






"""simple docstring"""

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

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 						A_							(      _lowerCamelCase  ):
				UpperCAmelCase__        =				4_2
				UpperCAmelCase__        =				None






def _lowerCAmelCase	(  __lowerCamelCase:Tuple ,						__lowerCamelCase:str=0.9_9_9 ,						__lowerCamelCase:List[str]="cosine" ,						):

						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__lowerCamelCase:Union[str, Any]   ):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__lowerCamelCase:Optional[Any]   ):
																		return math.exp(t * -1_2.0   )

						else:
												raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

						__magic_name__            =     []
						for i in range(SCREAMING_SNAKE_CASE_   ):
												__magic_name__            =     i / num_diffusion_timesteps
												__magic_name__            =     (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_   ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_   ) ,						SCREAMING_SNAKE_CASE_   )   )
						return torch.tensor(SCREAMING_SNAKE_CASE_ ,						dtype=torch.floataa   )


class 						A_							(      _lowerCamelCase					,							_lowerCamelCase  ):
				UpperCAmelCase__        =				1







				@register_to_config
				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						Any = 1_0_0_0						,    __lowerCamelCase							:						Optional[int] = 0.0001						,    __lowerCamelCase							:						Any = 0.02						,    __lowerCamelCase							:						int = "linear"						,    __lowerCamelCase							:						Any = None						,    __lowerCamelCase							:						List[Any] = True						,    __lowerCamelCase							:						List[Any] = True						,    __lowerCamelCase							:						List[str] = 0						,    __lowerCamelCase							:						List[Any] = "epsilon"						,    __lowerCamelCase							:						List[Any] = 1.0						,    **__lowerCamelCase							:						List[Any]						,    )				->					Any:
										if kwargs.get("set_alpha_to_one"						,    A__	) is not None:
																__magic_name__            =     (
																    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
																)
																deprecate("set_alpha_to_one"						,    "1.0.0"						,    A__						,    standard_warn=A__	)
																__magic_name__            =     kwargs["set_alpha_to_one"]
										if trained_betas is not None:
																__magic_name__            =     torch.tensor(A__						,    dtype=torch.floataa	)
										elif beta_schedule == "linear":
																__magic_name__            =     torch.linspace(A__						,    A__						,    A__						,    dtype=torch.floataa	)
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__magic_name__            =     (
																    torch.linspace(beta_start**0.5						,    beta_end**0.5						,    A__						,    dtype=torch.floataa	) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																__magic_name__            =     betas_for_alpha_bar(A__	)
										else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''	)

										__magic_name__            =     1.0 - self.betas
										__magic_name__            =     torch.cumprod(self.alphas						,    dim=0	)

										# At every step in inverted ddim, we are looking into the next alphas_cumprod
										# For the final step, there is no next alphas_cumprod, and the index is out of bounds
										# `set_alpha_to_zero` decides whether we set this parameter simply to zero
										# in this case, self.step() just output the predicted noise
										# or whether we use the final alpha of the "non-previous" one.
										__magic_name__            =     torch.tensor(0.0	) if set_alpha_to_zero else self.alphas_cumprod[-1]

										# standard deviation of the initial noise distribution
										__magic_name__            =     1.0

										# setable values
										__magic_name__            =     None
										__magic_name__            =     torch.from_numpy(np.arange(0						,    A__	).copy().astype(np.intaa	)	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Optional[Any] = None	)				->					torch.FloatTensor:
										return sample







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple = None	)				->					List[Any]:

										if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
																    f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
																    f''' maximal {self.config.num_train_timesteps} timesteps.'''	)

										__magic_name__            =     num_inference_steps
										__magic_name__            =     self.config.num_train_timesteps // self.num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# casting to int to avoid issues when num_inference_step is power of 3
										__magic_name__            =     (np.arange(0						,    A__	) * step_ratio).round().copy().astype(np.intaa	)
										__magic_name__            =     torch.from_numpy(A__	).to(A__	)
										self.timesteps += self.config.steps_offset







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Tuple = 0.0						,    __lowerCamelCase							:						str = False						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Any = True						,    )				->					Union[DDIMSchedulerOutput, Tuple]:
										# 1. get previous step value (=t+1)
										__magic_name__            =     timestep + self.config.num_train_timesteps // self.num_inference_steps

										# 2. compute alphas, betas
										# change original implementation to exactly match noise levels for analogous forward process
										__magic_name__            =     self.alphas_cumprod[timestep]
										__magic_name__            =     (
										    self.alphas_cumprod[prev_timestep]
										    if prev_timestep < self.config.num_train_timesteps
										    else self.final_alpha_cumprod
										)

										__magic_name__            =     1 - alpha_prod_t

										# 3. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										if self.config.prediction_type == "epsilon":
																__magic_name__            =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																__magic_name__            =     model_output
										elif self.config.prediction_type == "sample":
																__magic_name__            =     model_output
																__magic_name__            =     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
										elif self.config.prediction_type == "v_prediction":
																__magic_name__            =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																__magic_name__            =     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
										else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
																    " `v_prediction`"	)

