






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 						lowerCamelCase_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  DDIMPipeline
				a__					       =  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				a__					       =  PipelineTesterMixin.required_optional_params - {
				    '''num_images_per_prompt''',
				    '''latents''',
				    '''callback''',
				    '''callback_steps''',
				}
				a__					       =  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:List[str]        =      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''')       ,					)
								__magic_name__		:int        =      DDIMScheduler()
								__magic_name__		:List[Any]        =      {'''unet''': unet, '''scheduler''': scheduler}
								return components







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=0     ):
								"""simple docstring"""
								if str(__lowerCAmelCase     ).startswith('''mps'''     ):
												__magic_name__		:Dict        =      torch.manual_seed(__lowerCAmelCase     )
								else:
												__magic_name__		:List[Any]        =      torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
								__magic_name__		:int        =      {
								    '''batch_size''': 1,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      '''cpu'''

								__magic_name__		:Dict        =      self.get_dummy_components()
								__magic_name__		:Union[str, Any]        =      self.pipeline_class(**__lowerCAmelCase     )
								pipe.to(__lowerCAmelCase     )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:Dict        =      self.get_dummy_inputs(__lowerCAmelCase     )
								__magic_name__		:Tuple        =      pipe(**__lowerCAmelCase     ).images
								__magic_name__		:List[Any]        =      image[0, -3:, -3:, -1]

								self.assertEqual(image.shape       ,					(1, 3_2, 3_2, 3)     )
								__magic_name__		:List[Any]        =      np.array(
								    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]     )
								__magic_name__		:Union[str, Any]        =      np.abs(image_slice.flatten() - expected_slice     ).max()
								self.assertLessEqual(__lowerCAmelCase       ,					1E-3     )







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_save_load_local(expected_max_difference=3E-3     )







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_save_load_optional_components(expected_max_difference=3E-3     )







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      '''google/ddpm-cifar10-32'''

								__magic_name__		:List[Any]        =      UNetaDModel.from_pretrained(__lowerCAmelCase     )
								__magic_name__		:int        =      DDIMScheduler()

								__magic_name__		:Union[str, Any]        =      DDIMPipeline(unet=__lowerCAmelCase       ,					scheduler=__lowerCAmelCase     )
								ddim.to(__lowerCAmelCase     )
								ddim.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:int        =      torch.manual_seed(0     )
								__magic_name__		:Tuple        =      ddim(generator=__lowerCAmelCase       ,					eta=0.0       ,					output_type='''numpy'''     ).images

								__magic_name__		:int        =      image[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__magic_name__		:List[Any]        =      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     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      '''google/ddpm-ema-bedroom-256'''

								__magic_name__		:Dict        =      UNetaDModel.from_pretrained(__lowerCAmelCase     )
								__magic_name__		:Optional[int]        =      DDIMScheduler.from_pretrained(__lowerCAmelCase     )

								__magic_name__		:List[str]        =      DDIMPipeline(unet=__lowerCAmelCase       ,					scheduler=__lowerCAmelCase     )
								ddpm.to(__lowerCAmelCase     )
								ddpm.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:Tuple        =      torch.manual_seed(0     )
								__magic_name__		:Tuple        =      ddpm(generator=__lowerCAmelCase       ,					output_type='''numpy'''     ).images

								__magic_name__		:Tuple        =      image[0, -3:, -3:, -1]

								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__magic_name__		:Any        =      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














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






