





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


UpperCAmelCase_								=							8

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=BITS   )		->						List[str]:




       '''simple docstring'''






       _A=	x.device

       _A=	(x * 2_55).int().clamp(0       ,					2_55   )

       _A=	2 ** torch.arange(bits - 1       ,					-1       ,					-1       ,					device=lowerCAmelCase_   )
       _A=	rearrange(lowerCAmelCase_       ,					'd -> d 1 1'   )
       _A=	rearrange(lowerCAmelCase_       ,					'b c h w -> b c 1 h w'   )

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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=BITS   )		->						int:




       '''simple docstring'''






       _A=	x.device

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

       _A=	rearrange(lowerCAmelCase_       ,					'd -> d 1 1'   )
       _A=	rearrange(lowerCAmelCase_       ,					'b (c d) h w -> b c d h w'       ,					d=8   )
       _A=	reduce(x * mask       ,					'b c d h w -> b c h w'       ,					'sum'   )
       return (dec / 2_55).clamp(0.0       ,					1.0   )

def 							UpperCamelCase				(    self       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = 0.0       ,					lowerCAmelCase_ = True       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_ = True       ,					)		->						Union[DDIMSchedulerOutput, Tuple]:




       '''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)
       _A=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

       # 4. Clip "predicted x_0"
       _A=	self.bit_scale
       if self.config.clip_sample:
              _A=	torch.clamp(lowerCAmelCase_       ,					-scale       ,					lowerCAmelCase_   )

       # 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)
       _A=	self._get_variance(lowerCAmelCase_       ,					lowerCAmelCase_   )
       _A=	eta * variance ** 0.5

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

              _A=	prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_       ,					pred_original_sample=lowerCAmelCase_   )

def 							UpperCamelCase				(    self       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_="epsilon"       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_ = True       ,					)		->						Union[DDPMSchedulerOutput, Tuple]:




       '''simple docstring'''






       _A=	timestep

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

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

       # 3. Clip "predicted x_0"
       _A=	self.bit_scale
       if self.config.clip_sample:
              _A=	torch.clamp(lowerCAmelCase_       ,					-scale       ,					lowerCAmelCase_   )

       # 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
       _A=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
       _A=	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
       _A=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

       _A=	pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_       ,					pred_original_sample=lowerCAmelCase_   )





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = 1.0				,      ):
            super().__init__()
            _A=	bit_scale
            _A=	(
                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__				,      ):
            _A=	torch.randn(
                (batch_size, self.unet.config.in_channels, height, width)				,      generator=lowerCAmelCase__				,      )
            _A=	decimal_to_bits(lowerCAmelCase__       ) * self.bit_scale
            _A=	latents.to(self.device       )

            self.scheduler.set_timesteps(lowerCAmelCase__       )

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

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

            _A=	bits_to_decimal(lowerCAmelCase__       )

            if output_type == "pil":
                   _A=	self.numpy_to_pil(lowerCAmelCase__       )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__       )
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


UpperCAmelCase_								=							{
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_								=							[
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_								=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)