




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger(__name__)


UpperCAmelCase__				     = "https://openaipublic.azureedge.net/jukebox/models/"
UpperCAmelCase__				     = {
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}







def 		_A(       UpperCamelCase__							:			List[Any]				)    -> Optional[Any]:
							'''simple docstring'''


							if key.endswith('''.model.1.bias'''				) and len(key.split('''.'''				)				) > 10:
														__lowercase      =    key.replace('''.model.1.bias'''				,       '''.conv1d_1.bias'''				)
							elif key.endswith('''.model.1.weight'''				) and len(key.split('''.'''				)				) > 10:
														__lowercase      =    key.replace('''.model.1.weight'''				,       '''.conv1d_1.weight'''				)
							elif key.endswith('''.model.3.bias'''				) and len(key.split('''.'''				)				) > 10:
														__lowercase      =    key.replace('''.model.3.bias'''				,       '''.conv1d_2.bias'''				)
							elif key.endswith('''.model.3.weight'''				) and len(key.split('''.'''				)				) > 10:
														__lowercase      =    key.replace('''.model.3.weight'''				,       '''.conv1d_2.weight'''				)

							if "conditioner_blocks.0." in key:
														__lowercase      =    key.replace('''conditioner_blocks.0'''				,       '''conditioner_blocks'''				)

							if "prime_prior" in key:
														__lowercase      =    key.replace('''prime_prior'''				,       '''encoder'''				)

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														__lowercase      =    key.replace('''.emb.'''				,       '''.'''				)

							if key.endswith('''k'''				):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace('''.k'''				,       '''.codebook'''				)
							if "y_emb." in key:
														return key.replace('''y_emb.'''				,       '''metadata_embedding.'''				)

							if "x_emb.emb." in key:
														__lowercase      =    key.replace('''0.x_emb.emb'''				,       '''embed_tokens'''				)

							if "prime_state_ln" in key:
														return key.replace('''prime_state_ln'''				,       '''encoder.final_layer_norm'''				)
							if ".ln" in key:
														return key.replace('''.ln'''				,       '''.layer_norm'''				)
							if "_ln" in key:
														return key.replace('''_ln'''				,       '''_layer_norm'''				)

							if "prime_state_proj" in key:
														return key.replace('''prime_state_proj'''				,       '''encoder.proj_in'''				)
							if "prime_x_out" in key:
														return key.replace('''prime_x_out'''				,       '''encoder.lm_head'''				)
							if "prior.x_out" in key:
														return key.replace('''x_out'''				,       '''fc_proj_out'''				)
							if "x_emb" in key:
														return key.replace('''x_emb'''				,       '''embed_tokens'''				)

							return key







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			List[str]				)    -> int:
							'''simple docstring'''


							__lowercase      =    {}
							import re

							__lowercase      =    re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''				)
							__lowercase      =    re.compile(
							    r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
							__lowercase      =    re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''				)

							__lowercase      =    re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''				)
							__lowercase      =    re.compile(
							    r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
							__lowercase      =    re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''				)

							__lowercase      =    re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''				)
							__lowercase      =    re.compile(
							    r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
							__lowercase      =    re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''				)

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_encoder_block_conv_in.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    int(groups[2]				) * 2 + int(groups[3]				)
																					__lowercase      =    F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
																					__lowercase      =    re_encoder_block_conv_in.sub(UpperCamelCase__				,       UpperCamelCase__				)

														elif re_encoder_block_resnet.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_encoder_block_resnet.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    int(groups[2]				) * 2 + int(groups[3]				)
																					__lowercase      =    {'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowercase      =    F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
																					__lowercase      =    F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowercase      =    prefix + resnet_block
																					__lowercase      =    re_encoder_block_resnet.sub(UpperCamelCase__				,       UpperCamelCase__				)

														elif re_encoder_block_proj_out.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_encoder_block_proj_out.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
																					__lowercase      =    re_encoder_block_proj_out.sub(UpperCamelCase__				,       UpperCamelCase__				)

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_decoder_block_conv_out.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    int(groups[2]				) * 2 + int(groups[3]				) - 2
																					__lowercase      =    F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
																					__lowercase      =    re_decoder_block_conv_out.sub(UpperCamelCase__				,       UpperCamelCase__				)

														elif re_decoder_block_resnet.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_decoder_block_resnet.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    int(groups[2]				) * 2 + int(groups[3]				) - 2
																					__lowercase      =    {'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowercase      =    F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
																					__lowercase      =    F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowercase      =    prefix + resnet_block
																					__lowercase      =    re_decoder_block_resnet.sub(UpperCamelCase__				,       UpperCamelCase__				)

														elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_decoder_block_proj_in.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
																					__lowercase      =    re_decoder_block_proj_in.sub(UpperCamelCase__				,       UpperCamelCase__				)

