


def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     if not isinstance(UpperCamelCase__							,UpperCamelCase__    ):
          raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''    )
     if len(UpperCamelCase__    ) == 0:
          raise ValueError('''Input list must be a non empty list'''    )
     if len(UpperCamelCase__    ) == 1:
          return True
     snake_case					=    series[1] - series[0]
     for index in range(len(UpperCamelCase__    ) - 1    ):
          if series[index + 1] - series[index] != common_diff:
               return False
     return True
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     if not isinstance(UpperCamelCase__							,UpperCamelCase__    ):
          raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''    )
     if len(UpperCamelCase__    ) == 0:
          raise ValueError('''Input list must be a non empty list'''    )
     snake_case					=    0
     for val in series:
          answer += val
     return answer / len(UpperCamelCase__    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()








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__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		KandinskyImgaImgPipeline
    __magic_name__        =		['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    __magic_name__        =		[
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    __magic_name__        =		[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    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      , )

         snake_case					=    MultilingualCLIP(__snake_case   )
         snake_case					=    text_encoder.eval()

         return text_encoder
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         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,
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         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 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    VQModel(**self.dummy_movq_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_unet
         snake_case					=    self.dummy_movq

         snake_case					=    {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_0085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         snake_case					=    DDIMScheduler(**__snake_case   )

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

         return components
    def 		a_      (	self      , __snake_case      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, self.cross_attention_dim)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    floats_tensor((1, self.cross_attention_dim)      , rng=random.Random(seed + 1   )   ).to(__snake_case   )
         # create init_image
         snake_case					=    floats_tensor((1, 3, 6_4, 6_4)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   )[0]
         snake_case					=    Image.fromarray(np.uinta(__snake_case   )   ).convert('''RGB'''   ).resize((2_5_6, 2_5_6)   )

         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
         snake_case					=    {
             '''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 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    pipe(**self.get_dummy_inputs(__snake_case   )   )
         snake_case					=    output.images

         snake_case					=    pipe(
             **self.get_dummy_inputs(__snake_case   )      , return_dict=__snake_case      , )[0]

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

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

         snake_case					=    np.array(
             [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]   )
         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     ):







    """simple docstring"""

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




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

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

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

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

         pipeline.set_progress_bar_config(disable=__snake_case   )

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

         snake_case					=    pipeline(
             __snake_case      , image=__snake_case      , image_embeds=__snake_case      , negative_image_embeds=__snake_case      , generator=__snake_case      , num_inference_steps=1_0_0      , height=7_6_8      , width=7_6_8      , strength=0.2      , output_type='''np'''      , )

         snake_case					=    output.images[0]

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

         assert_mean_pixel_difference(__snake_case      , __snake_case   )




