




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


_lowerCamelCase      			=						8




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Dict=BITS				)  ->							Tuple:
    UpperCAmelCase_										=					x.device

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

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

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Union[str, Any]=BITS				)  ->							Optional[Any]:
    UpperCAmelCase_										=					x.device

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

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




def  SCREAMING_SNAKE_CASE     (	self      :						str	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						float = 0.0	,					__UpperCamelCase      :						bool = True	,					__UpperCamelCase      :						List[str]=None	,					__UpperCamelCase      :						bool = True	,					)  ->							Union[DDIMSchedulerOutput, Tuple]:
    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_										=					timestep - self.config.num_train_timesteps // self.num_inference_steps

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

    UpperCAmelCase_										=					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_										=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

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

        UpperCAmelCase_										=					prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=__UpperCamelCase	,					pred_original_sample=__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	self      :						int	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						Dict="epsilon"	,					__UpperCamelCase      :						Tuple=None	,					__UpperCamelCase      :						bool = True	,					)  ->							Union[DDPMSchedulerOutput, Tuple]:
    UpperCAmelCase_										=					timestep

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

    # 1. compute alphas, betas
    UpperCAmelCase_										=					self.alphas_cumprod[t]
    UpperCAmelCase_										=					self.alphas_cumprod[t - 1] if t > 0 else self.one
    UpperCAmelCase_										=					1 - alpha_prod_t
    UpperCAmelCase_										=					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_										=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        UpperCAmelCase_										=					model_output
    else:
        raise ValueError(f'Unsupported prediction_type {prediction_type}.'				)

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

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

    UpperCAmelCase_										=					pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=__UpperCamelCase	,					pred_original_sample=__UpperCamelCase				)



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Any ,   __snake_case       :      UNetaDConditionModel ,   __snake_case       :      Union[DDIMScheduler, DDPMScheduler] ,   __snake_case       :      Optional[float] = 1.0 ,   ):
         super().__init__()
         UpperCAmelCase_										=					bit_scale
         UpperCAmelCase_										=					(
             ddim_bit_scheduler_step if isinstance(__snake_case ,   __snake_case    ) else ddpm_bit_scheduler_step
         )

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




     @torch.no_grad()
     def __call__(     self       :      str ,   __snake_case       :      Optional[int] = 2_56 ,   __snake_case       :      Optional[int] = 2_56 ,   __snake_case       :      Optional[int] = 50 ,   __snake_case       :      Optional[torch.Generator] = None ,   __snake_case       :      Optional[int] = 1 ,   __snake_case       :      Optional[str] = "pil" ,   __snake_case       :      bool = True ,   **__snake_case       :      Dict ,   ):
         UpperCAmelCase_										=					torch.randn(
             (batch_size, self.unet.config.in_channels, height, width) ,   generator=__snake_case ,   )
         UpperCAmelCase_										=					decimal_to_bits(__snake_case    ) * self.bit_scale
         UpperCAmelCase_										=					latents.to(self.device    )

         self.scheduler.set_timesteps(__snake_case    )

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

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

         UpperCAmelCase_										=					bits_to_decimal(__snake_case    )

         if output_type == "pil":
             UpperCAmelCase_										=					self.numpy_to_pil(__snake_case    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=__snake_case    )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase      			=						{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['RobertaTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RobertaForCausalLM',
     'RobertaForMaskedLM',
     'RobertaForMultipleChoice',
     'RobertaForQuestionAnswering',
     'RobertaForSequenceClassification',
     'RobertaForTokenClassification',
     'RobertaModel',
     'RobertaPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRobertaForCausalLM',
     'TFRobertaForMaskedLM',
     'TFRobertaForMultipleChoice',
     'TFRobertaForQuestionAnswering',
     'TFRobertaForSequenceClassification',
     'TFRobertaForTokenClassification',
     'TFRobertaMainLayer',
     'TFRobertaModel',
     'TFRobertaPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'FlaxRobertaForCausalLM',
     'FlaxRobertaForMaskedLM',
     'FlaxRobertaForMultipleChoice',
     'FlaxRobertaForQuestionAnswering',
     'FlaxRobertaForSequenceClassification',
     'FlaxRobertaForTokenClassification',
     'FlaxRobertaModel',
     'FlaxRobertaPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
 from .tokenization_roberta import RobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roberta_fast import RobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta import (
      ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaForCausalLM,
      RobertaForMaskedLM,
      RobertaForMultipleChoice,
      RobertaForQuestionAnswering,
      RobertaForSequenceClassification,
      RobertaForTokenClassification,
      RobertaModel,
      RobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta import (
      TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaForCausalLM,
      TFRobertaForMaskedLM,
      TFRobertaForMultipleChoice,
      TFRobertaForQuestionAnswering,
      TFRobertaForSequenceClassification,
      TFRobertaForTokenClassification,
      TFRobertaMainLayer,
      TFRobertaModel,
      TFRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
      FlaxRobertaPreTrainedModel,
  )

else:
 import sys

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





