






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(					__UpperCamelCase     ,       unittest.TestCase      ):
 lowercase :       Any		      =		KandinskyPipeline
 lowercase :       Any		      =		[
     '''prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
 ]
 lowercase :       str		      =		['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
 lowercase :       List[str]		      =		[
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 lowercase :       str		      =		False



 @property
 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    return 32



 @property
 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






    return 32



 @property
 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    return self.time_input_dim



 @property
 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    return self.time_input_dim * 4



 @property
 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    return 100



 @property
 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






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



 @property
 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    torch.manual_seed(0	)
    lowercase__						:	Optional[int]         = 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=1_005 ,		)

    lowercase__						:	Union[str, Any]         = MultilingualCLIP(UpperCAmelCase__	)
    lowercase__						:	List[str]         = text_encoder.eval()

    return text_encoder



 @property
 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    torch.manual_seed(0	)

    lowercase__						:	List[Any]         = {
        """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,
    }

    lowercase__						:	Union[str, Any]         = UNetaDConditionModel(**UpperCAmelCase__	)
    return model



 @property
 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """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 						UpperCAmelCase    (	self   :   Optional[int]	):

    """simple docstring"""






    torch.manual_seed(0	)
    lowercase__						:	str         = VQModel(**self.dummy_movq_kwargs	)
    return model



 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    lowercase__						:	List[str]         = self.dummy_text_encoder
    lowercase__						:	Dict         = self.dummy_tokenizer
    lowercase__						:	Union[str, Any]         = self.dummy_unet
    lowercase__						:	List[str]         = self.dummy_movq

    lowercase__						:	List[Any]         = DDIMScheduler(
        num_train_timesteps=1_000 ,		beta_schedule="""linear""" ,		beta_start=0.00_085 ,		beta_end=0.012 ,		clip_sample=UpperCAmelCase__ ,		set_alpha_to_one=UpperCAmelCase__ ,		steps_offset=1 ,		prediction_type="""epsilon""" ,		thresholding=UpperCAmelCase__ ,		)

    lowercase__						:	str         = {
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }
    return components



 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   Union[str, Any] ,		UpperCAmelCase__   :   Tuple=0	):

    """simple docstring"""






    lowercase__						:	Any         = floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(UpperCAmelCase__	)	).to(UpperCAmelCase__	)
    lowercase__						:	Any         = floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(seed + 1	)	).to(UpperCAmelCase__	)
    if str(UpperCAmelCase__	).startswith("""mps"""	):
       lowercase__						:	Dict         = torch.manual_seed(UpperCAmelCase__	)
    else:
       lowercase__						:	str         = torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
    lowercase__						:	Union[str, Any]         = {
        """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 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






    lowercase__						:	Any         = """cpu"""

    lowercase__						:	List[str]         = self.get_dummy_components()

    lowercase__						:	Tuple         = self.pipeline_class(**UpperCAmelCase__	)
    lowercase__						:	Tuple         = pipe.to(UpperCAmelCase__	)

    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    lowercase__						:	int         = pipe(**self.get_dummy_inputs(UpperCAmelCase__	)	)
    lowercase__						:	Tuple         = output.images

    lowercase__						:	Optional[Any]         = pipe(
        **self.get_dummy_inputs(UpperCAmelCase__	) ,		return_dict=UpperCAmelCase__ ,		)[0]

    lowercase__						:	List[str]         = image[0, -3:, -3:, -1]
    lowercase__						:	str         = image_from_tuple[0, -3:, -3:, -1]

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

    lowercase__						:	str         = np.array(
        [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]	)

    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 						UpperCAmelCase    (	self   :   str	):

    """simple docstring"""






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







 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    lowercase__						:	List[Any]         = load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinsky/kandinsky_text2img_cat_fp16.npy"""	)

    lowercase__						:	Dict         = KandinskyPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-1-prior""" ,		torch_dtype=torch.floataa	)
    pipe_prior.to(UpperCAmelCase__	)

    lowercase__						:	List[str]         = KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1""" ,		torch_dtype=torch.floataa	)
    lowercase__						:	List[str]         = pipeline.to(UpperCAmelCase__	)
    pipeline.set_progress_bar_config(disable=UpperCAmelCase__	)

    lowercase__						:	Tuple         = """red cat, 4k photo"""

    lowercase__						:	Any         = torch.Generator(device="""cuda"""	).manual_seed(0	)
    lowercase__     ,					lowercase__						:	Any         = pipe_prior(
        UpperCAmelCase__ ,		generator=UpperCAmelCase__ ,		num_inference_steps=5 ,		negative_prompt="""""" ,		).to_tuple()

    lowercase__						:	List[str]         = torch.Generator(device="""cuda"""	).manual_seed(0	)
    lowercase__						:	Optional[int]         = pipeline(
        UpperCAmelCase__ ,		image_embeds=UpperCAmelCase__ ,		negative_image_embeds=UpperCAmelCase__ ,		generator=UpperCAmelCase__ ,		num_inference_steps=100 ,		output_type="""np""" ,		)

    lowercase__						:	Optional[int]         = output.images[0]

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

    assert_mean_pixel_difference(UpperCAmelCase__ ,		UpperCAmelCase__	)










a    =       10**8
def       a__						(  A					):
   if n % 10 == 0:
      return False
   lowercase__						:	Optional[Any]         = str(A					)
   return s == s[::-1]
def       a__						(  ):
   lowercase__						:	Union[str, Any]         = set()
   lowercase__						:	Dict         = 1
   lowercase__						:	List[str]         = 5
   while sum_squares < LIMIT:
      lowercase__						:	Tuple         = first_square + 1
      while sum_squares < LIMIT:
         if is_palindrome(A					):
            answer.add(A					)
         last_square += 1
         sum_squares += last_square**2
      first_square += 1
      lowercase__						:	Any         = first_square**2 + (first_square + 1) ** 2

   return sum(A					)


if __name__ == "__main__":
 print(solution())


