






'''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        a_		(    lowerCamelCase			, 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 						A__		(   self   )						->   str:



        """simple docstring"""






        return 32

  @property
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return 32

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        return self.time_input_dim

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        return self.time_input_dim * 4

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        return 100

  @property
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






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

  @property
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        torch.manual_seed(0   )
        UpperCamelCase       						=      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=1005    ,	)

        UpperCamelCase       						=      MultilingualCLIP(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      text_encoder.eval()

        return text_encoder

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        torch.manual_seed(0   )

        UpperCamelCase       						=      {
            """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,
        }

        UpperCamelCase       						=      UNetaDConditionModel(**_SCREAMING_SNAKE_CASE   )
        return model

  @property
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        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 						A__		(   self   )						->   Dict:



        """simple docstring"""






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

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.dummy_text_encoder
        UpperCamelCase       						=      self.dummy_tokenizer
        UpperCamelCase       						=      self.dummy_unet
        UpperCamelCase       						=      self.dummy_movq

        UpperCamelCase       						=      {
            """num_train_timesteps""": 1000,
            """beta_schedule""": """linear""",
            """beta_start""": 0.0_0_0_8_5,
            """beta_end""": 0.0_1_2,
            """clip_sample""": False,
            """set_alpha_to_one""": False,
            """steps_offset""": 0,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
        }

        UpperCamelCase       						=      DDIMScheduler(**_SCREAMING_SNAKE_CASE   )

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

        return components

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor((1, self.cross_attention_dim)    ,	rng=random.Random(_SCREAMING_SNAKE_CASE   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      floats_tensor((1, self.cross_attention_dim)    ,	rng=random.Random(seed + 1   )   ).to(_SCREAMING_SNAKE_CASE   )
        # create init_image
        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(_SCREAMING_SNAKE_CASE   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      image.cpu().permute(0    ,	2    ,	3    ,	1   )[0]
        UpperCamelCase       						=      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE   )   ).convert("""RGB"""   ).resize((256, 256)   )

        if str(_SCREAMING_SNAKE_CASE   ).startswith("""mps"""   ):
              UpperCamelCase       						=      torch.manual_seed(_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      torch.Generator(device=_SCREAMING_SNAKE_CASE   ).manual_seed(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      {
            """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 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""

        UpperCamelCase       						=      self.get_dummy_components()

        UpperCamelCase       						=      self.pipeline_class(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      pipe.to(_SCREAMING_SNAKE_CASE   )

        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      output.images

        UpperCamelCase       						=      pipe(
            **self.get_dummy_inputs(_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, 64, 64, 3)

        UpperCamelCase       						=      np.array(
            [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]   )
        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        a_		(    unittest.TestCase       ):

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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


  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

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

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

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

        pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      pipeline(
            _SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	image_embeds=_SCREAMING_SNAKE_CASE    ,	negative_image_embeds=_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=100    ,	height=768    ,	width=768    ,	strength=0.2    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            raise ValueError("""iterations must be defined as integers"""					)
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					) or not number >= 1:
            raise ValueError(
                """starting number must be
                         and integer and be more than 0"""					)
      if not iterations >= 1:
            raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""					)

      UpperCamelCase       						=      """"""
      while number <= iterations:
            if number % 3 == 0:
                  out += "Fizz"
            if number % 5 == 0:
                  out += "Buzz"
            if 0 not in (number % 3, number % 5):
                  out += str(__UpperCamelCase					)

            # print(out)
            number += 1
            out += " "
      return out


if __name__ == "__main__":
     import doctest

     doctest.testmod()
