SNAKE_CASE :      str       =   prepare_images()

        # test non-batched
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_processing(images[0]				,				return_tensors="""pt"""		)

        self.assertIsInstance(encoding.input_ids				,				torch.LongTensor		)
        self.assertEqual(encoding.input_ids.shape				,				(1, 1_0_2_4)		)

        SCREAMING_SNAKE_CASE :      str       =   [3_0_6, 1_9_1, 1_9_1]
        self.assertEqual(encoding.input_ids[0, :3].tolist()				,				UpperCAmelCase__		)

        # test batched
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_processing(UpperCAmelCase__				,				return_tensors="""pt"""		)

        self.assertIsInstance(encoding.input_ids				,				torch.LongTensor		)
        self.assertEqual(encoding.input_ids.shape				,				(2, 1_0_2_4)		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   [3_0_3, 1_3, 1_3]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()				,				UpperCAmelCase__		)







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  _lowercase      (			self    : Union[str, Any]		)					->List[Any]:
        """simple docstring"""







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


  @property
  def  _lowercase      (			self    : str		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   1
        SCREAMING_SNAKE_CASE :      Dict       =   3
        SCREAMING_SNAKE_CASE :      List[Any]       =   (3_2, 3_2)

        SCREAMING_SNAKE_CASE :      Any       =   floats_tensor((batch_size, num_channels) + sizes				,				rng=random.Random(0		)		).to(UpperCAmelCase__		)
        return image


  @property
  def  _lowercase      (			self    : Tuple		)					->List[Any]:
        """simple docstring"""







        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE :      str       =   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  _lowercase      (			self    : str		)					->int:
        """simple docstring"""







        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE :      Tuple       =   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  _lowercase      (			self    : Tuple		)					->Union[str, Any]:
        """simple docstring"""







        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE :      str       =   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(UpperCAmelCase__		)


  @property
  def  _lowercase      (			self    : Union[str, Any]		)					->List[str]:
        """simple docstring"""







        def extract(*UpperCAmelCase__    : List[Any]				,				**UpperCAmelCase__    : Tuple		):







              class        a__       :







                """simple docstring"""




                def __init__(			self    : Tuple		)					->Dict:
                      """simple docstring"""







                      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.ones([0]		)




                def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : Dict		)					->Optional[Any]:
                      """simple docstring"""







                      self.pixel_values.to(UpperCAmelCase__		)
                      return self


              return Out()

        return extract


  def  _lowercase      (			self    : List[str]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.dummy_cond_unet
        SCREAMING_SNAKE_CASE :      str       =   PNDMScheduler(skip_prk_steps=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   self.dummy_vae
        SCREAMING_SNAKE_CASE :      str       =   self.dummy_text_encoder
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   7_7

        SCREAMING_SNAKE_CASE :      str       =   self.dummy_image.to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE :      List[str]       =   AltDiffusionImgaImgPipeline(
            unet=UpperCAmelCase__				,				scheduler=UpperCAmelCase__				,				vae=UpperCAmelCase__				,				text_encoder=UpperCAmelCase__				,				tokenizer=UpperCAmelCase__				,				safety_checker=UpperCAmelCase__				,				feature_extractor=self.dummy_extractor				,				)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,				do_normalize=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Tuple       =   alt_pipe.to(UpperCAmelCase__		)
        alt_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      str       =   """A painting of a squirrel eating a burger"""
        SCREAMING_SNAKE_CASE :      Tuple       =   torch.Generator(device=UpperCAmelCase__		).manual_seed(0		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   alt_pipe(
            [prompt]				,				generator=UpperCAmelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="""np"""				,				image=UpperCAmelCase__				,				)

        SCREAMING_SNAKE_CASE :      List[str]       =   output.images

        SCREAMING_SNAKE_CASE :      List[Any]       =   torch.Generator(device=UpperCAmelCase__		).manual_seed(0		)
        SCREAMING_SNAKE_CASE :      Any       =   alt_pipe(
            [prompt]				,				generator=UpperCAmelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="""np"""				,				image=UpperCAmelCase__				,				return_dict=UpperCAmelCase__				,				)[0]

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE :      int       =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        SCREAMING_SNAKE_CASE :      str       =   np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99]		)

        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  _lowercase      (			self    : Optional[int]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.dummy_cond_unet
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   PNDMScheduler(skip_prk_steps=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   self.dummy_vae
        SCREAMING_SNAKE_CASE :      int       =   self.dummy_text_encoder
        SCREAMING_SNAKE_CASE :      List[Any]       =   XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""		)
        SCREAMING_SNAKE_CASE :      Tuple       =   7_7

        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.dummy_image.to(UpperCAmelCase__		)

        # put models in fp16
        SCREAMING_SNAKE_CASE :      List[Any]       =   unet.half()
        SCREAMING_SNAKE_CASE :      Tuple       =   vae.half()
        SCREAMING_SNAKE_CASE :      str       =   bert.half()

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE :      str       =   AltDiffusionImgaImgPipeline(
            unet=UpperCAmelCase__				,				scheduler=UpperCAmelCase__				,				vae=UpperCAmelCase__				,				text_encoder=UpperCAmelCase__				,				tokenizer=UpperCAmelCase__				,				safety_checker=UpperCAmelCase__				,				feature_extractor=self.dummy_extractor				,				)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,				do_normalize=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   alt_pipe.to(UpperCAmelCase__		)
        alt_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      int       =   """A painting of a squirrel eating a burger"""
        SCREAMING_SNAKE_CASE :      List[str]       =   torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE :      Any       =   alt_pipe(
            [prompt]				,				generator=UpperCAmelCase__				,				num_inference_steps=2				,				output_type="""np"""				,				image=UpperCAmelCase__				,				).images

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




  @unittest.skipIf(torch_device != """cuda"""				,				"""This test requires a GPU"""		)
  def  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   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 :      Union[str, Any]       =   init_image.resize((7_6_0, 5_0_4)		)

        SCREAMING_SNAKE_CASE :      int       =   """BAAI/AltDiffusion"""
        SCREAMING_SNAKE_CASE :      List[str]       =   AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCAmelCase__				,				safety_checker=UpperCAmelCase__				,				)
        pipe.to(UpperCAmelCase__		)
        pipe.set_progress_bar_config(disable=UpperCAmelCase__		)
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE :      int       =   """A fantasy landscape, trending on artstation"""

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   pipe(
            prompt=UpperCAmelCase__				,				image=UpperCAmelCase__				,				strength=0.75				,				guidance_scale=7.5				,				generator=UpperCAmelCase__				,				output_type="""np"""				,				)
        SCREAMING_SNAKE_CASE :      Tuple       =   output.images[0]

        SCREAMING_SNAKE_CASE :      Tuple       =   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 :      List[str]       =   np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00]		)

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







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







  """simple docstring"""




  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







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




  def  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      Tuple       =   """BAAI/AltDiffusion"""
        SCREAMING_SNAKE_CASE :      str       =   AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCAmelCase__				,				safety_checker=UpperCAmelCase__				,				)
        pipe.to(UpperCAmelCase__		)
        pipe.set_progress_bar_config(disable=UpperCAmelCase__		)
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE :      Dict       =   """A fantasy landscape, trending on artstation"""

        SCREAMING_SNAKE_CASE :      Optional[int]       =   torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   pipe(
            prompt=UpperCAmelCase__				,				image=UpperCAmelCase__				,				strength=0.75				,				guidance_scale=7.5				,				generator=UpperCAmelCase__				,				output_type="""np"""				,				)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   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




