'''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:			List[Any]	       =       8
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=BITS					):
 lowercase__    :   str						= x.device

 lowercase__    :   Any						= (x * 255).int().clamp(0		,					255					)

 lowercase__    :   int						= 2 ** torch.arange(bits - 1		,					-1		,					-1		,					device=UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= rearrange(UpperCAmelCase		,					'''d -> d 1 1'''					)
 lowercase__    :   Any						= rearrange(UpperCAmelCase		,					'''b c h w -> b c 1 h w'''					)

 lowercase__    :   Any						= ((x & mask) != 0).float()
 lowercase__    :   Tuple						= rearrange(UpperCAmelCase		,					'''b c d h w -> b (c d) h w'''					)
 lowercase__    :   str						= bits * 2 - 1
 return bits
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=BITS					):
 lowercase__    :   str						= x.device

 lowercase__    :   int						= (x > 0).int()
 lowercase__    :   Optional[int]						= 2 ** torch.arange(bits - 1		,					-1		,					-1		,					device=UpperCAmelCase		,					dtype=torch.intaa					)

 lowercase__    :   Optional[Any]						= rearrange(UpperCAmelCase		,					'''d -> d 1 1'''					)
 lowercase__    :   Optional[int]						= rearrange(UpperCAmelCase		,					'''b (c d) h w -> b c d h w'''		,					d=8					)
 lowercase__    :   Dict						= reduce(x * mask		,					'''b c d h w -> b c h w'''		,					'''sum'''					)
 return (dec / 255).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)
 lowercase__    :   Union[str, Any]						= timestep - self.config.num_train_timesteps // self.num_inference_steps

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

 lowercase__    :   Tuple						= 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
 lowercase__    :   str						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 lowercase__    :   Optional[Any]						= self.bit_scale
 if self.config.clip_sample:
  lowercase__    :   Optional[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)
 lowercase__    :   Optional[Any]						= self._get_variance(UpperCAmelCase		,					UpperCAmelCase					)
 lowercase__    :   Tuple						= eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  lowercase__    :   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
 lowercase__    :   Optional[Any]						= (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
 lowercase__    :   Optional[int]						= 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
  lowercase__    :   Optional[Any]						= model_output.device if torch.is_tensor(UpperCAmelCase					) else '''cpu'''
  lowercase__    :   Any						= torch.randn(model_output.shape		,					dtype=model_output.dtype		,					generator=UpperCAmelCase					).to(UpperCAmelCase					)
  lowercase__    :   Any						= self._get_variance(UpperCAmelCase		,					UpperCAmelCase					) ** 0.5 * eta * noise

  lowercase__    :   int						= 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		,					):
 lowercase__    :   Any						= timestep

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

 # 1. compute alphas, betas
 lowercase__    :   Optional[Any]						= self.alphas_cumprod[t]
 lowercase__    :   Optional[int]						= self.alphas_cumprod[t - 1] if t > 0 else self.one
 lowercase__    :   int						= 1 - alpha_prod_t
 lowercase__    :   Dict						= 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":
  lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  lowercase__    :   List[str]						= model_output
 else:
  raise ValueError(F"""Unsupported prediction_type {prediction_type}."""					)

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

 # 6. Add noise
 lowercase__    :   List[str]						= 0
 if t > 0:
  lowercase__    :   Optional[Any]						= torch.randn(
      model_output.size()		,					dtype=model_output.dtype		,					layout=model_output.layout		,					generator=UpperCAmelCase					).to(model_output.device					)
  lowercase__    :   Any						= (self._get_variance(UpperCAmelCase		,					predicted_variance=UpperCAmelCase					) ** 0.5) * noise

 lowercase__    :   Optional[int]						= pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=UpperCAmelCase		,					pred_original_sample=UpperCAmelCase					)





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       )  ->  str:
        super().__init__()
        lowercase__    :   List[str]						= bit_scale
        lowercase__    :   str						= (
            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     ,       )  ->  Union[Tuple, ImagePipelineOutput]:
        lowercase__    :   str						= torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)     ,       generator=__lowerCAmelCase     ,       )
        lowercase__    :   List[Any]						= decimal_to_bits(__lowerCAmelCase			) * self.bit_scale
        lowercase__    :   List[str]						= latents.to(self.device			)

        self.scheduler.set_timesteps(__lowerCAmelCase			)

        for t in self.progress_bar(self.scheduler.timesteps			):
         # predict the noise residual
         lowercase__    :   List[str]						= self.unet(__lowerCAmelCase     ,       __lowerCAmelCase			).sample

         # compute the previous noisy sample x_t -> x_t-1
         lowercase__    :   List[Any]						= self.scheduler.step(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			).prev_sample

        lowercase__    :   int						= bits_to_decimal(__lowerCAmelCase			)

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

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__lowerCAmelCase			)


'''simple docstring'''

