

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
class 		__magic_name__     (				_UpperCAmelCase				):

   '''simple docstring'''




   lowerCamelCase__					:      torch.FloatTensor
   lowerCamelCase__					:      torch.FloatTensor





def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					List[Any]=0.999      , _A :					Any="cosine"      , ):





  '''simple docstring'''







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_A :					Union[str, Any]				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_A :					Any				):
      return math.exp(t * -12.0				)

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

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







class 		__magic_name__     (				_UpperCAmelCase						,			_UpperCAmelCase				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[Any]        =  1



   @register_to_config
   def __init__(							self,       lowercase_ = 1000,       lowercase_ = 0.0001,       lowercase_ = 0.02,       lowercase_ = "linear",       lowercase_ = 0.0,       lowercase_ = None,       lowercase_ = True,       )				->       int:






     """simple docstring"""






     if trained_betas is not None:
       a__    =torch.from_numpy(snake_case_			)
     elif beta_schedule == "linear":
       a__    =torch.linspace(snake_case_,       snake_case_,       snake_case_,       dtype=torch.floataa			)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       a__    =(
           torch.linspace(beta_start**0.5,       beta_end**0.5,       snake_case_,       dtype=torch.floataa			) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       a__    =betas_for_alpha_bar(snake_case_			)
     elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       a__    =torch.linspace(-6,       6,       snake_case_			)
       a__    =torch.sigmoid(snake_case_			) * (beta_end - beta_start) + beta_start
     else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""			)

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

     a__    =torch.tensor(1.0			)

     # standard deviation of the initial noise distribution
     a__    =1.0

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

     a__    =eta



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None			)				->       Dict:






     """simple docstring"""






     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = 10,       lowercase_ = 10,       lowercase_ = None,       )				->       Any:






     """simple docstring"""






     a__    =min(self.config.num_train_timesteps,       snake_case_			)
     a__    =num_inference_steps

     a__    =[]

     a__    ={}
     for j in range(0,       num_inference_steps - jump_length,       snake_case_			):
       a__    =jump_n_sample - 1

     a__    =num_inference_steps
     while t >= 1:
       a__    =t - 1
       timesteps.append(snake_case_			)

       if jumps.get(snake_case_,       0			) > 0:
         a__    =jumps[t] - 1
         for _ in range(snake_case_			):
           a__    =t + 1
           timesteps.append(snake_case_			)

     a__    =np.array(snake_case_			) * (self.config.num_train_timesteps // self.num_inference_steps)
     a__    =torch.from_numpy(snake_case_			).to(snake_case_			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Tuple:






     """simple docstring"""






     a__    =t - self.config.num_train_timesteps // self.num_inference_steps

     a__    =self.alphas_cumprod[t]
     a__    =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     a__    =1 - alpha_prod_t
     a__    =1 - 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
     # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
     # without eta.
     # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
     a__    =(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_ = None,       lowercase_ = True,       )				->       Dict:






     """simple docstring"""






     a__    =timestep
     a__    =timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 1. compute alphas, betas
     a__    =self.alphas_cumprod[t]
     a__    =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     a__    =1 - alpha_prod_t

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     a__    =(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       a__    =torch.clamp(snake_case_,       -1,       1			)

     # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
     # substitute formula (7) in the algorithm coming from DDPM paper
     # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
     # DDIM schedule gives the same results as DDPM with eta = 1.0
     # Noise is being reused in 7. and 8., but no impact on quality has
     # been observed.

     # 5. Add noise
     a__    =model_output.device
     a__    =randn_tensor(model_output.shape,       generator=snake_case_,       device=snake_case_,       dtype=model_output.dtype			)
     a__    =self.eta * self._get_variance(snake_case_			) ** 0.5

     a__    =0
     if t > 0 and self.eta > 0:
       a__    =std_dev_t * noise

     # 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-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     a__    =alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

     # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
     a__    =(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

     # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
     a__    =mask * prev_known_part + (1.0 - mask) * prev_unknown_part

     if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

     return RePaintSchedulerOutput(prev_sample=snake_case_,       pred_original_sample=snake_case_			)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_=None			)				->       Optional[int]:






     """simple docstring"""






     a__    =self.config.num_train_timesteps // self.num_inference_steps

     for i in range(snake_case_			):
       a__    =self.betas[timestep + i]
       if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         a__    =randn_tensor(sample.shape,       dtype=sample.dtype,       generator=snake_case_			)
         a__    =noise.to(sample.device			)
       else:
         a__    =randn_tensor(sample.shape,       generator=snake_case_,       device=sample.device,       dtype=sample.dtype			)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       a__    =(1 - beta) ** 0.5 * sample + beta**0.5 * noise

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       )				->       Dict:






     """simple docstring"""






     raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''			)



   def __len__(							self			)				->       List[str]:






     """simple docstring"""






     return self.config.num_train_timesteps

'''simple docstring'''

import warnings

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




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				Union[str, Any]   							=       """VideoMAEImageProcessor"""
			__magic_name__ :				List[str]   							=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       int				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       Optional[int]				=    kwargs.pop("feature_extractor"					)

									snake_case :       Union[str, Any]				=    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__(snake_case_				,			snake_case_					)
									snake_case :       List[Any]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and videos is None:
															raise ValueError("You have to specify either text or videos. Both cannot be none."					)

									if text is not None:
															snake_case :       List[Any]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if videos is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and videos is not None:
															snake_case :       Tuple				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





