





"""simple docstring"""


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  (CMStochasticIterativeScheduler,)
			__lowerCAmelCase     :				Dict										=  10







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			{
									    """num_train_timesteps""": 2_0_1,
									    """sigma_min""": 0.0_02,
									    """sigma_max""": 80.0,
									}

									config.update(**_A    )
									return config







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			1_0

									UpperCamelCase				:		Union[str, Any]				   =			self.get_scheduler_config()
									UpperCamelCase				:		int				   =			self.scheduler_classes[0](**_A    )

									scheduler.set_timesteps(_A    )

									UpperCamelCase				:		Optional[int]				   =			scheduler.timesteps[0]
									UpperCamelCase				:		List[str]				   =			scheduler.timesteps[1]

									UpperCamelCase				:		str				   =			self.dummy_sample
									UpperCamelCase				:		int				   =			0.1 * sample

									UpperCamelCase				:		str				   =			scheduler.step(_A						,   _A						,   _A    ).prev_sample
									UpperCamelCase				:		Optional[Any]				   =			scheduler.step(_A						,   _A						,   _A    ).prev_sample

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







			def   _a      (      self    ):






									'''simple docstring'''







									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.scheduler_classes[0]
									UpperCamelCase				:		List[str]				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[str]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		str				   =			1
									scheduler.set_timesteps(_A    )
									UpperCamelCase				:		Optional[int]				   =			scheduler.timesteps

									UpperCamelCase				:		Any				   =			torch.manual_seed(0    )

									UpperCamelCase				:		Union[str, Any]				   =			self.dummy_model()
									UpperCamelCase				:		Dict				   =			self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(_A    ):
															# 1. scale model input
															UpperCamelCase				:		Any				   =			scheduler.scale_model_input(_A						,   _A    )

															# 2. predict noise residual
															UpperCamelCase				:		List[str]				   =			model(_A						,   _A    )

															# 3. predict previous sample x_t-1
															UpperCamelCase				:		Optional[int]				   =			scheduler.step(_A						,   _A						,   _A						,   generator=_A    ).prev_sample

															UpperCamelCase				:		Tuple				   =			pred_prev_sample

									UpperCamelCase				:		Tuple				   =			torch.sum(torch.abs(_A    )    )
									UpperCamelCase				:		Optional[int]				   =			torch.mean(torch.abs(_A    )    )

									assert abs(result_sum.item() - 1_92.76_14    ) < 1e-2
									assert abs(result_mean.item() - 0.25_10    ) < 1e-3







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.scheduler_classes[0]
									UpperCamelCase				:		int				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[str]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		int				   =			[1_0_6, 0]
									scheduler.set_timesteps(timesteps=_A    )
									UpperCamelCase				:		int				   =			scheduler.timesteps

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

									UpperCamelCase				:		List[str]				   =			self.dummy_model()
									UpperCamelCase				:		Union[str, Any]				   =			self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															UpperCamelCase				:		int				   =			scheduler.scale_model_input(_A						,   _A    )

															# 2. predict noise residual
															UpperCamelCase				:		int				   =			model(_A						,   _A    )

															# 3. predict previous sample x_t-1
															UpperCamelCase				:		str				   =			scheduler.step(_A						,   _A						,   _A						,   generator=_A    ).prev_sample

															UpperCamelCase				:		int				   =			pred_prev_sample

									UpperCamelCase				:		Union[str, Any]				   =			torch.sum(torch.abs(_A    )    )
									UpperCamelCase				:		Dict				   =			torch.mean(torch.abs(_A    )    )

									assert abs(result_sum.item() - 3_47.63_57    ) < 1e-2
									assert abs(result_mean.item() - 0.45_27    ) < 1e-3







			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		List[Any]				   =			[3_9, 3_0, 1_2, 1_5, 0]

									with self.assertRaises(_A						,   msg="""`timesteps` must be in descending order."""    ):
															scheduler.set_timesteps(timesteps=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.scheduler_classes[0]
									UpperCamelCase				:		List[str]				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[Any]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		Any				   =			[3_9, 3_0, 1_2, 1, 0]
									UpperCamelCase				:		Union[str, Any]				   =			len(_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.scheduler_classes[0]
									UpperCamelCase				:		Any				   =			self.get_scheduler_config()
									UpperCamelCase				:		int				   =			scheduler_class(**_A    )

									UpperCamelCase				:		Optional[int]				   =			[scheduler.config.num_train_timesteps]

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










'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :Tuple		,			lowercase_ :int = 3		,			lowercase_ :int = 3		,			lowercase_ :Tuple[str] = ("DownEncoderBlock2D",)		,			lowercase_ :Tuple[str] = ("UpDecoderBlock2D",)		,			lowercase_ :Tuple[int] = (64,)		,			lowercase_ :int = 1		,			lowercase_ :str = "silu"		,			lowercase_ :int = 3		,			lowercase_ :int = 32		,			lowercase_ :int = 2_56		,			lowercase_ :int = 32		,			lowercase_ :Optional[int] = None		,			lowercase_ :float = 0.1_8_2_1_5		,			lowercase_ :str = "group"		,			)->				Optional[int]:
     super().__init__()

     # pass init params to Encoder
     A__							=     Encoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			down_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			double_z=lowercase_		,			)

     A__							=     vq_embed_dim if vq_embed_dim is not None else latent_channels

     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)
     A__							=     VectorQuantizer(lowercase_		,			lowercase_		,			beta=0.2_5		,			remap=lowercase_		,			sane_index_shape=lowercase_				)
     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)

     # pass init params to Decoder
     A__							=     Decoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			up_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			norm_type=lowercase_		,			)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				VQEncoderOutput:
     A__							=     self.encoder(lowercase_				)
     A__							=     self.quant_conv(lowercase_				)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=lowercase_				)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = False		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         A__,      A__,      A__							=     self.quantize(lowercase_				)
     else:
         A__							=     h
     A__							=     self.post_quant_conv(lowercase_				)
     A__							=     self.decoder(lowercase_		,			quant if self.config.norm_type == "spatial" else None				)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     A__							=     sample
     A__							=     self.encode(lowercase_				).latents
     A__							=     self.decode(lowercase_				).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



