

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


snake_case_        =			False


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





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





    @property
    def 				snake_case_    (				self):
         return 12





    @property
    def 				snake_case_    (				self):
         return 12





    @property
    def 				snake_case_    (				self):
         return 32





    @property
    def 				snake_case_    (				self):
         torch.manual_seed(0)
         lowercase__   :       str				=	VQModel(
             block_out_channels=[32, 64]						,       in_channels=3						,       out_channels=3						,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,       latent_channels=3						,       num_vq_embeddings=self.num_embed						,       vq_embed_dim=3						,       )
         return model





    @property
    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
         return tokenizer





    @property
    def 				snake_case_    (				self):
         torch.manual_seed(0)
         lowercase__   :       int				=	CLIPTextConfig(
             bos_token_id=0						,       eos_token_id=2						,       hidden_size=self.text_embedder_hidden_size						,       intermediate_size=37						,       layer_norm_eps=1e-05						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1000						,       )
         return CLIPTextModel(_SCREAMING_SNAKE_CASE)





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

         lowercase__   :       Optional[Any]				=	12
         lowercase__   :       List[Any]				=	12

         lowercase__   :       int				=	{
             "attention_bias": True,
             "cross_attention_dim": 32,
             "attention_head_dim": height * width,
             "num_attention_heads": 1,
             "num_vector_embeds": self.num_embed,
             "num_embeds_ada_norm": self.num_embeds_ada_norm,
             "norm_num_groups": 32,
             "sample_size": width,
             "activation_fn": "geglu-approximate",
         }

         lowercase__   :       List[Any]				=	TransformeraDModel(**_SCREAMING_SNAKE_CASE)
         return model





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	"cpu"

         lowercase__   :       Optional[int]				=	self.dummy_vqvae
         lowercase__   :       str				=	self.dummy_text_encoder
         lowercase__   :       List[Any]				=	self.dummy_tokenizer
         lowercase__   :       Any				=	self.dummy_transformer
         lowercase__   :       Union[str, Any]				=	VQDiffusionScheduler(self.num_embed)
         lowercase__   :       Any				=	LearnedClassifierFreeSamplingEmbeddings(learnable=_SCREAMING_SNAKE_CASE)

         lowercase__   :       Optional[int]				=	VQDiffusionPipeline(
             vqvae=_SCREAMING_SNAKE_CASE						,       text_encoder=_SCREAMING_SNAKE_CASE						,       tokenizer=_SCREAMING_SNAKE_CASE						,       transformer=_SCREAMING_SNAKE_CASE						,       scheduler=_SCREAMING_SNAKE_CASE						,       learned_classifier_free_sampling_embeddings=_SCREAMING_SNAKE_CASE						,       )
         lowercase__   :       Optional[int]				=	pipe.to(_SCREAMING_SNAKE_CASE)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

         lowercase__   :       str				=	"teddy bear playing in the pool"

         lowercase__   :       List[Any]				=	torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
         lowercase__   :       List[Any]				=	pipe([prompt]						,       generator=_SCREAMING_SNAKE_CASE						,       num_inference_steps=2						,       output_type='np')
         lowercase__   :       List[Any]				=	output.images

         lowercase__   :       Optional[int]				=	torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
         lowercase__   :       Tuple				=	pipe(
             [prompt]						,       generator=_SCREAMING_SNAKE_CASE						,       output_type='np'						,       return_dict=_SCREAMING_SNAKE_CASE						,       num_inference_steps=2)[0]

         lowercase__   :       Any				=	image[0, -3:, -3:, -1]
         lowercase__   :       int				=	image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         lowercase__   :       Tuple				=	np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2





    def 				snake_case_    (				self):
         lowercase__   :       int				=	"cpu"

         lowercase__   :       int				=	self.dummy_vqvae
         lowercase__   :       Union[str, Any]				=	self.dummy_text_encoder
         lowercase__   :       List[str]				=	self.dummy_tokenizer
         lowercase__   :       Optional[Any]				=	self.dummy_transformer
         lowercase__   :       List[str]				=	VQDiffusionScheduler(self.num_embed)
         lowercase__   :       Optional[int]				=	LearnedClassifierFreeSamplingEmbeddings(
             learnable=_SCREAMING_SNAKE_CASE						,       hidden_size=self.text_embedder_hidden_size						,       length=tokenizer.model_max_length)

         lowercase__   :       Union[str, Any]				=	VQDiffusionPipeline(
             vqvae=_SCREAMING_SNAKE_CASE						,       text_encoder=_SCREAMING_SNAKE_CASE						,       tokenizer=_SCREAMING_SNAKE_CASE						,       transformer=_SCREAMING_SNAKE_CASE						,       scheduler=_SCREAMING_SNAKE_CASE						,       learned_classifier_free_sampling_embeddings=_SCREAMING_SNAKE_CASE						,       )
         lowercase__   :       str				=	pipe.to(_SCREAMING_SNAKE_CASE)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

         lowercase__   :       Optional[Any]				=	"teddy bear playing in the pool"

         lowercase__   :       Optional[int]				=	torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
         lowercase__   :       Optional[int]				=	pipe([prompt]						,       generator=_SCREAMING_SNAKE_CASE						,       num_inference_steps=2						,       output_type='np')
         lowercase__   :       str				=	output.images

         lowercase__   :       Optional[Any]				=	torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
         lowercase__   :       Any				=	pipe(
             [prompt]						,       generator=_SCREAMING_SNAKE_CASE						,       output_type='np'						,       return_dict=_SCREAMING_SNAKE_CASE						,       num_inference_steps=2)[0]

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

         assert image.shape == (1, 24, 24, 3)

         lowercase__   :       Any				=	np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

         assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



@slow
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





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





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

         lowercase__   :       Optional[Any]				=	VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
         lowercase__   :       Optional[Any]				=	pipeline.to(_SCREAMING_SNAKE_CASE)
         pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         lowercase__   :       Optional[int]				=	torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
         lowercase__   :       Optional[Any]				=	pipeline(
             'teddy bear playing in the pool'						,       num_images_per_prompt=1						,       generator=_SCREAMING_SNAKE_CASE						,       output_type='np'						,       )

         lowercase__   :       str				=	output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image).max() < 2.0




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return 1 if digit in (0, 1) else (digit * factorial(digit - 1      ))

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	0
     lowercase__   :       Optional[int]				=	number
     while duplicate > 0:
          lowercase__				,			lowercase__   :       Tuple				=	divmod(SCREAMING_SNAKE_CASE_     ,					10      )
          fact_sum += factorial(SCREAMING_SNAKE_CASE_      )
     return fact_sum == number


if __name__ == "__main__":
       print('''Program to check whether a number is a Krisnamurthy Number or not.''')
       snake_case_        =			int(input('''Enter number: ''').strip())
       print(
           F'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
       )

