'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						(	UpperCAmelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[Any]						   =		DDIMPipeline
  lowerCamelCase_		:str						   =		UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowerCamelCase_		:Any						   =		PipelineTesterMixin.required_optional_params - {
      """num_images_per_prompt""",
      """latents""",
      """callback""",
      """callback_steps""",
  }
  lowerCamelCase_		:str						   =		UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowerCamelCase_		:Optional[Any]						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       torch.manual_seed(0						)
       UpperCAmelCase_							:       Optional[int]       =				UNetaDModel(
           block_out_channels=(3_2, 6_4)				,		layers_per_block=2				,		sample_size=3_2				,		in_channels=3				,		out_channels=3				,		down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,		up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,		)
       UpperCAmelCase_							:       int       =				DDIMScheduler()
       UpperCAmelCase_							:       Any       =				{'''unet''': unet, '''scheduler''': scheduler}
       return components


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=0						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				'''cpu'''

       UpperCAmelCase_							:       int       =				self.get_dummy_components()
       UpperCAmelCase_							:       List[Any]       =				self.pipeline_class(**__lowercase						)
       pipe.to(__lowercase						)
       pipe.set_progress_bar_config(disable=__lowercase						)

       UpperCAmelCase_							:       Any       =				self.get_dummy_inputs(__lowercase						)
       UpperCAmelCase_							:       Union[str, Any]       =				pipe(**__lowercase						).images
       UpperCAmelCase_							:       List[Any]       =				image[0, -3:, -3:, -1]

       self.assertEqual(image.shape				,		(1, 3_2, 3_2, 3)						)
       UpperCAmelCase_							:       int       =				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_							:       str       =				np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(__lowercase				,		1E-3						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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







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







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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

       UpperCAmelCase_							:       Optional[Any]       =				UNetaDModel.from_pretrained(__lowercase						)
       UpperCAmelCase_							:       Dict       =				DDIMScheduler()

       UpperCAmelCase_							:       Optional[Any]       =				DDIMPipeline(unet=__lowercase				,		scheduler=__lowercase						)
       ddim.to(__lowercase						)
       ddim.set_progress_bar_config(disable=__lowercase						)

       UpperCAmelCase_							:       List[str]       =				torch.manual_seed(0						)
       UpperCAmelCase_							:       Optional[int]       =				ddim(generator=__lowercase				,		eta=0.0				,		output_type='numpy'						).images

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

       assert image.shape == (1, 3_2, 3_2, 3)
       UpperCAmelCase_							:       Union[str, Any]       =				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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				'''google/ddpm-ema-bedroom-256'''

       UpperCAmelCase_							:       str       =				UNetaDModel.from_pretrained(__lowercase						)
       UpperCAmelCase_							:       int       =				DDIMScheduler.from_pretrained(__lowercase						)

       UpperCAmelCase_							:       Any       =				DDIMPipeline(unet=__lowercase				,		scheduler=__lowercase						)
       ddpm.to(__lowercase						)
       ddpm.set_progress_bar_config(disable=__lowercase						)

       UpperCAmelCase_							:       Dict       =				torch.manual_seed(0						)
       UpperCAmelCase_							:       Any       =				ddpm(generator=__lowercase				,		output_type='numpy'						).images

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

       assert image.shape == (1, 2_5_6, 2_5_6, 3)
       UpperCAmelCase_							:       Dict       =				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'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:int
  lowerCamelCase_		:int







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       list[list[Edge]]       =				[[] for _ in range(snake_case_						)]
       UpperCAmelCase_							:       Any       =				size


  def __getitem__(			self				,		snake_case_						):




       '''simple docstring'''

       return iter(self._graph[vertex]						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._size


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if weight not in (0, 1):
            raise ValueError('Edge weight must be either 0 or 1.'						)

       if to_vertex < 0 or to_vertex >= self.size:
            raise ValueError('Vertex indexes must be in [0; size).'						)

       self._graph[from_vertex].append(Edge(snake_case_				,		snake_case_						)						)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				deque([start_vertex]						)
       UpperCAmelCase_							:       list[int | None]       =				[None] * self.size
       UpperCAmelCase_							:       str       =				0

       while queue:
            UpperCAmelCase_							:       Optional[int]       =				queue.popleft()
            UpperCAmelCase_							:       List[str]       =				distances[current_vertex]
            if current_distance is None:
                 continue

            for edge in self[current_vertex]:
                 UpperCAmelCase_							:       Union[str, Any]       =				current_distance + edge.weight
                 UpperCAmelCase_							:       Union[str, Any]       =				distances[edge.destination_vertex]
                 if (
                     isinstance(snake_case_				,		snake_case_						)
                     and new_distance >= dest_vertex_distance
                 ):
                      continue
                 UpperCAmelCase_							:       Optional[Any]       =				new_distance
                 if edge.weight == 0:
                      queue.appendleft(edge.destination_vertex						)
                 else:
                      queue.append(edge.destination_vertex						)

       if distances[finish_vertex] is None:
            raise ValueError('No path from start_vertex to finish_vertex.'						)

       return distances[finish_vertex]


if __name__ == "__main__":
 import doctest

 doctest.testmod()






