





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =DDIMPipeline
      _snake_case				  =UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      _snake_case				  =PipelineTesterMixin.required_optional_params - {
          '''num_images_per_prompt''',
          '''latents''',
          '''callback''',
          '''callback_steps''',
      }
      _snake_case				  =UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      _snake_case				  =False



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     str:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =UNetaDModel(
             block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=3		,	out_channels=3		,	down_block_types=("DownBlock2D", "AttnDownBlock2D")		,	up_block_types=("AttnUpBlock2D", "UpBlock2D")		,	)
         UpperCAmelCase_           =DDIMScheduler()
         UpperCAmelCase_           ={"unet": unet, "scheduler": scheduler}
         return components



      def 						lowerCAmelCase__     (						self:    str		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Union[str, Any]=0							)    ->     Tuple:
         '''simple docstring'''

         if str(_lowerCAmelCase							).startswith("mps"							):
            UpperCAmelCase_           =torch.manual_seed(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "batch_size": 1,
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }
         return inputs



      def 						lowerCAmelCase__     (						self:    str							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"

         UpperCAmelCase_           =self.get_dummy_components()
         UpperCAmelCase_           =self.pipeline_class(**_lowerCAmelCase							)
         pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs(_lowerCAmelCase							)
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape		,	(1, 32, 32, 3)							)
         UpperCAmelCase_           =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]							)
         UpperCAmelCase_           =np.abs(image_slice.flatten() - expected_slice							).max()
         self.assertLessEqual(_lowerCAmelCase		,	1e-3							)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Tuple:
         '''simple docstring'''

         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3							)



      def 						lowerCAmelCase__     (						self:    int							)    ->     Union[str, Any]:
         '''simple docstring'''

         super().test_save_load_local(expected_max_difference=3e-3							)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     str:
         '''simple docstring'''

         super().test_save_load_optional_components(expected_max_difference=3e-3							)






      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[int]:
         '''simple docstring'''

         super().test_inference_batch_single_identical(expected_max_diff=3e-3							)

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



      def 						lowerCAmelCase__     (						self:    Any							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           ="google/ddpm-cifar10-32"

         UpperCAmelCase_           =UNetaDModel.from_pretrained(_lowerCAmelCase							)
         UpperCAmelCase_           =DDIMScheduler()

         UpperCAmelCase_           =DDIMPipeline(unet=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)
         ddim.to(_lowerCAmelCase							)
         ddim.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =torch.manual_seed(0							)
         UpperCAmelCase_           =ddim(generator=_lowerCAmelCase		,	eta=0.0		,	output_type="numpy"							).images

         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase_           =np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]							)

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






      def 						lowerCAmelCase__     (						self:    Any							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           ="google/ddpm-ema-bedroom-256"

         UpperCAmelCase_           =UNetaDModel.from_pretrained(_lowerCAmelCase							)
         UpperCAmelCase_           =DDIMScheduler.from_pretrained(_lowerCAmelCase							)

         UpperCAmelCase_           =DDIMPipeline(unet=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)
         ddpm.to(_lowerCAmelCase							)
         ddpm.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =torch.manual_seed(0							)
         UpperCAmelCase_           =ddpm(generator=_lowerCAmelCase		,	output_type="numpy"							).images

         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         UpperCAmelCase_           =np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]							)

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







'''simple docstring'''



def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # edges = list of graph's edges
    lowerCAmelCase 						=							get_edges(snake_case			)

    # 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:
        lowerCAmelCase						,   lowerCAmelCase 						=							edges.pop()
        chosen_vertices.add(snake_case			)
        chosen_vertices.add(snake_case			)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case			)
    return chosen_vertices


def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							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)}")