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_prior_cond_conv_out.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    int(groups[1]				) * 2 + int(groups[2]				) - 2
																					__lowercase      =    F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
																					__lowercase      =    re_prior_cond_conv_out.sub(UpperCamelCase__				,       UpperCamelCase__				)

														elif re_prior_cond_resnet.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_prior_cond_resnet.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    int(groups[1]				) * 2 + int(groups[2]				) - 2
																					__lowercase      =    {'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowercase      =    F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
																					__lowercase      =    F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowercase      =    prefix + resnet_block
																					__lowercase      =    re_prior_cond_resnet.sub(UpperCamelCase__				,       UpperCamelCase__				)

														elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__				):
																					__lowercase      =    re_prior_cond_proj_in.match(UpperCamelCase__				)
																					__lowercase      =    regex_match.groups()
																					__lowercase      =    F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
																					__lowercase      =    re_prior_cond_proj_in.sub(UpperCamelCase__				,       UpperCamelCase__				)

														# keep original key
														else:
																					__lowercase      =    original_key

														__lowercase      =    replace_key(UpperCamelCase__				)

														if F'{key_prefix}.{key}' not in model_state_dict or key is None:
																					print(F'failed converting {original_key} to {key}, does not match'				)

														# handle missmatched shape
														elif value.shape != model_state_dict[F'{key_prefix}.{key}'].shape:
																					__lowercase      =    model_state_dict[F'{key_prefix}.{key}']
																					print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'				)
																					__lowercase      =    original_key

														__lowercase      =    original_key
														__lowercase      =    value

							return new_dict







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			List[str]=None				,       UpperCamelCase__							:			int=None				)    -> Optional[Any]:
							'''simple docstring'''


							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'				):
																					__lowercase      =    requests.get(F'{PREFIX}{file}'				,       allow_redirects=UpperCamelCase__				)
																					os.makedirs(F'{pytorch_dump_folder_path}/'				,       exist_ok=UpperCamelCase__				)
																					open(F'{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'				,       '''wb'''				).write(r.content				)

							__lowercase      =    MODEL_MAPPING[model_name.split('''/'''				)[-1]]

							__lowercase      =    JukeboxConfig.from_pretrained(UpperCamelCase__				)
							__lowercase      =    JukeboxModel(UpperCamelCase__				)

							__lowercase      =    []
							__lowercase      =    {}
							for i, dict_name in enumerate(UpperCamelCase__				):
														__lowercase      =    torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/"				)[-1]}'				)['''model''']

														__lowercase      =    {}
														for k in old_dic.keys():
																					if k.endswith('''.b'''				):
																												__lowercase      =    old_dic[k]
																					elif k.endswith('''.w'''				):
																												__lowercase      =    old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												__lowercase      =    old_dic[k]
																					else:
																												__lowercase      =    old_dic[k]

														__lowercase      =    '''vqvae''' if i == 0 else F'priors.{3 - i}'
														__lowercase      =    fix_jukebox_keys(UpperCamelCase__				,       model.state_dict()				,       UpperCamelCase__				,       UpperCamelCase__				)
														weight_dict.append(UpperCamelCase__				)

							__lowercase      =    weight_dict.pop(0				)
							model.vqvae.load_state_dict(UpperCamelCase__				)
							for i in range(len(UpperCamelCase__				)				):
														model.priors[i].load_state_dict(weight_dict[2 - i]				)

							Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
							with open(F'{pytorch_dump_folder_path}/mapping.json'				,       '''w'''				) as txtfile:
														json.dump(UpperCamelCase__				,       UpperCamelCase__				)

							print(F'Saving model {model_name} to {pytorch_dump_folder_path}'				)
							model.save_pretrained(UpperCamelCase__				)

							return weight_dict


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="jukebox-5b-lyrics",
			    type=str,
			    help="Name of the model you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="jukebox-5b-lyrics-converted",
			    type=str,
			    help="Path to the output PyTorch model directory.",
			)
			UpperCAmelCase__				     = parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		(DEISMultistepScheduler,)
						UpperCamelCase_	:       Any       	=		(('num_inference_steps', 25),)






						def     UpperCAmelCase_      ( self :     List[str]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {
													    '''num_train_timesteps''': 1_000,
													    '''beta_start''': 0.0_0_0_1,
													    '''beta_end''': 0.0_2,
													    '''beta_schedule''': '''linear''',
													    '''solver_order''': 2,
													}

													config.update(**lowerCamelCase__				)
													return config






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]=0				,							**lowerCamelCase__ :     Dict				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)
																				# copy over dummy past residuals
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCamelCase__				)
																											__lowercase      =    scheduler_class.from_pretrained(lowerCamelCase__				)
																											new_scheduler.set_timesteps(lowerCamelCase__				)
																											# copy over dummy past residuals
																											__lowercase      =    dummy_past_residuals[: new_scheduler.config.solver_order]

																				__lowercase     ,     __lowercase      =    sample, sample
																				for t in range(lowerCamelCase__				,							time_step + scheduler.config.solver_order + 1				):
																											__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																											__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






						def     UpperCAmelCase_      ( self :     Any				)    ->						Tuple:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Any=0				,							**lowerCamelCase__ :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)

																				# copy over dummy past residuals (must be after setting timesteps)
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCamelCase__				)
																											__lowercase      =    scheduler_class.from_pretrained(lowerCamelCase__				)
																											# copy over dummy past residuals
																											new_scheduler.set_timesteps(lowerCamelCase__				)

																											# copy over dummy past residual (must be after setting timesteps)
																											__lowercase      =    dummy_past_residuals[: new_scheduler.config.solver_order]

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																				assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     Optional[int]=None				,							**lowerCamelCase__ :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													if scheduler is None:
																				__lowercase      =    self.scheduler_classes[0]
																				__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter
													scheduler.set_timesteps(lowerCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													return sample






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)

													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

																				__lowercase      =    self.dummy_sample
																				__lowercase      =    0.1 * sample

																				if num_inference_steps is not None and hasattr(lowerCamelCase__				,							'''set_timesteps'''				):
																											scheduler.set_timesteps(lowerCamelCase__				)
																				elif num_inference_steps is not None and not hasattr(lowerCamelCase__				,							'''set_timesteps'''				):
																											__lowercase      =    num_inference_steps

																				# copy over dummy past residuals (must be done after set_timesteps)
																				__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				__lowercase      =    scheduler.timesteps[5]
																				__lowercase      =    scheduler.timesteps[6]

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																				self.assertEqual(output_a.shape				,							sample.shape				)
																				self.assertEqual(output_a.shape				,							output_a.shape				)






						def     UpperCAmelCase_      ( self :     str				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    DEISMultistepScheduler(**self.get_scheduler_config()				)
													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

													__lowercase      =    DPMSolverSinglestepScheduler.from_config(scheduler.config				)
													__lowercase      =    DPMSolverMultistepScheduler.from_config(scheduler.config				)
													__lowercase      =    UniPCMultistepScheduler.from_config(scheduler.config				)
													__lowercase      =    DEISMultistepScheduler.from_config(scheduler.config				)

													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     int				)    ->						List[Any]:
													"""simple docstring"""


													for timesteps in [25, 50, 100, 999, 1_000]:
																				self.check_over_configs(num_train_timesteps=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Any:
													"""simple docstring"""


													self.check_over_configs(thresholding=lowerCamelCase__				)
													for order in [1, 2, 3]:
																				for solver_type in ["logrho"]:
																											for threshold in [0.5, 1.0, 2.0]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    thresholding=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							sample_max_value=lowerCamelCase__				,							algorithm_type='''deis'''				,							solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													for algorithm_type in ["deis"]:
																				for solver_type in ["logrho"]:
																											for order in [1, 2, 3]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									__lowercase      =    self.full_loop(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									assert not torch.isnan(lowerCamelCase__				).any(), "Samples have nan numbers"






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


													self.check_over_configs(lower_order_final=lowerCamelCase__				)
													self.check_over_configs(lower_order_final=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																				self.check_over_forward(num_inference_steps=lowerCamelCase__				,							time_step=0				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop()
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop(prediction_type='''v_prediction'''				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.0_9_1				) < 1e-3






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(thresholding=lowerCamelCase__				,							dynamic_thresholding_ratio=0				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter.half()
													scheduler.set_timesteps(lowerCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													assert sample.dtype == torch.floataa