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam



class       UpperCAmelCase  (						tf.keras.optimizers.schedules.LearningRateSchedule ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = None     ,       )  ->  Union[str, Any]:
        super().__init__()
        lowercase__    :   Any						= initial_learning_rate
        lowercase__    :   str						= warmup_steps
        lowercase__    :   Dict						= power
        lowercase__    :   int						= decay_schedule_fn
        lowercase__    :   Dict						= name

       def __call__(						self     ,       __lowerCAmelCase			)  ->  int:
        with tf.name_scope(self.name or '''WarmUp'''			) as name:
         # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
         # learning rate will be `global_step/num_warmup_steps * init_lr`.
         lowercase__    :   Union[str, Any]						= tf.cast(__lowerCAmelCase     ,       tf.floataa			)
         lowercase__    :   Optional[int]						= tf.cast(self.warmup_steps     ,       tf.floataa			)
         lowercase__    :   Dict						= global_step_float / warmup_steps_float
         lowercase__    :   Dict						= self.initial_learning_rate * tf.math.pow(__lowerCAmelCase     ,       self.power			)
         return tf.cond(
             global_step_float < warmup_steps_float     ,       lambda: warmup_learning_rate     ,       lambda: self.decay_schedule_fn(step - self.warmup_steps			)     ,       name=__lowerCAmelCase     ,       )


       def _lowerCAmelCase(						self			)  ->  List[str]:
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 0.9		,					UpperCAmelCase = 0.9_9_9		,					UpperCAmelCase = 1E-8		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 1.0		,					UpperCAmelCase = None		,					):
 lowercase__    :   List[Any]						= tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=UpperCAmelCase		,					decay_steps=num_train_steps - num_warmup_steps		,					end_learning_rate=init_lr * min_lr_ratio		,					power=UpperCAmelCase		,					)
 if num_warmup_steps:
  lowercase__    :   Optional[Any]						= WarmUp(
      initial_learning_rate=UpperCAmelCase		,					decay_schedule_fn=UpperCAmelCase		,					warmup_steps=UpperCAmelCase		,					)
 if weight_decay_rate > 0.0:
  lowercase__    :   str						= AdamWeightDecay(
      learning_rate=UpperCAmelCase		,					weight_decay_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']		,					include_in_weight_decay=UpperCAmelCase		,					)
 else:
  lowercase__    :   Any						= tf.keras.optimizers.Adam(
      learning_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					)
 # We return the optimizer and the LR scheduler in order to better track the
 # evolution of the LR independently of the optimizer.
 return optimizer, lr_schedule



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase = 0.0_0_1     ,       __lowerCAmelCase = 0.9     ,       __lowerCAmelCase = 0.9_9_9     ,       __lowerCAmelCase = 1E-7     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = 0.0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "AdamWeightDecay"     ,       **__lowerCAmelCase     ,       )  ->  str:
        super().__init__(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)
        lowercase__    :   int						= weight_decay_rate
        lowercase__    :   Dict						= include_in_weight_decay
        lowercase__    :   List[str]						= exclude_from_weight_decay

       @classmethod
       def _lowerCAmelCase(						cls     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   int						= {'''WarmUp''': WarmUp}
        return super(__lowerCAmelCase     ,       cls			).from_config(__lowerCAmelCase     ,       custom_objects=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        super(__lowerCAmelCase     ,       self			)._prepare_local(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Any						= tf.constant(
            self.weight_decay_rate     ,       name='''adam_weight_decay_rate'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   str						= self._do_use_weight_decay(var.name			)
        if do_decay:
         return var.assign_sub(
             learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']     ,       use_locking=self._use_locking     ,       )
        return tf.no_op()

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   Any						= list(zip(*__lowerCAmelCase			)			)
        return super(__lowerCAmelCase     ,       self			).apply_gradients(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       name=__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        if apply_state is None:
         return self._decayed_lr_t[var_dtype], {}

        lowercase__    :   Union[str, Any]						= apply_state or {}
        lowercase__    :   Optional[Any]						= apply_state.get((var_device, var_dtype)			)
        if coefficients is None:
         lowercase__    :   Union[str, Any]						= self._fallback_apply_state(__lowerCAmelCase     ,       __lowerCAmelCase			)
         lowercase__    :   Optional[int]						= coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Any:
        lowercase__			,							lowercase__    :   Dict						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   List[str]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_dense(__lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_sparse(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Optional[Any]						= super().get_config()
        config.update({'''weight_decay_rate''': self.weight_decay_rate}			)
        return config


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[Any]:
        if self.weight_decay_rate == 0:
         return False

        if self._include_in_weight_decay:
         for r in self._include_in_weight_decay:
          if re.search(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return True

        if self._exclude_from_weight_decay:
         for r in self._exclude_from_weight_decay:
          if re.search(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return False
        return True



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= []
        lowercase__    :   Optional[int]						= None

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if self._accum_steps is None:
         lowercase__    :   int						= tf.Variable(
             tf.constant(0     ,       dtype=tf.intaa			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )

        return self._accum_steps.value()

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        if not self._gradients:
         raise ValueError('''The accumulator should be called first to initialize the gradients'''			)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

       def __call__(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self._gradients:
         lowercase__    :   str						= self.step  # Create the step variable.
         self._gradients.extend(
             [
                 tf.Variable(
                     tf.zeros_like(__lowerCAmelCase			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )
                 if gradient is not None
                 else gradient
                 for gradient in gradients
             ]			)
        if len(__lowerCAmelCase			) != len(self._gradients			):
         raise ValueError(F"""Expected {len(self._gradients			)} gradients, but got {len(__lowerCAmelCase			)}"""			)

        for accum_gradient, gradient in zip(self._gradients     ,       __lowerCAmelCase			):
         if accum_gradient is not None and gradient is not None:
          accum_gradient.assign_add(__lowerCAmelCase			)

        self._accum_steps.assign_add(1			)


       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if not self._gradients:
         return
        self._accum_steps.assign(0			)
        for gradient in self._gradients:
         if gradient is not None:
          gradient.assign(tf.zeros_like(__lowerCAmelCase			)			)

