






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'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				tuple[float, list[float]]:
      UpperCamelCase       						=      list(range(len(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [v / w for v, w in zip(__UpperCamelCase	,		__UpperCamelCase					)]
      index.sort(key=lambda __UpperCamelCase					: ratio[i]	,		reverse=__UpperCamelCase					)

      UpperCamelCase       						=      0
      UpperCamelCase       						=      [0] * len(__UpperCamelCase					)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase       						=      1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase       						=      capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()
