
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowercase			:  Tuple   							=	[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

_lowercase			:  Dict   							=	[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

_lowercase			:  List[str]   							=	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														_lowercase			:  Optional[Any]   							=	f"""down_blocks.{i}.resnets.{j}."""
														_lowercase			:  str   							=	f"""input_blocks.{3*i + j + 1}.0."""
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					_lowercase			:  str   							=	f"""down_blocks.{i}.attentions.{j}."""
																					_lowercase			:  Any   							=	f"""input_blocks.{3*i + j + 1}.1."""
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														_lowercase			:  str   							=	f"""up_blocks.{i}.resnets.{j}."""
														_lowercase			:  Any   							=	f"""output_blocks.{3*i + j}.0."""
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					_lowercase			:  Optional[Any]   							=	f"""up_blocks.{i}.attentions.{j}."""
																					_lowercase			:  Any   							=	f"""output_blocks.{3*i + j}.1."""
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														_lowercase			:  Optional[Any]   							=	f"""down_blocks.{i}.downsamplers.0.conv."""
														_lowercase			:  Dict   							=	f"""input_blocks.{3*(i+1)}.0.op."""
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														_lowercase			:  Optional[Any]   							=	f"""up_blocks.{i}.upsamplers.0."""
														_lowercase			:  Dict   							=	f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowercase			:  str   							=	"mid_block.attentions.0."
_lowercase			:  Dict   							=	"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							_lowercase			:  Optional[int]   							=	f"""mid_block.resnets.{j}."""
							_lowercase			:  str   							=	f"""middle_block.{2*j}."""
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						lowercase_     :					int													=	{k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												lowercase_     :					int													=	sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								lowercase_     :					int													=	v.replace(lowerCamelCase__				,				lowerCamelCase__							)
																		lowercase_     :					List[Any]													=	v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		lowercase_     :					int													=	v.replace(lowerCamelCase__				,				lowerCamelCase__							)
												lowercase_     :					List[Any]													=	v
						lowercase_     :					List[Any]													=	{v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowercase			:  Optional[Any]   							=	[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														_lowercase			:  List[str]   							=	f"""encoder.down_blocks.{i}.resnets.{j}."""
														_lowercase			:  int   							=	f"""encoder.down.{i}.block.{j}."""
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														_lowercase			:  List[str]   							=	f"""down_blocks.{i}.downsamplers.0."""
														_lowercase			:  Union[str, Any]   							=	f"""down.{i}.downsample."""
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														_lowercase			:  Dict   							=	f"""up_blocks.{i}.upsamplers.0."""
														_lowercase			:  Dict   							=	f"""up.{3-i}.upsample."""
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														_lowercase			:  Dict   							=	f"""decoder.up_blocks.{i}.resnets.{j}."""
														_lowercase			:  Tuple   							=	f"""decoder.up.{3-i}.block.{j}."""
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							_lowercase			:  Tuple   							=	f"""mid_block.resnets.{i}."""
							_lowercase			:  int   							=	f"""mid.block_{i+1}."""
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowercase			:  Union[str, Any]   							=	[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





						return w.reshape(*w.shape				,				1				,				1							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	{k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		lowercase_     :					str													=	v.replace(lowerCamelCase__				,				lowerCamelCase__							)
												lowercase_     :					Tuple													=	v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								lowercase_     :					Any													=	v.replace(lowerCamelCase__				,				lowerCamelCase__							)
																		lowercase_     :					Any													=	v
						lowercase_     :					Union[str, Any]													=	{v: vae_state_dict[k] for k, v in mapping.items()}
						lowercase_     :					List[str]													=	['''q''', '''k''', '''v''', '''proj_out''']
						for k, v in new_state_dict.items():
												for weight_name in weights_to_convert:
																		if F'''mid.attn_1.{weight_name}.weight''' in k:
																								print(F'''Reshaping {k} for SD format'''							)
																								lowercase_     :					Tuple													=	reshape_weight_for_sd(lowerCamelCase__							)
						return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowercase			:  List[str]   							=	[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
_lowercase			:  Any   							=	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowercase			:  Optional[int]   							=	re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowercase			:  Optional[Any]   							=	{"q": 0, "k": 1, "v": 2}
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	{}
						lowercase_     :					List[str]													=	{}
						lowercase_     :					str													=	{}
						for k, v in text_enc_dict.items():
												if (
												    k.endswith('''.self_attn.q_proj.weight'''							)
												    or k.endswith('''.self_attn.k_proj.weight'''							)
												    or k.endswith('''.self_attn.v_proj.weight'''							)
												):
																		lowercase_     :					List[Any]													=	k[: -len('''.q_proj.weight'''							)]
																		lowercase_     :					str													=	k[-len('''q_proj.weight'''							)]
																		if k_pre not in capture_qkv_weight:
																								lowercase_     :					Dict													=	[None, None, None]
																		lowercase_     :					List[Any]													=	v
																		continue

