





"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					DiTPipeline
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  SCREAMING_SNAKE_CASE__							:Dict	     =					PipelineTesterMixin.required_optional_params - {
      "latents",
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  SCREAMING_SNAKE_CASE__							:Any	     =					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__							:str	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[Any]:
        torch.manual_seed(0	)
        _UpperCamelCase      :	int						=       TransformeraDModel(
            sample_size=16					,   num_layers=2					,   patch_size=4					,   attention_head_dim=8					,   num_attention_heads=2					,   in_channels=4					,   out_channels=8					,   attention_bias=__a					,   activation_fn="gelu-approximate"					,   num_embeds_ada_norm=1000					,   norm_type="ada_norm_zero"					,   norm_elementwise_affine=__a					,   )
        _UpperCamelCase      :	Any						=       AutoencoderKL()
        _UpperCamelCase      :	Dict						=       DDIMScheduler()
        _UpperCamelCase      :	str						=       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
        return components

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       Any=0	)		->				List[str]:
        if str(__a	).startswith("mps"	):
              _UpperCamelCase      :	Optional[int]						=       torch.manual_seed(__a	)
        else:
              _UpperCamelCase      :	Dict						=       torch.Generator(device=__a	).manual_seed(__a	)
        _UpperCamelCase      :	Optional[Any]						=       {
            "class_labels": [1],
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[int]						=       "cpu"

        _UpperCamelCase      :	Optional[int]						=       self.get_dummy_components()
        _UpperCamelCase      :	str						=       self.pipeline_class(**__a	)
        pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)

        _UpperCamelCase      :	Any						=       self.get_dummy_inputs(__a	)
        _UpperCamelCase      :	str						=       pipe(**__a	).images
        _UpperCamelCase      :	Any						=       image[0, -3:, -3:, -1]

        self.assertEqual(image.shape					,   (1, 16, 16, 3)	)
        _UpperCamelCase      :	Any						=       np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]	)
        _UpperCamelCase      :	Optional[Any]						=       np.abs(image_slice.flatten() - expected_slice	).max()
        self.assertLessEqual(__a					,   1e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Any:
        self._test_inference_batch_single_identical(relax_max_difference=__a					,   expected_max_diff=1e-3	)


  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()					,   reason="XFormers attention is only available with CUDA and `xformers` installed"					,   )
  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Any:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)





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







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Tuple:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				List[str]:
        _UpperCamelCase      :	Dict						=       torch.manual_seed(0	)

        _UpperCamelCase      :	List[str]						=       DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"	)
        pipe.to("cuda"	)

        _UpperCamelCase      :	Dict						=       ["vase", "umbrella", "white shark", "white wolf"]
        _UpperCamelCase      :	Any						=       pipe.get_label_ids(__a	)

        _UpperCamelCase      :	Optional[int]						=       pipe(__a					,   generator=__a					,   num_inference_steps=40					,   output_type="np"	).images

        for word, image in zip(__a					,   __a	):
              _UpperCamelCase      :	List[str]						=       load_numpy(
                  F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''	)
              assert np.abs((expected_image - image).max()	) < 1e-2


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Union[str, Any]:
        _UpperCamelCase      :	Optional[int]						=       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"	)
        _UpperCamelCase      :	Union[str, Any]						=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
        pipe.to("cuda"	)

        _UpperCamelCase      :	Tuple						=       ["vase", "umbrella"]
        _UpperCamelCase      :	Optional[int]						=       pipe.get_label_ids(__a	)

        _UpperCamelCase      :	Optional[Any]						=       torch.manual_seed(0	)
        _UpperCamelCase      :	List[str]						=       pipe(__a					,   generator=__a					,   num_inference_steps=25					,   output_type="np"	).images

        for word, image in zip(__a					,   __a	):
              _UpperCamelCase      :	str						=       load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  F'''/dit/{word}_512.npy'''	)

              assert np.abs((expected_image - image).max()	) < 1e-1







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
