

"""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        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       KandinskyImgaImgPipeline
     _lowerCamelCase     =       ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
     _lowerCamelCase     =       [
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     _lowerCamelCase     =       [
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     _lowerCamelCase     =       False







     @property
     def 	lowerCAmelCase__    (      self       ):
      return 32







     @property
     def 	lowerCAmelCase__    (      self       ):
      return 32







     @property
     def 	lowerCAmelCase__    (      self       ):
      return self.time_input_dim







     @property
     def 	lowerCAmelCase__    (      self       ):
      return self.time_input_dim * 4







     @property
     def 	lowerCAmelCase__    (      self       ):
      return 100







     @property
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''       )
      return tokenizer







     @property
     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      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 ,						)

      __magic_name__     =      MultilingualCLIP(UpperCamelCase_       )
      __magic_name__     =      text_encoder.eval()

      return text_encoder







     @property
     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )

      __magic_name__     =      {
          '''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,
      }

      __magic_name__     =      UNetaDConditionModel(**UpperCamelCase_       )
      return model







     @property
     def 	lowerCAmelCase__    (      self       ):
      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 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      VQModel(**self.dummy_movq_kwargs       )
      return model







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.dummy_text_encoder
      __magic_name__     =      self.dummy_tokenizer
      __magic_name__     =      self.dummy_unet
      __magic_name__     =      self.dummy_movq

      __magic_name__     =      {
          '''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,
      }

      __magic_name__     =      DDIMScheduler(**UpperCamelCase_       )

      __magic_name__     =      {
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }

      return components







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=0       ):
      __magic_name__     =      floats_tensor((1, self.cross_attention_dim) ,						rng=random.Random(UpperCamelCase_       )       ).to(UpperCamelCase_       )
      __magic_name__     =      floats_tensor((1, self.cross_attention_dim) ,						rng=random.Random(seed + 1       )       ).to(UpperCamelCase_       )
      # create init_image
      __magic_name__     =      floats_tensor((1, 3, 64, 64) ,						rng=random.Random(UpperCamelCase_       )       ).to(UpperCamelCase_       )
      __magic_name__     =      image.cpu().permute(0 ,						2 ,						3 ,						1       )[0]
      __magic_name__     =      Image.fromarray(np.uinta(UpperCamelCase_       )       ).convert('''RGB'''       ).resize((256, 256)       )

      if str(UpperCamelCase_       ).startswith('''mps'''       ):
       __magic_name__     =      torch.manual_seed(UpperCamelCase_       )
      else:
       __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      {
          '''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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''cpu'''

      __magic_name__     =      self.get_dummy_components()

      __magic_name__     =      self.pipeline_class(**UpperCamelCase_       )
      __magic_name__     =      pipe.to(UpperCamelCase_       )

      pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      pipe(**self.get_dummy_inputs(UpperCamelCase_       )       )
      __magic_name__     =      output.images

      __magic_name__     =      pipe(
          **self.get_dummy_inputs(UpperCamelCase_       ) ,						return_dict=UpperCamelCase_ ,						)[0]

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

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

      __magic_name__     =      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        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinsky/kandinsky_img2img_frog.npy'''       )

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

      __magic_name__     =      KandinskyPriorPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-1-prior''' ,						torch_dtype=torch.floataa       )
      pipe_prior.to(UpperCamelCase_       )

      __magic_name__     =      KandinskyImgaImgPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-1''' ,						torch_dtype=torch.floataa       )
      __magic_name__     =      pipeline.to(UpperCamelCase_       )

      pipeline.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      torch.Generator(device='''cpu'''       ).manual_seed(0       )
      __magic_name__   ,						__magic_name__     =      pipe_prior(
          UpperCamelCase_ ,						generator=UpperCamelCase_ ,						num_inference_steps=5 ,						negative_prompt='''''' ,						).to_tuple()

      __magic_name__     =      pipeline(
          UpperCamelCase_ ,						image=UpperCamelCase_ ,						image_embeds=UpperCamelCase_ ,						negative_image_embeds=UpperCamelCase_ ,						generator=UpperCamelCase_ ,						num_inference_steps=100 ,						height=768 ,						width=768 ,						strength=0.2 ,						output_type='''np''' ,						)

      __magic_name__     =      output.images[0]

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

      assert_mean_pixel_difference(UpperCamelCase_ ,						UpperCamelCase_       )




"""simple docstring"""






from __future__ import annotations

from random import choice





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 return choice(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      random_pivot(__UpperCamelCase						)

 # partition based on pivot
 # linear time
 __magic_name__     =      [e for e in lst if e < pivot]
 __magic_name__     =      [e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(__UpperCamelCase						) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(__UpperCamelCase						) < k - 1:
  return kth_number(__UpperCamelCase      ,   k - len(__UpperCamelCase						) - 1						)
 # pivot is in elements smaller than k
 else:
  return kth_number(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

