





'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __a			( _snake_case      ):
   __UpperCamelCase :			torch.FloatTensor
   __UpperCamelCase :			Optional[torch.FloatTensor]						   =						None





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=0.9_9_9				,						__UpperCAmelCase="cosine"				,						)    ->       Union[str, Any]:

   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase   ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__UpperCAmelCase   ):
         return math.exp(t * -1_2.0   )

   else:
      raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

   __SCREAMING_SNAKE_CASE   				=							[]
   for i in range(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							i / num_diffusion_timesteps
      __SCREAMING_SNAKE_CASE   				=							(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase   ) / alpha_bar_fn(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )
   return torch.tensor(__UpperCAmelCase				,						dtype=torch.floataa   )







class __a			( _snake_case, _snake_case      ):





   @register_to_config
   def __init__(       self					:      Tuple					,lowerCamelCase					:      int = 1000					,lowerCamelCase					:      str = "fixed_small_log"					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Optional[float] = 1.0					,lowerCamelCase					:      str = "epsilon"					,lowerCamelCase					:      str = "squaredcos_cap_v2"					,):

      '''simple docstring'''

      if beta_schedule != "squaredcos_cap_v2":
         raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""     )

      __SCREAMING_SNAKE_CASE   				=							betas_for_alpha_bar(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							1.0 - self.betas
      __SCREAMING_SNAKE_CASE   				=							torch.cumprod(self.alphas					,dim=0     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(1.0     )

      # standard deviation of the initial noise distribution
      __SCREAMING_SNAKE_CASE   				=							1.0

      # setable values
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(np.arange(0					,lowerCamelCase     )[::-1].copy()     )

      __SCREAMING_SNAKE_CASE   				=							variance_type





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      Optional[int] = None     ):

      '''simple docstring'''

      return sample





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, torch.device] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							num_inference_steps
      __SCREAMING_SNAKE_CASE   				=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      __SCREAMING_SNAKE_CASE   				=							(np.arange(0					,lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     ).to(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]=None					,lowerCamelCase					:      List[str]=None					,lowerCamelCase					:      Any=None     ):

      '''simple docstring'''

      if prev_timestep is None:
         __SCREAMING_SNAKE_CASE   				=							t - 1

      __SCREAMING_SNAKE_CASE   				=							self.alphas_cumprod[t]
      __SCREAMING_SNAKE_CASE   				=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      __SCREAMING_SNAKE_CASE   				=							1 - alpha_prod_t
      __SCREAMING_SNAKE_CASE   				=							1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
         __SCREAMING_SNAKE_CASE   				=							self.betas[t]
      else:
         __SCREAMING_SNAKE_CASE   				=							1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      __SCREAMING_SNAKE_CASE   				=							beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
         __SCREAMING_SNAKE_CASE   				=							self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
         __SCREAMING_SNAKE_CASE   				=							torch.log(torch.clamp(lowerCamelCase					,min=1E-2_0     )     )
         __SCREAMING_SNAKE_CASE   				=							torch.exp(0.5 * variance     )
      elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         __SCREAMING_SNAKE_CASE   				=							variance.log()
         __SCREAMING_SNAKE_CASE   				=							beta.log()

         __SCREAMING_SNAKE_CASE   				=							(predicted_variance + 1) / 2
         __SCREAMING_SNAKE_CASE   				=							frac * max_log + (1 - frac) * min_log

      return variance





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      int					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Tuple=None					,lowerCamelCase					:      bool = True					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							timestep

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

      # 1. compute alphas, betas
      if prev_timestep is None:
         __SCREAMING_SNAKE_CASE   				=							t - 1

      __SCREAMING_SNAKE_CASE   				=							self.alphas_cumprod[t]
      __SCREAMING_SNAKE_CASE   				=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      __SCREAMING_SNAKE_CASE   				=							1 - alpha_prod_t
      __SCREAMING_SNAKE_CASE   				=							1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
         __SCREAMING_SNAKE_CASE   				=							self.betas[t]
         __SCREAMING_SNAKE_CASE   				=							self.alphas[t]
      else:
         __SCREAMING_SNAKE_CASE   				=							1 - alpha_prod_t / alpha_prod_t_prev
         __SCREAMING_SNAKE_CASE   				=							1 - beta

      # 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 self.config.prediction_type == "epsilon":
         __SCREAMING_SNAKE_CASE   				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
         __SCREAMING_SNAKE_CASE   				=							model_output
      else:
         raise ValueError(
             f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             """ for the UnCLIPScheduler."""     )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         __SCREAMING_SNAKE_CASE   				=							torch.clamp(
             lowerCamelCase					,-self.config.clip_sample_range					,self.config.clip_sample_range     )

      # 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
      __SCREAMING_SNAKE_CASE   				=							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      __SCREAMING_SNAKE_CASE   				=							alpha ** 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
      __SCREAMING_SNAKE_CASE   				=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      __SCREAMING_SNAKE_CASE   				=							0
      if t > 0:
         __SCREAMING_SNAKE_CASE   				=							randn_tensor(
             model_output.shape					,dtype=model_output.dtype					,generator=lowerCamelCase					,device=model_output.device     )

         __SCREAMING_SNAKE_CASE   				=							self._get_variance(
             lowerCamelCase					,predicted_variance=lowerCamelCase					,prev_timestep=lowerCamelCase					,)

         if self.variance_type == "fixed_small_log":
            __SCREAMING_SNAKE_CASE   				=							variance
         elif self.variance_type == "learned_range":
            __SCREAMING_SNAKE_CASE   				=							(0.5 * variance).exp()
         else:
            raise ValueError(
                f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                """ for the UnCLIPScheduler."""     )

         __SCREAMING_SNAKE_CASE   				=							variance * variance_noise

      __SCREAMING_SNAKE_CASE   				=							pred_prev_sample + variance

      if not return_dict:
         return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase					,pred_original_sample=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.IntTensor					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.alphas_cumprod.to(device=original_samples.device					,dtype=original_samples.dtype     )
      __SCREAMING_SNAKE_CASE   				=							timesteps.to(original_samples.device     )

      __SCREAMING_SNAKE_CASE   				=							alphas_cumprod[timesteps] ** 0.5
      __SCREAMING_SNAKE_CASE   				=							sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape     ) < len(original_samples.shape     ):
         __SCREAMING_SNAKE_CASE   				=							sqrt_alpha_prod.unsqueeze(-1     )

      __SCREAMING_SNAKE_CASE   				=							(1 - alphas_cumprod[timesteps]) ** 0.5
      __SCREAMING_SNAKE_CASE   				=							sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
         __SCREAMING_SNAKE_CASE   				=							sqrt_one_minus_alpha_prod.unsqueeze(-1     )

      __SCREAMING_SNAKE_CASE   				=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples











"""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

