

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
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							):


		'''simple docstring'''







		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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.dummy_uncond_unet
		UpperCAmelCase_       				=			ScoreSdeVeScheduler()

		UpperCAmelCase_       				=			ScoreSdeVePipeline(unet=lowerCAmelCase		,	scheduler=lowerCAmelCase							)
		sde_ve.to(lowerCAmelCase							)
		sde_ve.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sde_ve(num_inference_steps=2		,	output_type="numpy"		,	generator=lowerCAmelCase							).images

		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sde_ve(num_inference_steps=2		,	output_type="numpy"		,	generator=lowerCAmelCase		,	return_dict=lowerCAmelCase							)[
		    0
		]

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

		assert image.shape == (1, 32, 32, 3)
		UpperCAmelCase_       				=			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 						__UpperCamelCase    (					unittest.TestCase						):




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"google/ncsnpp-church-256"
		UpperCAmelCase_       				=			UNetaDModel.from_pretrained(lowerCAmelCase							)

		UpperCAmelCase_       				=			ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase							)

		UpperCAmelCase_       				=			ScoreSdeVePipeline(unet=lowerCAmelCase		,	scheduler=lowerCAmelCase							)
		sde_ve.to(lowerCAmelCase							)
		sde_ve.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sde_ve(num_inference_steps=10		,	output_type="numpy"		,	generator=lowerCAmelCase							).images

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

		assert image.shape == (1, 256, 256, 3)

		UpperCAmelCase_       				=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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


from .imports import is_tqdm_available


if is_tqdm_available():
				from tqdm.auto import tqdm as _tqdm

from ..state import PartialState



def       __lowerCAmelCase      (						A = True       ,     *A       ,     **A    ):
	if not is_tqdm_available():
		raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."    )
	UpperCAmelCase_       				=			False
	if main_process_only:
		UpperCAmelCase_       				=			PartialState().local_process_index == 0
	return _tqdm(*A       ,     **A       ,     disable=A    )