


"""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	:  Any            =     8


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=BITS ):

			'''simple docstring'''




			__lowerCAmelCase							       =     x.device

			__lowerCAmelCase							       =     (x * 255).int().clamp(0       ,					255 )

			__lowerCAmelCase							       =     2 ** torch.arange(bits - 1       ,					-1       ,					-1       ,					device=_UpperCamelCase )
			__lowerCAmelCase							       =     rearrange(_UpperCamelCase       ,					"d -> d 1 1" )
			__lowerCAmelCase							       =     rearrange(_UpperCamelCase       ,					"b c h w -> b c 1 h w" )

			__lowerCAmelCase							       =     ((x & mask) != 0).float()
			__lowerCAmelCase							       =     rearrange(_UpperCamelCase       ,					"b c d h w -> b (c d) h w" )
			__lowerCAmelCase							       =     bits * 2 - 1
			return bits


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=BITS ):

			'''simple docstring'''




			__lowerCAmelCase							       =     x.device

			__lowerCAmelCase							       =     (x > 0).int()
			__lowerCAmelCase							       =     2 ** torch.arange(bits - 1       ,					-1       ,					-1       ,					device=_UpperCamelCase       ,					dtype=torch.intaa )

			__lowerCAmelCase							       =     rearrange(_UpperCamelCase       ,					"d -> d 1 1" )
			__lowerCAmelCase							       =     rearrange(_UpperCamelCase       ,					"b (c d) h w -> b c d h w"       ,					d=8 )
			__lowerCAmelCase							       =     reduce(x * mask       ,					"b c d h w -> b c h w"       ,					"sum" )
			return (dec / 255).clamp(0.0       ,					1.0 )


def _lowerCamelCase      (						self       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase = 0.0       ,					_UpperCamelCase = True       ,					_UpperCamelCase=None       ,					_UpperCamelCase = True       ,					):

			'''simple docstring'''




			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)
			__lowerCAmelCase							       =     timestep - self.config.num_train_timesteps // self.num_inference_steps

			# 2. compute alphas, betas
			__lowerCAmelCase							       =     self.alphas_cumprod[timestep]
			__lowerCAmelCase							       =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

			__lowerCAmelCase							       =     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
			__lowerCAmelCase							       =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

			# 4. Clip "predicted x_0"
			__lowerCAmelCase							       =     self.bit_scale
			if self.config.clip_sample:
						__lowerCAmelCase							       =     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)
			__lowerCAmelCase							       =     self._get_variance(_UpperCamelCase       ,					_UpperCamelCase )
			__lowerCAmelCase							       =     eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						__lowerCAmelCase							       =     (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
			__lowerCAmelCase							       =     (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
			__lowerCAmelCase							       =     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
						__lowerCAmelCase							       =     model_output.device if torch.is_tensor(_UpperCamelCase ) else "cpu"
						__lowerCAmelCase							       =     torch.randn(model_output.shape       ,					dtype=model_output.dtype       ,					generator=_UpperCamelCase ).to(_UpperCamelCase )
						__lowerCAmelCase							       =     self._get_variance(_UpperCamelCase       ,					_UpperCamelCase ) ** 0.5 * eta * noise

						__lowerCAmelCase							       =     prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=_UpperCamelCase       ,					pred_original_sample=_UpperCamelCase )


def _lowerCamelCase      (						self       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase="epsilon"       ,					_UpperCamelCase=None       ,					_UpperCamelCase = True       ,					):

			'''simple docstring'''




			__lowerCAmelCase							       =     timestep

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

			# 1. compute alphas, betas
			__lowerCAmelCase							       =     self.alphas_cumprod[t]
			__lowerCAmelCase							       =     self.alphas_cumprod[t - 1] if t > 0 else self.one
			__lowerCAmelCase							       =     1 - alpha_prod_t
			__lowerCAmelCase							       =     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":
						__lowerCAmelCase							       =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif prediction_type == "sample":
						__lowerCAmelCase							       =     model_output
			else:
						raise ValueError(f"Unsupported prediction_type {prediction_type}." )

			# 3. Clip "predicted x_0"
			__lowerCAmelCase							       =     self.bit_scale
			if self.config.clip_sample:
						__lowerCAmelCase							       =     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
			__lowerCAmelCase							       =     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			__lowerCAmelCase							       =     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
			__lowerCAmelCase							       =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

			__lowerCAmelCase							       =     pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=_UpperCamelCase       ,					pred_original_sample=_UpperCamelCase )


class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a = 1.0       ,  ):
										super().__init__()
										__lowerCAmelCase							       =     bit_scale
										__lowerCAmelCase							       =     (
										    ddim_bit_scheduler_step if isinstance(__a       ,  __a      ) else ddpm_bit_scheduler_step
										)

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



							@torch.no_grad()
							def __call__(	self       ,  __a = 2_56       ,  __a = 2_56       ,  __a = 50       ,  __a = None       ,  __a = 1       ,  __a = "pil"       ,  __a = True       ,  **__a       ,  ):
										__lowerCAmelCase							       =     torch.randn(
										    (batch_size, self.unet.config.in_channels, height, width)       ,  generator=__a       ,  )
										__lowerCAmelCase							       =     decimal_to_bits(__a      ) * self.bit_scale
										__lowerCAmelCase							       =     latents.to(self.device      )

										self.scheduler.set_timesteps(__a      )

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

													# compute the previous noisy sample x_t -> x_t-1
													__lowerCAmelCase							       =     self.scheduler.step(__a       ,  __a       ,  __a      ).prev_sample

										__lowerCAmelCase							       =     bits_to_decimal(__a      )

										if output_type == "pil":
													__lowerCAmelCase							       =     self.numpy_to_pil(__a      )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=__a      )








"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




