
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@property
							def 	lowercase							(self  )					->       Optional[Any]:
														torch.manual_seed(0  )
														_snake_case       =  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





							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  self.dummy_uncond_unet
														_snake_case       =  KarrasVeScheduler()

														_snake_case       =  KarrasVePipeline(unet=snake_case__  ,  scheduler=snake_case__  )
														pipe.to(snake_case__  )
														pipe.set_progress_bar_config(disable=snake_case__  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(num_inference_steps=2  ,  generator=snake_case__  ,  output_type="""numpy"""  ).images

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(num_inference_steps=2  ,  generator=snake_case__  ,  output_type="""numpy"""  ,  return_dict=snake_case__  )[0]

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

														assert image.shape == (1, 32, 32, 3)
														_snake_case       =  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]  )

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


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  "google/ncsnpp-celebahq-256"
														_snake_case       =  UNetaDModel.from_pretrained(snake_case__  )
														_snake_case       =  KarrasVeScheduler()

														_snake_case       =  KarrasVePipeline(unet=snake_case__  ,  scheduler=snake_case__  )
														pipe.to(snake_case__  )
														pipe.set_progress_bar_config(disable=snake_case__  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(num_inference_steps=20  ,  generator=snake_case__  ,  output_type="""numpy"""  ).images

														_snake_case       =  image[0, -3:, -3:, -1]
														assert image.shape == (1, 256, 256, 3)
														_snake_case       =  np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]  )

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

'''simple docstring'''
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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  hf_hub_url(repo_id=_SCREAMING_SNAKE_CASE							, path=_SCREAMING_SNAKE_CASE							, revision=_SCREAMING_SNAKE_CASE				)
							assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(_SCREAMING_SNAKE_CASE				)}"""