





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     (_SCREAMING_SNAKE_CASE							,unittest.TestCase					):
      _SCREAMING_SNAKE_CASE       =				DDIMPipeline
      _SCREAMING_SNAKE_CASE       =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      _SCREAMING_SNAKE_CASE       =				PipelineTesterMixin.required_optional_params - {
          """num_images_per_prompt""",
          """latents""",
          """callback""",
          """callback_steps""",
      }
      _SCREAMING_SNAKE_CASE       =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE       =				False



      def  __a			(       self       )   -> int:



             '''simple docstring'''

             torch.manual_seed(0       )
             _snake_case   :				int  					=    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   :				Any  					=    DDIMScheduler()
             _snake_case   :				Any  					=    {'''unet''': unet, '''scheduler''': scheduler}
             return components



      def  __a			(       self  ,	lowercase_  ,	lowercase_=0       )   -> Dict:



             '''simple docstring'''

             if str(__snake_case       ).startswith('''mps'''       ):
                    _snake_case   :				List[str]  					=    torch.manual_seed(__snake_case       )
             else:
                    _snake_case   :				Optional[int]  					=    torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
             _snake_case   :				Optional[Any]  					=    {
                 '''batch_size''': 1,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }
             return inputs



      def  __a			(       self       )   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    '''cpu'''

             _snake_case   :				str  					=    self.get_dummy_components()
             _snake_case   :				List[Any]  					=    self.pipeline_class(**__snake_case       )
             pipe.to(__snake_case       )
             pipe.set_progress_bar_config(disable=__snake_case       )

             _snake_case   :				Tuple  					=    self.get_dummy_inputs(__snake_case       )
             _snake_case   :				List[Any]  					=    pipe(**__snake_case       ).images
             _snake_case   :				int  					=    image[0, -3:, -3:, -1]

             self.assertEqual(image.shape  ,	(1, 32, 32, 3)       )
             _snake_case   :				str  					=    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   :				Optional[Any]  					=    np.abs(image_slice.flatten() - expected_slice       ).max()
             self.assertLessEqual(__snake_case  ,	1E-3       )



      def  __a			(       self       )   -> Tuple:



             '''simple docstring'''

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



      def  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

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



      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

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



      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

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


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



      def  __a			(       self       )   -> int:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    '''google/ddpm-cifar10-32'''

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

             _snake_case   :				List[Any]  					=    DDIMPipeline(unet=__snake_case  ,	scheduler=__snake_case       )
             ddim.to(__snake_case       )
             ddim.set_progress_bar_config(disable=__snake_case       )

             _snake_case   :				Optional[Any]  					=    torch.manual_seed(0       )
             _snake_case   :				List[str]  					=    ddim(generator=__snake_case  ,	eta=0.0  ,	output_type='''numpy'''       ).images

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

             assert image.shape == (1, 32, 32, 3)
             _snake_case   :				Tuple  					=    np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]       )

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



      def  __a			(       self       )   -> Tuple:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    '''google/ddpm-ema-bedroom-256'''

             _snake_case   :				Optional[Any]  					=    UNetaDModel.from_pretrained(__snake_case       )
             _snake_case   :				str  					=    DDIMScheduler.from_pretrained(__snake_case       )

             _snake_case   :				List[Any]  					=    DDIMPipeline(unet=__snake_case  ,	scheduler=__snake_case       )
             ddpm.to(__snake_case       )
             ddpm.set_progress_bar_config(disable=__snake_case       )

             _snake_case   :				List[str]  					=    torch.manual_seed(0       )
             _snake_case   :				Any  					=    ddpm(generator=__snake_case  ,	output_type='''numpy'''       ).images

             _snake_case   :				Optional[int]  					=    image[0, -3:, -3:, -1]

             assert image.shape == (1, 256, 256, 3)
             _snake_case   :				int  					=    np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]       )

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













# 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

a__      :     Optional[int]         =		TypeVar('T')





class UpperCAmelCase_				( Generic[T] ):


  def __init__(							self ,__snake_case = True ):



       """simple docstring"""





       A_          =       {}  # dictionary of lists
       A_          =       directed


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       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(__snake_case )
                 self.adj_list[destination_vertex].append(__snake_case )
            # 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(__snake_case )
                 A_          =       [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(__snake_case )
                 A_          =       [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_          =       [destination_vertex]
                 A_          =       [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(__snake_case )
            # 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(__snake_case )
                 A_          =       []
            # 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_          =       [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_          =       [destination_vertex]
                 A_          =       []

       return self




  def __repr__(							self ):



       """simple docstring"""





       return pformat(self.adj_list )



