


'''simple docstring'''



from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__A		=8







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=BITS   ):
			UpperCAmelCase__					:   Dict       				=       x.device

			UpperCAmelCase__					:   Optional[Any]       				=       (x * 2_5_5).int().clamp(0	, 2_5_5   )

			UpperCAmelCase__					:   List[str]       				=       2 ** torch.arange(bits - 1	, -1	, -1	, device=UpperCamelCase__   )
			UpperCAmelCase__					:   Union[str, Any]       				=       rearrange(UpperCamelCase__	, """d -> d 1 1"""   )
			UpperCAmelCase__					:   Optional[int]       				=       rearrange(UpperCamelCase__	, """b c h w -> b c 1 h w"""   )

			UpperCAmelCase__					:   Dict       				=       ((x & mask) != 0).float()
			UpperCAmelCase__					:   int       				=       rearrange(UpperCamelCase__	, """b c d h w -> b (c d) h w"""   )
			UpperCAmelCase__					:   str       				=       bits * 2 - 1
			return bits







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=BITS   ):
			UpperCAmelCase__					:   int       				=       x.device

			UpperCAmelCase__					:   List[Any]       				=       (x > 0).int()
			UpperCAmelCase__					:   Union[str, Any]       				=       2 ** torch.arange(bits - 1	, -1	, -1	, device=UpperCamelCase__	, dtype=torch.intaa   )

			UpperCAmelCase__					:   str       				=       rearrange(UpperCamelCase__	, """d -> d 1 1"""   )
			UpperCAmelCase__					:   Any       				=       rearrange(UpperCamelCase__	, """b (c d) h w -> b c d h w"""	, d=8   )
			UpperCAmelCase__					:   Tuple       				=       reduce(x * mask	, """b c d h w -> b c h w"""	, """sum"""   )
			return (dec / 2_5_5).clamp(0.0	, 1.0   )







def       _UpperCamelCase (						self	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__ = 0.0	, UpperCamelCase__ = True	, UpperCamelCase__=None	, UpperCamelCase__ = True	, ):
			if self.num_inference_steps is None:
						raise ValueError(
						    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""   )

			# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
			# Ideally, read DDIM paper in-detail understanding

			# Notation (<variable name> -> <name in paper>
			# - pred_noise_t -> e_theta(x_t, t)
			# - pred_original_sample -> f_theta(x_t, t) or x_0
			# - std_dev_t -> sigma_t
			# - eta -> η
			# - pred_sample_direction -> "direction pointing to x_t"
			# - pred_prev_sample -> "x_t-1"

			# 1. get previous step value (=t-1)
			UpperCAmelCase__					:   str       				=       timestep - self.config.num_train_timesteps // self.num_inference_steps

			# 2. compute alphas, betas
			UpperCAmelCase__					:   Any       				=       self.alphas_cumprod[timestep]
			UpperCAmelCase__					:   str       				=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

			UpperCAmelCase__					:   List[Any]       				=       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
			UpperCAmelCase__					:   int       				=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

			# 4. Clip "predicted x_0"
			UpperCAmelCase__					:   Optional[Any]       				=       self.bit_scale
			if self.config.clip_sample:
						UpperCAmelCase__					:   int       				=       torch.clamp(UpperCamelCase__	, -scale	, UpperCamelCase__   )

			# 5. compute variance: "sigma_t(η)" -> see formula (16)
			# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
			UpperCAmelCase__					:   Optional[Any]       				=       self._get_variance(UpperCamelCase__	, UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						UpperCAmelCase__					:   List[str]       				=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

			# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			UpperCAmelCase__					:   int       				=       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

			# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			UpperCAmelCase__					:   Union[str, Any]       				=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

			if eta > 0:
						# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
						UpperCAmelCase__					:   List[Any]       				=       model_output.device if torch.is_tensor(UpperCamelCase__   ) else """cpu"""
						UpperCAmelCase__					:   Optional[Any]       				=       torch.randn(model_output.shape	, dtype=model_output.dtype	, generator=UpperCamelCase__   ).to(UpperCamelCase__   )
						UpperCAmelCase__					:   List[str]       				=       self._get_variance(UpperCamelCase__	, UpperCamelCase__   ) ** 0.5 * eta * noise

