

"""simple docstring"""




import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class      _UpperCAmelCase   (							unittest.TestCase       ):



	@property
	def        a  (  self     :       int						):
		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''')			,    )
		return model



	@property
	def        a  (  self     :       Any						):
		torch.manual_seed(0						)
		__UpperCAmelCase      		= 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			,    )
		return model



	@property
	def        a  (  self     :       Union[str, Any]						):
		torch.manual_seed(0						)
		__UpperCAmelCase      		= CLIPTextConfig(
		    bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1E-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=10_00			,    )
		return CLIPTextModel(_UpperCAmelCase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.dummy_uncond_unet
		__UpperCAmelCase      		= DDIMScheduler()
		__UpperCAmelCase      		= self.dummy_vq_model

		__UpperCAmelCase      		= LDMPipeline(unet=_UpperCAmelCase			,    vqvae=_UpperCAmelCase			,    scheduler=_UpperCAmelCase						)
		ldm.to(_UpperCAmelCase						)
		ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

		__UpperCAmelCase      		= torch.manual_seed(0						)
		__UpperCAmelCase      		= ldm(generator=_UpperCAmelCase			,    num_inference_steps=2			,    output_type='''numpy'''						).images

		__UpperCAmelCase      		= torch.manual_seed(0						)
		__UpperCAmelCase      		= ldm(generator=_UpperCAmelCase			,    num_inference_steps=2			,    output_type='''numpy'''			,    return_dict=_UpperCAmelCase						)[0]

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

		assert image.shape == (1, 64, 64, 3)
		__UpperCAmelCase      		= np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]						)
		__UpperCAmelCase      		= 1E-2 if torch_device != 'mps' else 3E-2

		assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance






@slow
@require_torch
class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''						)
		ldm.to(_UpperCAmelCase						)
		ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

		__UpperCAmelCase      		= torch.manual_seed(0						)
		__UpperCAmelCase      		= ldm(generator=_UpperCAmelCase			,    num_inference_steps=5			,    output_type='''numpy'''						).images

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

		assert image.shape == (1, 2_56, 2_56, 3)
		__UpperCAmelCase      		= np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]						)
		__UpperCAmelCase      		= 1E-2 if torch_device != 'mps' else 3E-2

		assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance





'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCAmelCase		     =  TypeVar('''T''')

class       __magic_name__		(       Generic[T]  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			bool = True						):
									_a	: dict[T, list[T]]								=				{}  # dictionary of lists
									_a	: Tuple								=				directed







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			T      ,_UpperCAmelCase   :			T						):

									if not self.directed:  # For undirected graphs
											# if both source vertex and destination vertex are both present in the
											# adjacency list, add destination vertex to source vertex list of adjacent
											# vertices and add source vertex to destination vertex list of adjacent
											# vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination vertex
											# to source vertex list of adjacent vertices, then create a new vertex with
											# destination vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: Optional[Any]								=				[source_vertex]
											# if only destination vertex is present in adjacency list, add source vertex
											# to destination vertex list of adjacent vertices, then create a new vertex
											# with source vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif destination_vertex in self.adj_list:
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
													_a	: Union[str, Any]								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and assign a list
											# containing the destination vertex as it's first adjacent vertex also
											# create a new vertex with destination vertex as key and assign a list
											# containing the source vertex as it's first adjacent vertex.
											else:
													_a	: Union[str, Any]								=				[destination_vertex]
													_a	: Optional[int]								=				[source_vertex]
									else:  # For directed graphs
											# if both source vertex and destination vertex are present in adjacency
											# list, add destination vertex to source vertex list of adjacent vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination
											# vertex to source vertex list of adjacent vertices and create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: int								=				[]
											# if only destination vertex is present in adjacency list, create a new
											# vertex with source vertex as key and assign a list containing destination
											# vertex as first adjacent vertex
											elif destination_vertex in self.adj_list:
													_a	: Tuple								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and a list containing
											# destination vertex as it's first adjacent vertex. Then create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											else:
													_a	: Tuple								=				[destination_vertex]
													_a	: str								=				[]

									return self







							def __repr__(				self   :			int						):
									return pformat(self.adj_list						)




