



"""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    SCREAMING_SNAKE_CASE__ (							UpperCamelCase__      ,  unittest.TestCase   ):
    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 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return 3_2

    @property
    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return 3_2

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return self.time_input_dim

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







      """simple docstring"""







      return self.time_input_dim * 4

    @property
    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      return 1_0_0

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
      return tokenizer

    @property
    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      torch.manual_seed(0)
      lowercase_								= MCLIPConfig(
          numDims=self.cross_attention_dim ,  transformerDimensions=self.text_embedder_hidden_size ,  hidden_size=self.text_embedder_hidden_size ,  intermediate_size=3_7 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  vocab_size=1_0_0_5 ,  )

      lowercase_								= MultilingualCLIP(lowerCAmelCase_)
      lowercase_								= text_encoder.eval()

      return text_encoder

    @property
    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      torch.manual_seed(0)

      lowercase_								= {
          """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,
      }

      lowercase_								= UNetaDConditionModel(**lowerCAmelCase_)
      return model

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }

    @property
    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      torch.manual_seed(0)
      lowercase_								= VQModel(**self.dummy_movq_kwargs)
      return model

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= self.dummy_text_encoder
      lowercase_								= self.dummy_tokenizer
      lowercase_								= self.dummy_unet
      lowercase_								= self.dummy_movq

      lowercase_								= {
          """num_train_timesteps""": 1_0_0_0,
          """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,
      }

      lowercase_								= DDIMScheduler(**lowerCAmelCase_)

      lowercase_								= {
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				List[Any]=0):







      """simple docstring"""







      lowercase_								= floats_tensor((1, self.cross_attention_dim) ,  rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
      lowercase_								= floats_tensor((1, self.cross_attention_dim) ,  rng=random.Random(seed + 1)).to(lowerCAmelCase_)
      # create init_image
      lowercase_								= floats_tensor((1, 3, 6_4, 6_4) ,  rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
      lowercase_								= image.cpu().permute(0 ,  2 ,  3 ,  1)[0]
      lowercase_								= Image.fromarray(np.uinta(lowerCAmelCase_)).convert("""RGB""").resize((2_5_6, 2_5_6))

      if str(lowerCAmelCase_).startswith("""mps"""):
        lowercase_								= torch.manual_seed(lowerCAmelCase_)
      else:
        lowercase_								= torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)
      lowercase_								= {
          """prompt""": """horse""",
          """image""": init_image,
          """image_embeds""": image_embeds,
          """negative_image_embeds""": negative_image_embeds,
          """generator""": generator,
          """height""": 6_4,
          """width""": 6_4,
          """num_inference_steps""": 1_0,
          """guidance_scale""": 7.0,
          """strength""": 0.2,
          """output_type""": """np""",
      }
      return inputs




    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= """cpu"""

      lowercase_								= self.get_dummy_components()

      lowercase_								= self.pipeline_class(**lowerCAmelCase_)
      lowercase_								= pipe.to(lowerCAmelCase_)

      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      lowercase_								= pipe(**self.get_dummy_inputs(lowerCAmelCase_))
      lowercase_								= output.images

      lowercase_								= pipe(
          **self.get_dummy_inputs(lowerCAmelCase_) ,  return_dict=lowerCAmelCase_ ,  )[0]

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

      assert image.shape == (1, 6_4, 6_4, 3)

      lowercase_								= 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    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







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




    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinsky/kandinsky_img2img_frog.npy""")

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

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

      lowercase_								= KandinskyImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1""" ,  torch_dtype=torch.floataa)
      lowercase_								= pipeline.to(lowerCAmelCase_)

      pipeline.set_progress_bar_config(disable=lowerCAmelCase_)

      lowercase_								= torch.Generator(device="""cpu""").manual_seed(0)
      lowercase_					,    lowercase_								= pipe_prior(
          lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  num_inference_steps=5 ,  negative_prompt="""""" ,  ).to_tuple()

      lowercase_								= pipeline(
          lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  image_embeds=lowerCAmelCase_ ,  negative_image_embeds=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  height=7_6_8 ,  width=7_6_8 ,  strength=0.2 ,  output_type="""np""" ,  )

      lowercase_								= output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(lowerCAmelCase_ ,  lowerCAmelCase_)






"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


  while a != 0:
    lowercase_					,    lowercase_								= b % a, a
  return b


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


  if gcd(__lowerCAmelCase ,  __lowerCAmelCase							) != 1:
    lowercase_								= F'''mod inverse of {a!r} and {m!r} does not exist'''
    raise ValueError(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_								= 1, 0, a
  lowercase_					,    lowercase_					,    lowercase_								= 0, 1, m
  while va != 0:
    lowercase_								= ua // va
    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m