												if (
												    k.endswith('''.self_attn.q_proj.bias'''							)
												    or k.endswith('''.self_attn.k_proj.bias'''							)
												    or k.endswith('''.self_attn.v_proj.bias'''							)
												):
																		lowercase_     :					List[Any]													=	k[: -len('''.q_proj.bias'''							)]
																		lowercase_     :					List[Any]													=	k[-len('''q_proj.bias'''							)]
																		if k_pre not in capture_qkv_bias:
																								lowercase_     :					Tuple													=	[None, None, None]
																		lowercase_     :					List[str]													=	v
																		continue

												lowercase_     :					Optional[int]													=	textenc_pattern.sub(lambda __SCREAMING_SNAKE_CASE							: protected[re.escape(m.group(0							)							)]				,				lowerCamelCase__							)
												lowercase_     :					Tuple													=	v

						for k_pre, tensors in capture_qkv_weight.items():
												if None in tensors:
																		raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''							)
												lowercase_     :					Union[str, Any]													=	textenc_pattern.sub(lambda __SCREAMING_SNAKE_CASE							: protected[re.escape(m.group(0							)							)]				,				lowerCamelCase__							)
												lowercase_     :					Dict													=	torch.cat(lowerCamelCase__							)

						for k_pre, tensors in capture_qkv_bias.items():
												if None in tensors:
																		raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''							)
												lowercase_     :					List[str]													=	textenc_pattern.sub(lambda __SCREAMING_SNAKE_CASE							: protected[re.escape(m.group(0							)							)]				,				lowerCamelCase__							)
												lowercase_     :					List[Any]													=	torch.cat(lowerCamelCase__							)

						return new_state_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





						return text_enc_dict


if __name__ == "__main__":
							_lowercase			:  Optional[Any]   							=	argparse.ArgumentParser()

							parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
							parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
							parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
							parser.add_argument(
							    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
							)

							_lowercase			:  Dict   							=	parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							_lowercase			:  List[str]   							=	osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
							_lowercase			:  str   							=	osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
							_lowercase			:  Dict   							=	osp.join(args.model_path, "text_encoder", "model.safetensors")

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														_lowercase			:  int   							=	load_file(unet_path, device="cpu")
							else:
														_lowercase			:  Union[str, Any]   							=	osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
														_lowercase			:  Tuple   							=	torch.load(unet_path, map_location="cpu")

							if osp.exists(vae_path):
														_lowercase			:  Tuple   							=	load_file(vae_path, device="cpu")
							else:
														_lowercase			:  str   							=	osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
														_lowercase			:  Any   							=	torch.load(vae_path, map_location="cpu")

							if osp.exists(text_enc_path):
														_lowercase			:  Optional[int]   							=	load_file(text_enc_path, device="cpu")
							else:
														_lowercase			:  int   							=	osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
														_lowercase			:  Optional[Any]   							=	torch.load(text_enc_path, map_location="cpu")

							# Convert the UNet model
							_lowercase			:  List[Any]   							=	convert_unet_state_dict(unet_state_dict)
							_lowercase			:  List[str]   							=	{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							_lowercase			:  List[Any]   							=	convert_vae_state_dict(vae_state_dict)
							_lowercase			:  Union[str, Any]   							=	{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							_lowercase			:  str   							=	"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														_lowercase			:  str   							=	{"transformer." + k: v for k, v in text_enc_dict.items()}
														_lowercase			:  Union[str, Any]   							=	convert_text_enc_state_dict_vaa(text_enc_dict)
														_lowercase			:  Optional[Any]   							=	{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
							else:
														_lowercase			:  Dict   							=	convert_text_enc_state_dict(text_enc_dict)
														_lowercase			:  Dict   							=	{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							_lowercase			:  List[Any]   							=	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														_lowercase			:  str   							=	{k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														_lowercase			:  Any   							=	{"state_dict": state_dict}
														torch.save(state_dict, args.checkpoint_path)





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					int													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''`custom_timesteps` must be in descending order.'''		):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
																	scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __SCREAMING_SNAKE_CASE    ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,							):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)



