


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	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Dict  						=						DDIMPipeline
							__UpperCAmelCase					:							Any  						=						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
							__UpperCAmelCase					:							Tuple  						=						PipelineTesterMixin.required_optional_params - {
							    'num_images_per_prompt',
							    'latents',
							    'callback',
							    'callback_steps',
							}
							__UpperCAmelCase					:							str  						=						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
							__UpperCAmelCase					:							int  						=						False

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														torch.manual_seed(0       )
														__snake_case	:    List[str]											=     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")					,			)
														__snake_case	:    Optional[Any]											=     DDIMScheduler()
														__snake_case	:    int											=     {'''unet''': unet, '''scheduler''': scheduler}
														return components

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

							def 					__snake_case      (							self :						int       )			->					List[Any]:
														__snake_case	:    Dict											=     '''cpu'''

														__snake_case	:    Any											=     self.get_dummy_components()
														__snake_case	:    int											=     self.pipeline_class(**UpperCAmelCase__       )
														pipe.to(UpperCAmelCase__       )
														pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

														__snake_case	:    Any											=     self.get_dummy_inputs(UpperCAmelCase__       )
														__snake_case	:    Optional[int]											=     pipe(**UpperCAmelCase__       ).images
														__snake_case	:    Dict											=     image[0, -3:, -3:, -1]

														self.assertEqual(image.shape					,			(1, 32, 32, 3)       )
														__snake_case	:    Dict											=     np.array(
														    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]       )
														__snake_case	:    Tuple											=     np.abs(image_slice.flatten() - expected_slice       ).max()
														self.assertLessEqual(UpperCAmelCase__					,			1E-3       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[int]:
														super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[Any]:
														super().test_save_load_local(expected_max_difference=3E-3       )

							def 					__snake_case      (							self :						Tuple       )			->					List[Any]:
														super().test_save_load_optional_components(expected_max_difference=3E-3       )






							def 					__snake_case      (							self :						List[Any]       )			->					int:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3       )

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




							"""simple docstring"""


							def 					__snake_case      (							self :						Any       )			->					int:
														__snake_case	:    List[str]											=     '''google/ddpm-cifar10-32'''

														__snake_case	:    str											=     UNetaDModel.from_pretrained(UpperCAmelCase__       )
														__snake_case	:    List[Any]											=     DDIMScheduler()

														__snake_case	:    Tuple											=     DDIMPipeline(unet=UpperCAmelCase__					,			scheduler=UpperCAmelCase__       )
														ddim.to(UpperCAmelCase__       )
														ddim.set_progress_bar_config(disable=UpperCAmelCase__       )

														__snake_case	:    Optional[int]											=     torch.manual_seed(0       )
														__snake_case	:    str											=     ddim(generator=UpperCAmelCase__					,			eta=0.0					,			output_type="numpy"       ).images

														__snake_case	:    Any											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														__snake_case	:    int											=     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 					__snake_case      (							self :						Dict       )			->					Union[str, Any]:
														__snake_case	:    str											=     '''google/ddpm-ema-bedroom-256'''

														__snake_case	:    Optional[int]											=     UNetaDModel.from_pretrained(UpperCAmelCase__       )
														__snake_case	:    Optional[int]											=     DDIMScheduler.from_pretrained(UpperCAmelCase__       )

														__snake_case	:    Tuple											=     DDIMPipeline(unet=UpperCAmelCase__					,			scheduler=UpperCAmelCase__       )
														ddpm.to(UpperCAmelCase__       )
														ddpm.set_progress_bar_config(disable=UpperCAmelCase__       )

														__snake_case	:    Union[str, Any]											=     torch.manual_seed(0       )
														__snake_case	:    str											=     ddpm(generator=UpperCAmelCase__					,			output_type="numpy"       ).images

														__snake_case	:    Union[str, Any]											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 256, 256, 3)
														__snake_case	:    Optional[Any]											=     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





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

