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   :     str       =				False


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  @property
  def       __A   (							self       :				Union[str, Any]      )     ->		Dict:
        return 12







  @property
  def       __A   (							self       :				Any      )     ->		Optional[Any]:
        return 12







  @property
  def       __A   (							self       :				Union[str, Any]      )     ->		Tuple:
        return 32







  @property
  def       __A   (							self       :				List[Any]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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       :				List[str]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )
        return tokenizer







  @property
  def       __A   (							self       :				int      )     ->		List[Any]:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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=1_000     ,   )
        return CLIPTextModel(__magic_name__      )







  @property
  def       __A   (							self       :				Union[str, Any]      )     ->		Any:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     12
        SCREAMING_SNAKE_CASE_      =     12

        SCREAMING_SNAKE_CASE_      =     {
            "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",
        }

        SCREAMING_SNAKE_CASE_      =     TransformeraDModel(**__magic_name__      )
        return model







  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.dummy_vqvae
        SCREAMING_SNAKE_CASE_      =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_      =     self.dummy_tokenizer
        SCREAMING_SNAKE_CASE_      =     self.dummy_transformer
        SCREAMING_SNAKE_CASE_      =     VQDiffusionScheduler(self.num_embed      )
        SCREAMING_SNAKE_CASE_      =     LearnedClassifierFreeSamplingEmbeddings(learnable=__magic_name__      )

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

        SCREAMING_SNAKE_CASE_      =     "teddy bear playing in the pool"

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

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

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

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

        SCREAMING_SNAKE_CASE_      =     np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]      )

        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       :				Any      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     "cpu"

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

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

        SCREAMING_SNAKE_CASE_      =     "teddy bear playing in the pool"

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

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

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

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

        SCREAMING_SNAKE_CASE_      =     np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]      )

        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      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Optional[int]      )     ->		List[str]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				str      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"      )

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

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

        SCREAMING_SNAKE_CASE_      =     output.images[0]

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



def    a__       (						__UpperCamelCase = 1_0_0_0 ):
      SCREAMING_SNAKE_CASE_      =     -1
      SCREAMING_SNAKE_CASE_      =     0
      for a in range(1   ,					n // 3 ):
            # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
            SCREAMING_SNAKE_CASE_      =     (n * n - 2 * a * n) // (2 * n - 2 * a)
            SCREAMING_SNAKE_CASE_      =     n - a - b
            if c * c == (a * a + b * b):
                  SCREAMING_SNAKE_CASE_      =     a * b * c
                  if candidate >= product:
                        SCREAMING_SNAKE_CASE_      =     candidate
      return product


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