						UpperCAmelCase__					:   Tuple       				=       prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=UpperCamelCase__	, pred_original_sample=UpperCamelCase__   )







def       _UpperCamelCase (						self	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__="epsilon"	, UpperCamelCase__=None	, UpperCamelCase__ = True	, ):
			UpperCAmelCase__					:   str       				=       timestep

			if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
						UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       torch.split(UpperCamelCase__	, sample.shape[1]	, dim=1   )
			else:
						UpperCAmelCase__					:   List[str]       				=       None

			# 1. compute alphas, betas
			UpperCAmelCase__					:   List[Any]       				=       self.alphas_cumprod[t]
			UpperCAmelCase__					:   List[Any]       				=       self.alphas_cumprod[t - 1] if t > 0 else self.one
			UpperCAmelCase__					:   Dict       				=       1 - alpha_prod_t
			UpperCAmelCase__					:   Optional[int]       				=       1 - alpha_prod_t_prev

			# 2. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
			if prediction_type == "epsilon":
						UpperCAmelCase__					:   Tuple       				=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif prediction_type == "sample":
						UpperCAmelCase__					:   Tuple       				=       model_output
			else:
						raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''   )

			# 3. Clip "predicted x_0"
			UpperCAmelCase__					:   List[Any]       				=       self.bit_scale
			if self.config.clip_sample:
						UpperCAmelCase__					:   int       				=       torch.clamp(UpperCamelCase__	, -scale	, UpperCamelCase__   )

			# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
			# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
			UpperCAmelCase__					:   str       				=       (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			UpperCAmelCase__					:   Optional[int]       				=       self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

			# 5. Compute predicted previous sample µ_t
			# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
			UpperCAmelCase__					:   Optional[int]       				=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

			# 6. Add noise
			UpperCAmelCase__					:   int       				=       0
			if t > 0:
						UpperCAmelCase__					:   str       				=       torch.randn(
						    model_output.size()	, dtype=model_output.dtype	, layout=model_output.layout	, generator=UpperCamelCase__   ).to(model_output.device   )
						UpperCAmelCase__					:   Tuple       				=       (self._get_variance(UpperCamelCase__	, predicted_variance=UpperCamelCase__   ) ** 0.5) * noise

			UpperCAmelCase__					:   Union[str, Any]       				=       pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=UpperCamelCase__	, pred_original_sample=UpperCamelCase__   )


class 						_snake_case   (       a__		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = 1.0							,							):
								super().__init__()
								UpperCAmelCase__					:   Optional[int]       				=       bit_scale
								UpperCAmelCase__					:   List[Any]       				=       (
								    ddim_bit_scheduler_step if isinstance(_lowerCamelCase							,							_lowerCamelCase) else ddpm_bit_scheduler_step
								)

								self.register_modules(unet=_lowerCamelCase							,							scheduler=_lowerCamelCase)




					@torch.no_grad()
					def __call__( self							,							_lowerCamelCase = 256							,							_lowerCamelCase = 256							,							_lowerCamelCase = 50							,							_lowerCamelCase = None							,							_lowerCamelCase = 1							,							_lowerCamelCase = "pil"							,							_lowerCamelCase = True							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Any       				=       torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)							,							generator=_lowerCamelCase							,							)
								UpperCAmelCase__					:   int       				=       decimal_to_bits(_lowerCamelCase) * self.bit_scale
								UpperCAmelCase__					:   Any       				=       latents.to(self.device)

								self.scheduler.set_timesteps(_lowerCamelCase)

								for t in self.progress_bar(self.scheduler.timesteps):
											# predict the noise residual
											UpperCAmelCase__					:   Dict       				=       self.unet(_lowerCamelCase							,							_lowerCamelCase).sample

											# compute the previous noisy sample x_t -> x_t-1
											UpperCAmelCase__					:   Tuple       				=       self.scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

								UpperCAmelCase__					:   List[str]       				=       bits_to_decimal(_lowerCamelCase)

								if output_type == "pil":
											UpperCAmelCase__					:   Optional[int]       				=       self.numpy_to_pil(_lowerCamelCase)

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=_lowerCamelCase)



'''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 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       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							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)