





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase:	Union[str, Any] = logging.get_logger(__name__)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Dict=False			)   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		OrderedDict()
		for key, value in state_dict.items():
				if encoder_only and not key.startswith('head'			):
						lowercase__					:      List[Any]      =		'segformer.encoder.' + key
				if key.startswith('backbone'			):
						lowercase__					:      Dict      =		key.replace('backbone'   ,    'segformer.encoder'			)
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowercase__					:      Tuple      =		key[key.find('patch_embed'			) + len('patch_embed'			)]
						lowercase__					:      str      =		key.replace(f"""patch_embed{idx}"""   ,    f"""patch_embeddings.{int(_lowercase			)-1}"""			)
				if "norm" in key:
						lowercase__					:      Optional[int]      =		key.replace('norm'   ,    'layer_norm'			)
				if "segformer.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowercase__					:      Union[str, Any]      =		key[key.find('segformer.encoder.layer_norm'			) + len('segformer.encoder.layer_norm'			)]
						lowercase__					:      int      =		key.replace(f"""layer_norm{idx}"""   ,    f"""layer_norm.{int(_lowercase			)-1}"""			)
				if "layer_norm1" in key:
						lowercase__					:      str      =		key.replace('layer_norm1'   ,    'layer_norm_1'			)
				if "layer_norm2" in key:
						lowercase__					:      Optional[Any]      =		key.replace('layer_norm2'   ,    'layer_norm_2'			)
				if "block" in key:
						# replace for example block1 by block.0
						lowercase__					:      Optional[int]      =		key[key.find('block'			) + len('block'			)]
						lowercase__					:      List[str]      =		key.replace(f"""block{idx}"""   ,    f"""block.{int(_lowercase			)-1}"""			)
				if "attn.q" in key:
						lowercase__					:      Union[str, Any]      =		key.replace('attn.q'   ,    'attention.self.query'			)
				if "attn.proj" in key:
						lowercase__					:      Optional[Any]      =		key.replace('attn.proj'   ,    'attention.output.dense'			)
				if "attn" in key:
						lowercase__					:      int      =		key.replace('attn'   ,    'attention.self'			)
				if "fc1" in key:
						lowercase__					:      Any      =		key.replace('fc1'   ,    'dense1'			)
				if "fc2" in key:
						lowercase__					:      Union[str, Any]      =		key.replace('fc2'   ,    'dense2'			)
				if "linear_pred" in key:
						lowercase__					:      str      =		key.replace('linear_pred'   ,    'classifier'			)
				if "linear_fuse" in key:
						lowercase__					:      Tuple      =		key.replace('linear_fuse.conv'   ,    'linear_fuse'			)
						lowercase__					:      int      =		key.replace('linear_fuse.bn'   ,    'batch_norm'			)
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowercase__					:      Optional[Any]      =		key[key.find('linear_c'			) + len('linear_c'			)]
						lowercase__					:      int      =		key.replace(f"""linear_c{idx}"""   ,    f"""linear_c.{int(_lowercase			)-1}"""			)
				if key.startswith('head'			):
						lowercase__					:      Optional[Any]      =		key.replace('head'   ,    'classifier'			)
				lowercase__					:      str      =		value

		return new_state_dict


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : Any			)   ->							List[Any]:




		'''simple docstring'''
		# for each of the encoder blocks:
		for i in range(config.num_encoder_blocks			):
				for j in range(config.depths[i]			):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowercase__					:      Any      =		state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""			)
						lowercase__					:      Dict      =		state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""			)
						# next, add keys and values (in that order) to the state dict
						lowercase__					:      Optional[int]      =		kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowercase__					:      Any      =		kv_bias[: config.hidden_sizes[i]]
						lowercase__					:      Union[str, Any]      =		kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowercase__					:      Optional[Any]      =		kv_bias[
						    config.hidden_sizes[i] :
						]


def       SCREAMING_SNAKE_CASE__  (       )   ->							str:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowercase__					:      List[Any]      =		Image.open(requests.get(_lowercase   ,    stream=_lowercase			).raw			)

		return image


@torch.no_grad()
def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Any   ,    _lowercase     : Tuple			)   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      Tuple      =		SegformerConfig()
		lowercase__					:      Union[str, Any]      =		False

