


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


A_		:     Optional[Any]        =     False



class 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Union[str, Any]							)       ->				Any:


    """simple docstring"""






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

   @property
   def 		A							(      self			:       Any							)       ->				Any:


    """simple docstring"""






    return 12

   @property
   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    return 12

   @property
   def 		A							(      self			:       int							)       ->				List[Any]:


    """simple docstring"""






    return 32

   @property
   def 		A							(      self			:       int							)       ->				Optional[Any]:


    """simple docstring"""






    torch.manual_seed(0							)
    a						=		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 		A							(      self			:       Optional[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
    return tokenizer

   @property
   def 		A							(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    torch.manual_seed(0							)
    a						=		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(_lowercase							)

   @property
   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    torch.manual_seed(0							)

    a						=		12
    a						=		12

    a						=		{
        """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""",
    }

    a						=		TransformeraDModel(**_lowercase							)
    return model

   def 		A							(      self			:       Any							)       ->				Optional[int]:


    """simple docstring"""






    a						=		"""cpu"""

    a						=		self.dummy_vqvae
    a						=		self.dummy_text_encoder
    a						=		self.dummy_tokenizer
    a						=		self.dummy_transformer
    a						=		VQDiffusionScheduler(self.num_embed							)
    a						=		LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase							)

    a						=		VQDiffusionPipeline(
        vqvae=_lowercase							,   text_encoder=_lowercase							,   tokenizer=_lowercase							,   transformer=_lowercase							,   scheduler=_lowercase							,   learned_classifier_free_sampling_embeddings=_lowercase							,   )
    a						=		pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)

    a						=		"""teddy bear playing in the pool"""

    a						=		torch.Generator(device=_lowercase							).manual_seed(0							)
    a						=		pipe([prompt]							,   generator=_lowercase							,   num_inference_steps=2							,   output_type="np"							)
    a						=		output.images

    a						=		torch.Generator(device=_lowercase							).manual_seed(0							)
    a						=		pipe(
        [prompt]							,   generator=_lowercase							,   output_type="np"							,   return_dict=_lowercase							,   num_inference_steps=2							)[0]

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

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

    a						=		np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]							)

    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 		A							(      self			:       Optional[Any]							)       ->				str:


    """simple docstring"""






    a						=		"""cpu"""

    a						=		self.dummy_vqvae
    a						=		self.dummy_text_encoder
    a						=		self.dummy_tokenizer
    a						=		self.dummy_transformer
    a						=		VQDiffusionScheduler(self.num_embed							)
    a						=		LearnedClassifierFreeSamplingEmbeddings(
        learnable=_lowercase							,   hidden_size=self.text_embedder_hidden_size							,   length=tokenizer.model_max_length							)

    a						=		VQDiffusionPipeline(
        vqvae=_lowercase							,   text_encoder=_lowercase							,   tokenizer=_lowercase							,   transformer=_lowercase							,   scheduler=_lowercase							,   learned_classifier_free_sampling_embeddings=_lowercase							,   )
    a						=		pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)

    a						=		"""teddy bear playing in the pool"""

    a						=		torch.Generator(device=_lowercase							).manual_seed(0							)
    a						=		pipe([prompt]							,   generator=_lowercase							,   num_inference_steps=2							,   output_type="np"							)
    a						=		output.images

    a						=		torch.Generator(device=_lowercase							).manual_seed(0							)
    a						=		pipe(
        [prompt]							,   generator=_lowercase							,   output_type="np"							,   return_dict=_lowercase							,   num_inference_steps=2							)[0]

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

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

    a						=		np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]							)

    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 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Optional[int]							)       ->				List[Any]:


    """simple docstring"""






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




   def 		A							(      self			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"							)

    a						=		VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"							)
    a						=		pipeline.to(_lowercase							)
    pipeline.set_progress_bar_config(disable=_lowercase							)

    # requires GPU generator for gumbel softmax
    # don't use GPU generator in tests though
    a						=		torch.Generator(device=_lowercase							).manual_seed(0							)
    a						=		pipeline(
        "teddy bear playing in the pool"							,   num_images_per_prompt=1							,   generator=_lowercase							,   output_type="np"							,   )

    a						=		output.images[0]

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






from __future__ import annotations


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		str(UpperCAmelCase__					)
 return len(UpperCAmelCase__					) == 9 and set(UpperCAmelCase__					) == set("123456789"					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 for base_num in range(99_99 ,					49_99 ,					-1					):
  a						=		10_00_02 * base_num
  if is_9_pandigital(UpperCAmelCase__					):
   return candidate

 for base_num in range(3_33 ,					99 ,					-1					):
  a						=		1_00_20_03 * base_num
  if is_9_pandigital(UpperCAmelCase__					):
   return candidate

 return None


if __name__ == "__main__":
     print(F"""{solution() = }""")

