

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						List[str]    			=	KandinskyPipeline
  __lowerCAmelCase  :						Any    			=	[
      'prompt',
      'image_embeds',
      'negative_image_embeds',
  ]
  __lowerCAmelCase  :						Union[str, Any]    			=	['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
  __lowerCAmelCase  :						Optional[Any]    			=	[
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  __lowerCAmelCase  :						str    			=	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):
      SCREAMING_SNAKE_CASE_						=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
      return tokenizer

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

      SCREAMING_SNAKE_CASE_						=    MultilingualCLIP(_A)
      SCREAMING_SNAKE_CASE_						=    text_encoder.eval()

      return text_encoder

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

      SCREAMING_SNAKE_CASE_						=    {
          '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,
      }

      SCREAMING_SNAKE_CASE_						=    UNetaDConditionModel(**_A)
      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)
      SCREAMING_SNAKE_CASE_						=    VQModel(**self.dummy_movq_kwargs)
      return model

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

      SCREAMING_SNAKE_CASE_						=    DDIMScheduler(
          num_train_timesteps=1000    ,		beta_schedule='linear'    ,		beta_start=0.0_0_0_8_5    ,		beta_end=0.0_1_2    ,		clip_sample=_A    ,		set_alpha_to_one=_A    ,		steps_offset=1    ,		prediction_type='epsilon'    ,		thresholding=_A    ,		)

      SCREAMING_SNAKE_CASE_						=    {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }
      return components

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A=0):
      SCREAMING_SNAKE_CASE_						=    floats_tensor((1, self.cross_attention_dim)    ,		rng=random.Random(_A)).to(_A)
      SCREAMING_SNAKE_CASE_						=    floats_tensor((1, self.cross_attention_dim)    ,		rng=random.Random(seed + 1)).to(_A)
      if str(_A).startswith('mps'):
          SCREAMING_SNAKE_CASE_						=    torch.manual_seed(_A)
      else:
          SCREAMING_SNAKE_CASE_						=    torch.Generator(device=_A).manual_seed(_A)
      SCREAMING_SNAKE_CASE_						=    {
          'prompt': 'horse',
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'guidance_scale': 4.0,
          'num_inference_steps': 2,
          'output_type': 'np',
      }
      return inputs


  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    'cpu'

      SCREAMING_SNAKE_CASE_						=    self.get_dummy_components()

      SCREAMING_SNAKE_CASE_						=    self.pipeline_class(**_A)
      SCREAMING_SNAKE_CASE_						=    pipe.to(_A)

      pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_						=    pipe(**self.get_dummy_inputs(_A))
      SCREAMING_SNAKE_CASE_						=    output.images

      SCREAMING_SNAKE_CASE_						=    pipe(
          **self.get_dummy_inputs(_A)    ,		return_dict=_A    ,		)[0]

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

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

      SCREAMING_SNAKE_CASE_						=    np.array(
          [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_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    __snake_case	(     unittest.TestCase							):

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


  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_text2img_cat_fp16.npy')

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

      SCREAMING_SNAKE_CASE_						=    KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,		torch_dtype=torch.floataa)
      SCREAMING_SNAKE_CASE_						=    pipeline.to(_A)
      pipeline.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_						=    'red cat, 4k photo'

      SCREAMING_SNAKE_CASE_						=    torch.Generator(device='cuda').manual_seed(0)
      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    pipe_prior(
          _A    ,		generator=_A    ,		num_inference_steps=5    ,		negative_prompt=''    ,		).to_tuple()

      SCREAMING_SNAKE_CASE_						=    torch.Generator(device='cuda').manual_seed(0)
      SCREAMING_SNAKE_CASE_						=    pipeline(
          _A    ,		image_embeds=_A    ,		negative_image_embeds=_A    ,		generator=_A    ,		num_inference_steps=100    ,		output_type='np'    ,		)

      SCREAMING_SNAKE_CASE_						=    output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(_A    ,		_A)





def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    if len(_SCREAMING_SNAKE_CASE							) == 1:
        return True
    try:
        SCREAMING_SNAKE_CASE_						=    series[1] / series[0]
        for index in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if series[index + 1] / series[index] != common_ratio:
                return False
    except ZeroDivisionError:
        return False
    return True


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    SCREAMING_SNAKE_CASE_						=    1
    for value in series:
        answer *= value
    return pow(_SCREAMING_SNAKE_CASE			, 1 / len(_SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