		# set attributes based on model_name
		lowercase__					:      Dict      =		'huggingface/label-files'
		if "segformer" in model_name:
				lowercase__					:      Union[str, Any]      =		model_name[len('segformer.'			) : len('segformer.'			) + 2]
				if "ade" in model_name:
						lowercase__					:      List[str]      =		150
						lowercase__					:      str      =		'ade20k-id2label.json'
						lowercase__					:      List[str]      =		(1, 150, 128, 128)
				elif "city" in model_name:
						lowercase__					:      Union[str, Any]      =		19
						lowercase__					:      Union[str, Any]      =		'cityscapes-id2label.json'
						lowercase__					:      Optional[int]      =		(1, 19, 128, 128)
				else:
						raise ValueError(f"""Model {model_name} not supported"""			)
		elif "mit" in model_name:
				lowercase__					:      Dict      =		True
				lowercase__					:      Any      =		model_name[4:6]
				lowercase__					:      Union[str, Any]      =		1_000
				lowercase__					:      Optional[Any]      =		'imagenet-1k-id2label.json'
				lowercase__					:      Any      =		(1, 1_000)
		else:
				raise ValueError(f"""Model {model_name} not supported"""			)

		# set config attributes
		lowercase__					:      Any      =		json.load(open(hf_hub_download(_lowercase   ,    _lowercase   ,    repo_type='dataset'			)   ,    'r'			)			)
		lowercase__					:      List[Any]      =		{int(_lowercase			): v for k, v in idalabel.items()}
		lowercase__					:      Any      =		idalabel
		lowercase__					:      Dict      =		{v: k for k, v in idalabel.items()}
		if size == "b0":
				pass
		elif size == "b1":
				lowercase__					:      List[Any]      =		[64, 128, 320, 512]
				lowercase__					:      Union[str, Any]      =		256
		elif size == "b2":
				lowercase__					:      Optional[int]      =		[64, 128, 320, 512]
				lowercase__					:      List[Any]      =		768
				lowercase__					:      int      =		[3, 4, 6, 3]
		elif size == "b3":
				lowercase__					:      Optional[int]      =		[64, 128, 320, 512]
				lowercase__					:      List[Any]      =		768
				lowercase__					:      List[str]      =		[3, 4, 18, 3]
		elif size == "b4":
				lowercase__					:      Optional[Any]      =		[64, 128, 320, 512]
				lowercase__					:      Union[str, Any]      =		768
				lowercase__					:      str      =		[3, 8, 27, 3]
		elif size == "b5":
				lowercase__					:      Optional[Any]      =		[64, 128, 320, 512]
				lowercase__					:      int      =		768
				lowercase__					:      Optional[int]      =		[3, 6, 40, 3]
		else:
				raise ValueError(f"""Size {size} not supported"""			)

		# load image processor (only resize + normalize)
		lowercase__					:      Dict      =		SegformerImageProcessor(
		    image_scale=(512, 512)   ,    keep_ratio=_lowercase   ,    align=_lowercase   ,    do_random_crop=_lowercase			)

		# prepare image
		lowercase__					:      Tuple      =		prepare_img()
		lowercase__					:      int      =		image_processor(images=_lowercase   ,    return_tensors='pt'			).pixel_values

		logger.info(f"""Converting model {model_name}..."""			)

		# load original state dict
		if encoder_only:
				lowercase__					:      List[Any]      =		torch.load(_lowercase   ,    map_location=torch.device('cpu'			)			)
		else:
				lowercase__					:      List[str]      =		torch.load(_lowercase   ,    map_location=torch.device('cpu'			)			)['state_dict']

		# rename keys
		lowercase__					:      int      =		rename_keys(_lowercase   ,    encoder_only=_lowercase			)
		if not encoder_only:
				del state_dict["decode_head.conv_seg.weight"]
				del state_dict["decode_head.conv_seg.bias"]

		# key and value matrices need special treatment
		read_in_k_v(_lowercase   ,    _lowercase			)

		# create HuggingFace model and load state dict
		if encoder_only:
				lowercase__					:      Any      =		False
				lowercase__					:      List[str]      =		SegformerForImageClassification(_lowercase			)
		else:
				lowercase__					:      Optional[Any]      =		SegformerForSemanticSegmentation(_lowercase			)
		model.load_state_dict(_lowercase			)
		model.eval()

