




"""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 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   int=BITS				)       ->   int:






      '''simple docstring'''


      _A     =  x.device

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

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

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






def 		_snake_case	(   _snake_case				:   Tuple							,    _snake_case				:   int=BITS				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  x.device

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

      _A     =  rearrange(_snake_case							,    'd -> d 1 1'				)
      _A     =  rearrange(_snake_case							,    '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 		_snake_case	(   self				:   Dict							,    _snake_case				:   torch.FloatTensor							,    _snake_case				:   int							,    _snake_case				:   torch.FloatTensor							,    _snake_case				:   float = 0.0							,    _snake_case				:   bool = True							,    _snake_case				:   str=None							,    _snake_case				:   bool = 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(_snake_case							,    -scale							,    _snake_case				)

      # 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(_snake_case							,    _snake_case				)
      _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(_snake_case				) else 'cpu'
            _A     =  torch.randn(model_output.shape							,    dtype=model_output.dtype							,    generator=_snake_case				).to(_snake_case				)
            _A     =  self._get_variance(_snake_case							,    _snake_case				) ** 0.5 * eta * noise

            _A     =  prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=_snake_case							,    pred_original_sample=_snake_case				)






def 		_snake_case	(   self				:   Tuple							,    _snake_case				:   torch.FloatTensor							,    _snake_case				:   int							,    _snake_case				:   torch.FloatTensor							,    _snake_case				:   Optional[int]="epsilon"							,    _snake_case				:   List[Any]=None							,    _snake_case				:   bool = 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(_snake_case							,    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(_snake_case							,    -scale							,    _snake_case				)

      # 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=_snake_case				).to(model_output.device				)
            _A     =  (self._get_variance(_snake_case							,    predicted_variance=_snake_case				) ** 0.5) * noise

      _A     =  pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=_snake_case							,    pred_original_sample=_snake_case				)







class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

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

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



 @torch.no_grad()
 def __call__(					self			:			List[str]       ,				_UpperCAmelCase			:			Optional[int] = 256       ,				_UpperCAmelCase			:			Optional[int] = 256       ,				_UpperCAmelCase			:			Optional[int] = 50       ,				_UpperCAmelCase			:			Optional[torch.Generator] = None       ,				_UpperCAmelCase			:			Optional[int] = 1       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       _A     =  torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)       ,				generator=_UpperCAmelCase       ,				)
       _A     =  decimal_to_bits(_UpperCAmelCase						) * self.bit_scale
       _A     =  latents.to(self.device						)

       self.scheduler.set_timesteps(_UpperCAmelCase						)

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

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

       _A     =  bits_to_decimal(_UpperCAmelCase						)

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

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)







"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'       ,				_UpperCAmelCase       ,				)
             _A     =  kwargs.pop('feature_extractor'						)

       _A     =  image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None and images is not None:
             _A     =  {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A     =  image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A     =  {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase						)       ,				tensor_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