										# 4. Clip or threshold "predicted x_0"
										if self.config.clip_sample:
																__magic_name__            =     pred_original_sample.clamp(
																    -self.config.clip_sample_range						,    self.config.clip_sample_range	)

										# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										__magic_name__            =     (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

										# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										__magic_name__            =     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

										if not return_dict:
																return (prev_sample, pred_original_sample)
										return DDIMSchedulerOutput(prev_sample=A__						,    pred_original_sample=A__	)







				def __len__(							self							:						Dict	)				->					List[Any]:
										return self.config.num_train_timesteps











"""simple docstring"""

from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				42





class 						A_							(      snake_case_					,							snake_case_  ):
				UpperCAmelCase__        =				True







				@register_to_config
				def __init__(							self							:						Any						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Tuple[str] = ("DownEncoderBlock2D",)						,    __lowerCamelCase							:						Tuple[str] = ("UpDecoderBlock2D",)						,    __lowerCamelCase							:						Tuple[int] = (6_4,)						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						str = "silu"						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						float = 0.1_8215						,    )				->					Any:
										super().__init__()

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

										# pass init params to Decoder
										__magic_name__            =     Decoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    up_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    )

										__magic_name__            =     nn.Convad(2 * latent_channels						,    2 * latent_channels						,    1	)
										__magic_name__            =     nn.Convad(__lowerCamelCase						,    __lowerCamelCase						,    1	)

										__magic_name__            =     False
										__magic_name__            =     False

										# only relevant if vae tiling is enabled
										__magic_name__            =     self.config.sample_size
										__magic_name__            =     (
										    self.config.sample_size[0]
										    if isinstance(self.config.sample_size						,    (list, tuple)	)
										    else self.config.sample_size
										)
										__magic_name__            =     int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
										__magic_name__            =     0.25







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    (Encoder, Decoder)	):
																__magic_name__            =     value







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						bool = True	)				->					int:
										__magic_name__            =     use_tiling







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.enable_tiling(__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     True







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__            =     False







				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def     _snake_case					(							self							:						Optional[int]	)				->					Dict[str, AttentionProcessor]:
										__magic_name__            =     {}

										def fn_recursive_add_processors(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						Dict[str, AttentionProcessor]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						__magic_name__            =     module.processor

																for sub_name, child in module.named_children():
																						fn_recursive_add_processors(f'''{name}.{sub_name}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

																return processors

										for name, module in self.named_children():
																fn_recursive_add_processors(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)

										return processors







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)				->					int:
										__magic_name__            =     len(self.attn_processors.keys()	)

										if isinstance(__lowerCamelCase						,    __lowerCamelCase	) and len(__lowerCamelCase	) != count:
																raise ValueError(
																    f'''A dict of processors was passed, but the number of processors {len(__lowerCamelCase	)} does not match the'''
																    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''	)

										def fn_recursive_attn_processor(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						List[str]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						if not isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																												module.set_processor(__lowerCamelCase	)
																						else:
																												module.set_processor(processor.pop(f'''{name}.processor'''	)	)

																for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

										for name, module in self.named_children():
																fn_recursive_attn_processor(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										self.set_attn_processor(AttnProcessor()	)







				@apply_forward_hook
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																return self.tiled_encode(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										if self.use_slicing and x.shape[0] > 1:
																__magic_name__            =     [self.encoder(__lowerCamelCase	) for x_slice in x.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self.encoder(__lowerCamelCase	)

										__magic_name__            =     self.quant_conv(__lowerCamelCase	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																return self.tiled_decode(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
										__magic_name__            =     self.decoder(__lowerCamelCase	)

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				@apply_forward_hook
				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_slicing and z.shape[0] > 1:
																__magic_name__            =     [self._decode(__lowerCamelCase	).sample for z_slice in z.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self._decode(__lowerCamelCase	).sample

										if not return_dict:
																return (decoded,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[2]						,    b.shape[2]						,    __lowerCamelCase	)
										for y in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[3]						,    b.shape[3]						,    __lowerCamelCase	)
										for x in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										__magic_name__            =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_latent_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_latent_min_size - blend_extent

										# Split the image into 512x512 tiles and encode them separately.
										__magic_name__            =     []
										for i in range(0						,    x.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    x.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																						__magic_name__            =     self.encoder(__lowerCamelCase	)
																						__magic_name__            =     self.quant_conv(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_sample_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_sample_min_size - blend_extent

										# Split z into overlapping 64x64 tiles and decode them separately.
										# The tiles have an overlap to avoid seams between tiles.
										__magic_name__            =     []
										for i in range(0						,    z.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    z.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
																						__magic_name__            =     self.decoder(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[torch.Generator] = None						,    )				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     sample
										__magic_name__            =     self.encode(__lowerCamelCase	).latent_dist
										if sample_posterior:
																__magic_name__            =     posterior.sample(generator=__lowerCamelCase	)
										else:
																__magic_name__            =     posterior.mode()
										__magic_name__            =     self.decode(__lowerCamelCase	).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)