		# forward pass
		lowercase__					:      List[str]      =		model(_lowercase			)
		lowercase__					:      int      =		outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				lowercase__					:      Dict      =		torch.tensor(
				    [
				        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
				        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
				        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
				    ]			)
		elif model_name == "segformer.b1.512x512.ade.160k":
				lowercase__					:      List[Any]      =		torch.tensor(
				    [
				        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
				        [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
				        [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
				    ]			)
		elif model_name == "segformer.b2.512x512.ade.160k":
				lowercase__					:      List[str]      =		torch.tensor(
				    [
				        [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
				        [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
				        [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
				    ]			)
		elif model_name == "segformer.b3.512x512.ade.160k":
				lowercase__					:      Tuple      =		torch.tensor(
				    [
				        [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
				        [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
				        [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
				    ]			)
		elif model_name == "segformer.b4.512x512.ade.160k":
				lowercase__					:      str      =		torch.tensor(
				    [
				        [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
				        [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
				        [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
				    ]			)
		elif model_name == "segformer.b5.640x640.ade.160k":
				lowercase__					:      Tuple      =		torch.tensor(
				    [
				        [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
				        [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
				        [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
				    ]			)
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				lowercase__					:      int      =		torch.tensor(
				    [
				        [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
				        [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
				        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
				    ]			)
		elif model_name == "segformer.b0.512x1024.city.160k":
				lowercase__					:      Optional[int]      =		torch.tensor(
				    [
				        [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
				        [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
				        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
				    ]			)
		elif model_name == "segformer.b0.640x1280.city.160k":
				lowercase__					:      List[str]      =		torch.tensor(
				    [
				        [
				            [-1.13_72e01, -1.27_87e01, -1.34_77e01],
				            [-1.25_36e01, -1.41_94e01, -1.44_09e01],
				            [-1.32_17e01, -1.48_88e01, -1.53_27e01],
				        ],
				        [
				            [-1.47_91e01, -1.71_22e01, -1.82_77e01],
				            [-1.71_63e01, -1.91_92e01, -1.95_33e01],
				            [-1.78_97e01, -1.99_91e01, -2.03_15e01],
				        ],
				        [
				            [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
				            [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
				            [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
				        ],
				    ]			)
		elif model_name == "segformer.b0.768x768.city.160k":
				lowercase__					:      Tuple      =		torch.tensor(
				    [
				        [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
				        [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
				        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
				    ]			)
		elif model_name == "segformer.b1.1024x1024.city.160k":
				lowercase__					:      Union[str, Any]      =		torch.tensor(
				    [
				        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
				        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
				        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
				    ]			)
		elif model_name == "segformer.b2.1024x1024.city.160k":
				lowercase__					:      Dict      =		torch.tensor(
				    [
				        [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
				        [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
				        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
				    ]			)
		elif model_name == "segformer.b3.1024x1024.city.160k":
				lowercase__					:      Any      =		torch.tensor(
				    [
				        [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
				        [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
				        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
				    ]			)
		elif model_name == "segformer.b4.1024x1024.city.160k":
				lowercase__					:      List[str]      =		torch.tensor(
				    [
				        [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
				        [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
				        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
				    ]			)
		elif model_name == "segformer.b5.1024x1024.city.160k":
				lowercase__					:      Union[str, Any]      =		torch.tensor(
				    [
				        [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
				        [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
				        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
				    ]			)
		else:
				lowercase__					:      List[str]      =		logits.argmax(-1			).item()
				print('Predicted class:'   ,    model.config.idalabel[predicted_class_idx]			)

		# verify logits
		if not encoder_only:
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3, :3, :3]   ,    _lowercase   ,    atol=1e-2			)

		# finally, save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""			)
		Path(_lowercase			).mkdir(exist_ok=_lowercase			)
		model.save_pretrained(_lowercase			)
		image_processor.save_pretrained(_lowercase			)


if __name__ == "__main__":
			__UpperCamelCase:	Any = argparse.ArgumentParser()

			parser.add_argument(
			    """--model_name""",
			    default="""segformer.b0.512x512.ade.160k""",
			    type=str,
			    help="""Name of the model you'd like to convert.""",
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
			)
			__UpperCamelCase:	int = parser.parse_args()
			convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				(EulerDiscreteScheduler,)
	_A													=				10

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						Optional[Any]			):
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],					[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
					self.check_over_configs(beta_start=lowerCamelCase_,					beta_end=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Any      =		torch.manual_seed(0			)

			lowercase__					:      int      =		self.dummy_model()
			lowercase__					:      Tuple      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Union[str, Any]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      Dict      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Any      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      str      =		self.get_scheduler_config(prediction_type='v_prediction'			)
			lowercase__					:      Optional[int]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Optional[int]      =		torch.manual_seed(0			)

			lowercase__					:      str      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Optional[int]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      int      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Dict      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
			assert abs(result_mean.item() - 2.2676E-06			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Dict      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      Dict      =		torch.manual_seed(0			)

			lowercase__					:      Union[str, Any]      =		self.dummy_model()
			lowercase__					:      Any      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[Any]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Optional[int]      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      List[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		output.prev_sample

			lowercase__					:      Union[str, Any]      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Union[str, Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_,					use_karras_sigmas=lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      int      =		torch.manual_seed(0			)

			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      str      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[str]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Any      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		output.prev_sample

			lowercase__					:      int      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1E-2
			assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 1E-3


