




'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= KandinskyImgaImgPipeline
    lowercase       			= ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    lowercase       			= [
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    lowercase       			= [
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowercase       			= False

    @property
    def __lowercase       (       self   :  List[Any]		)       ->  str:
      return 32

    @property
    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      return 32

    @property
    def __lowercase       (       self   :  int		)       ->  Optional[int]:
      return self.time_input_dim

    @property
    def __lowercase       (       self   :  Optional[int]		)       ->  Any:
      return self.time_input_dim * 4

    @property
    def __lowercase       (       self   :  List[str]		)       ->  Dict:
      return 1_00

    @property
    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Any      =       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
      return tokenizer

    @property
    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Dict      =       MCLIPConfig(
          numDims=self.cross_attention_dim   ,						transformerDimensions=self.text_embedder_hidden_size   ,						hidden_size=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=10_05   ,						)

      lowerCAmelCase_      :				str      =       MultilingualCLIP(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       text_encoder.eval()

      return text_encoder

    @property
    def __lowercase       (       self   :  List[Any]		)       ->  int:
      torch.manual_seed(0		)

      lowerCAmelCase_      :				Optional[Any]      =       {
          """in_channels""": 4,
          # Out channels is double in channels because predicts mean and variance
          """out_channels""": 8,
          """addition_embed_type""": """text_image""",
          """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
          """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
          """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
          """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
          """layers_per_block""": 1,
          """encoder_hid_dim""": self.text_embedder_hidden_size,
          """encoder_hid_dim_type""": """text_image_proj""",
          """cross_attention_dim""": self.cross_attention_dim,
          """attention_head_dim""": 4,
          """resnet_time_scale_shift""": """scale_shift""",
          """class_embed_type""": None,
      }

      lowerCAmelCase_      :				List[Any]      =       UNetaDConditionModel(**lowerCamelCase		)
      return model

    @property
    def __lowercase       (       self   :  int		)       ->  Union[str, Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }

    @property
    def __lowercase       (       self   :  Tuple		)       ->  Optional[int]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Any      =       VQModel(**self.dummy_movq_kwargs		)
      return model

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				List[Any]      =       self.dummy_text_encoder
      lowerCAmelCase_      :				List[str]      =       self.dummy_tokenizer
      lowerCAmelCase_      :				Union[str, Any]      =       self.dummy_unet
      lowerCAmelCase_      :				Union[str, Any]      =       self.dummy_movq

      lowerCAmelCase_      :				Any      =       {
          """num_train_timesteps""": 10_00,
          """beta_schedule""": """linear""",
          """beta_start""": 0.00_085,
          """beta_end""": 0.012,
          """clip_sample""": False,
          """set_alpha_to_one""": False,
          """steps_offset""": 0,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
      }

      lowerCAmelCase_      :				List[Any]      =       DDIMScheduler(**lowerCamelCase		)

      lowerCAmelCase_      :				List[str]      =       {
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  int=0		)       ->  Any:
      lowerCAmelCase_      :				List[str]      =       floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(seed + 1		)		).to(lowerCamelCase		)
      # create init_image
      lowerCAmelCase_      :				int      =       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       image.cpu().permute(0   ,						2   ,						3   ,						1		)[0]
      lowerCAmelCase_      :				Optional[int]      =       Image.fromarray(np.uinta(lowerCamelCase		)		).convert("""RGB"""		).resize((2_56, 2_56)		)

      if str(lowerCamelCase		).startswith("""mps"""		):
        lowerCAmelCase_      :				Any      =       torch.manual_seed(lowerCamelCase		)
      else:
        lowerCAmelCase_      :				Optional[int]      =       torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       {
          """prompt""": """horse""",
          """image""": init_image,
          """image_embeds""": image_embeds,
          """negative_image_embeds""": negative_image_embeds,
          """generator""": generator,
          """height""": 64,
          """width""": 64,
          """num_inference_steps""": 10,
          """guidance_scale""": 7.0,
          """strength""": 0.2,
          """output_type""": """np""",
      }
      return inputs





    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Union[str, Any]      =       """cpu"""

      lowerCAmelCase_      :				Optional[int]      =       self.get_dummy_components()

      lowerCAmelCase_      :				Optional[Any]      =       self.pipeline_class(**lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       pipe.to(lowerCamelCase		)

      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				int      =       pipe(**self.get_dummy_inputs(lowerCamelCase		)		)
      lowerCAmelCase_      :				Tuple      =       output.images

      lowerCAmelCase_      :				int      =       pipe(
          **self.get_dummy_inputs(lowerCamelCase		)   ,						return_dict=lowerCamelCase   ,						)[0]

      lowerCAmelCase_      :				Union[str, Any]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCAmelCase_      :				Tuple      =       np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]		)
      assert (
          np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







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







    """simple docstring"""



    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





    def __lowercase       (       self   :  Optional[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				List[str]      =       load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinsky/kandinsky_img2img_frog.npy"""		)

      lowerCAmelCase_      :				Tuple      =       load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
      lowerCAmelCase_      :				int      =       """A red cartoon frog, 4k"""

      lowerCAmelCase_      :				Dict      =       KandinskyPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1-prior"""   ,						torch_dtype=torch.floataa		)
      pipe_prior.to(lowerCamelCase		)

      lowerCAmelCase_      :				Optional[Any]      =       KandinskyImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1"""   ,						torch_dtype=torch.floataa		)
      lowerCAmelCase_      :				Any      =       pipeline.to(lowerCamelCase		)

      pipeline.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				int      =       torch.Generator(device="""cpu"""		).manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       pipe_prior(
          lowerCamelCase   ,						generator=lowerCamelCase   ,						num_inference_steps=5   ,						negative_prompt=""""""   ,						).to_tuple()

      lowerCAmelCase_      :				Tuple      =       pipeline(
          lowerCamelCase   ,						image=lowerCamelCase   ,						image_embeds=lowerCamelCase   ,						negative_image_embeds=lowerCamelCase   ,						generator=lowerCamelCase   ,						num_inference_steps=1_00   ,						height=7_68   ,						width=7_68   ,						strength=0.2   ,						output_type="""np"""   ,						)

      lowerCAmelCase_      :				List[str]      =       output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(lowerCamelCase   ,						lowerCamelCase		)







'''simple docstring'''






from ..utils import DummyObject, requires_backends






class        __snake_case	(       metaclass=_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['note_seq']

    def __init__(       self   :  Tuple   ,						*lowerCamelCase   :  Optional[int]   ,						**lowerCamelCase   :  Tuple		)       ->  Any:
      requires_backends(self   ,						["""note_seq"""]		)

    @classmethod
    def __lowercase       (       cls   :  Optional[Any]   ,						*lowerCamelCase   :  List[Any]   ,						**lowerCamelCase   :  Dict		)       ->  List[Any]:
      requires_backends(cls   ,						["""note_seq"""]		)





    @classmethod
    def __lowercase       (       cls   :  Union[str, Any]   ,						*lowerCamelCase   :  Union[str, Any]   ,						**lowerCamelCase   :  List[Any]		)       ->  Optional[int]:
      requires_backends(cls   ,						["""note_seq"""]		)



