

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       (UnCLIPScheduler,)


							def 							__lowercase							(     self			,				**_a			)       ->       Optional[Any]:
												_a      :       Tuple   =						{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''variance_type''': '''fixed_small_log''',
												    '''clip_sample''': True,
												    '''clip_sample_range''': 1.0,
												    '''prediction_type''': '''epsilon''',
												}

												config.update(**_a			)
												return config


							def 							__lowercase							(     self			)       ->       List[str]:
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												for variance in ["fixed_small_log", "learned_range"]:
																	self.check_over_configs(variance_type=_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a			)


							def 							__lowercase							(     self			)       ->       List[str]:
												for clip_sample_range in [1, 5, 1_0, 2_0]:
																	self.check_over_configs(clip_sample_range=_a			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												for prediction_type in ["epsilon", "sample"]:
																	self.check_over_configs(prediction_type=_a			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for time_step in [0, 5_0_0, 9_9_9]:
																	for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																						self.check_over_forward(time_step=_a			,				prev_timestep=_a			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Optional[int]   =						self.scheduler_classes[0]
												_a      :       str   =						self.get_scheduler_config(variance_type='''fixed_small_log'''			)
												_a      :       List[str]   =						scheduler_class(**_a			)

												assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.054_9625			)			) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.999_4987			)			) < 1e-5


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.scheduler_classes[0]
												_a      :       List[str]   =						self.get_scheduler_config(variance_type='''learned_range'''			)
												_a      :       Tuple   =						scheduler_class(**_a			)

												_a      :       List[str]   =						0.5

												assert scheduler._get_variance(1			,				predicted_variance=_a			) - -10.171_2790 < 1e-5
												assert scheduler._get_variance(4_8_7			,				predicted_variance=_a			) - -5.799_8052 < 1e-5
												assert scheduler._get_variance(9_9_9			,				predicted_variance=_a			) - -0.001_0011 < 1e-5


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Union[str, Any]   =						self.scheduler_classes[0]
												_a      :       int   =						self.get_scheduler_config()
												_a      :       List[Any]   =						scheduler_class(**_a			)

												_a      :       List[Any]   =						scheduler.timesteps

												_a      :       Tuple   =						self.dummy_model()
												_a      :       List[str]   =						self.dummy_sample_deter
												_a      :       Union[str, Any]   =						torch.manual_seed(0			)

												for i, t in enumerate(_a			):
																	# 1. predict noise residual
																	_a      :       Union[str, Any]   =						model(_a			,				_a			)

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

																	_a      :       Tuple   =						pred_prev_sample

												_a      :       int   =						torch.sum(torch.abs(_a			)			)
												_a      :       Dict   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_sum.item() - 252.268_2495			) < 1e-2
												assert abs(result_mean.item() - 0.328_4743			) < 1e-3


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.scheduler_classes[0]
												_a      :       Any   =						self.get_scheduler_config()
												_a      :       Tuple   =						scheduler_class(**_a			)

												scheduler.set_timesteps(2_5			)

												_a      :       Optional[int]   =						scheduler.timesteps

												_a      :       Dict   =						self.dummy_model()
												_a      :       Tuple   =						self.dummy_sample_deter
												_a      :       Dict   =						torch.manual_seed(0			)

												for i, t in enumerate(_a			):
																	# 1. predict noise residual
																	_a      :       Dict   =						model(_a			,				_a			)

																	if i + 1 == timesteps.shape[0]:
																						_a      :       Tuple   =						None
																	else:
																						_a      :       Union[str, Any]   =						timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	_a      :       str   =						scheduler.step(
																	    _a			,				_a			,				_a			,				prev_timestep=_a			,				generator=_a			).prev_sample

																	_a      :       Tuple   =						pred_prev_sample

												_a      :       Any   =						torch.sum(torch.abs(_a			)			)
												_a      :       str   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_sum.item() - 258.204_4983			) < 1e-2
												assert abs(result_mean.item() - 0.336_2038			) < 1e-3


							def 							__lowercase							(     self			)       ->       List[Any]:
												pass




							def 							__lowercase							(     self			)       ->       List[Any]:
												pass






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			parser.add_argument(
			    '''--original_config_file''',
			    type=str,
			    required=True,
			    help='''The YAML config file corresponding to the original architecture.''',
			)
			parser.add_argument(
			    '''--num_in_channels''',
			    default=None,
			    type=int,
			    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=512,
			    type=int,
			    help=(
			        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
			        ''' Base. Use 768 for Stable Diffusion v2.'''
			    ),
			)
			parser.add_argument(
			    '''--extract_ema''',
			    action='''store_true''',
			    help=(
			        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
			        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
			        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
			    ),
			)
			parser.add_argument(
			    '''--upcast_attention''',
			    action='''store_true''',
			    help=(
			        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
			        ''' diffusion 2.1.'''
			    ),
			)
			parser.add_argument(
			    '''--from_safetensors''',
			    action='''store_true''',
			    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
			)
			parser.add_argument(
			    '''--to_safetensors''',
			    action='''store_true''',
			    help='''Whether to store pipeline in safetensors format or not.''',
			)
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
			parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



			def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								if string == "True":
													return True
								elif string == "False":
													return False
								else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

			parser.add_argument(
			    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
			)

			parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

			a__												=						parser.parse_args()

			a__												=						download_controlnet_from_original_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    extract_ema=args.extract_ema,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    use_linear_projection=args.use_linear_projection,
			    cross_attention_dim=args.cross_attention_dim,
			)







			controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



