




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	torch.FloatTensor
     lowerCAmelCase    :	Optional[torch.FloatTensor]       		=  None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Optional[int]=0.999	,					__UpperCamelCase      :						Union[str, Any]="cosine"	,					)  ->							Tuple:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCamelCase      :						List[str]				):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCamelCase      :						int				):
            return math.exp(t * -12.0				)

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

    UpperCAmelCase_										=					[]
    for i in range(__UpperCamelCase				):
        UpperCAmelCase_										=					i / num_diffusion_timesteps
        UpperCAmelCase_										=					(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					(       _A     ,       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Any       		=  1
     @register_to_config
     def __init__(     self       :      List[Any] ,   __snake_case       :      int = 10_00 ,   __snake_case       :      float = 0.0_001 ,   __snake_case       :      float = 0.02 ,   __snake_case       :      str = "linear" ,   __snake_case       :      Optional[Union[np.ndarray, List[float]]] = None ,   __snake_case       :      bool = True ,   __snake_case       :      bool = True ,   __snake_case       :      int = 0 ,   __snake_case       :      str = "epsilon" ,   __snake_case       :      float = 1.0 ,   **__snake_case       :      List[Any] ,   ):
         if kwargs.get('''set_alpha_to_one''' ,   __snake_case    ) is not None:
             UpperCAmelCase_										=					(
                 '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
             )
             deprecate('''set_alpha_to_one''' ,   '''1.0.0''' ,   __snake_case ,   standard_warn=__snake_case    )
             UpperCAmelCase_										=					kwargs['''set_alpha_to_one''']
         if trained_betas is not None:
             UpperCAmelCase_										=					torch.tensor(__snake_case ,   dtype=torch.floataa    )
         elif beta_schedule == "linear":
             UpperCAmelCase_										=					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.
             UpperCAmelCase_										=					(
                 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
             UpperCAmelCase_										=					betas_for_alpha_bar(__snake_case    )
         else:
             raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'    )

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

         # At every step in inverted ddim, we are looking into the next alphas_cumprod
         # For the final step, there is no next alphas_cumprod, and the index is out of bounds
         # `set_alpha_to_zero` decides whether we set this parameter simply to zero
         # in this case, self.step() just output the predicted noise
         # or whether we use the final alpha of the "non-previous" one.
         UpperCAmelCase_										=					torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

         # standard deviation of the initial noise distribution
         UpperCAmelCase_										=					1.0

         # setable values
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					torch.from_numpy(np.arange(0 ,   __snake_case    ).copy().astype(np.intaa    )    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      torch.FloatTensor ,   __snake_case       :      Optional[int] = None    ):
         return sample
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      int ,   __snake_case       :      Union[str, torch.device] = None    ):

         if num_inference_steps > self.config.num_train_timesteps:
             raise ValueError(
                 F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                 F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                 F' maximal {self.config.num_train_timesteps} timesteps.'    )

         UpperCAmelCase_										=					num_inference_steps
         UpperCAmelCase_										=					self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         UpperCAmelCase_										=					(np.arange(0 ,   __snake_case    ) * step_ratio).round().copy().astype(np.intaa    )
         UpperCAmelCase_										=					torch.from_numpy(__snake_case    ).to(__snake_case    )
         self.timesteps += self.config.steps_offset
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      torch.FloatTensor ,   __snake_case       :      int ,   __snake_case       :      torch.FloatTensor ,   __snake_case       :      float = 0.0 ,   __snake_case       :      bool = False ,   __snake_case       :      Optional[torch.FloatTensor] = None ,   __snake_case       :      bool = True ,   ):
         # 1. get previous step value (=t+1)
         UpperCAmelCase_										=					timestep + self.config.num_train_timesteps // self.num_inference_steps

         # 2. compute alphas, betas
         # change original implementation to exactly match noise levels for analogous forward process
         UpperCAmelCase_										=					self.alphas_cumprod[timestep]
         UpperCAmelCase_										=					(
             self.alphas_cumprod[prev_timestep]
             if prev_timestep < self.config.num_train_timesteps
             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
         if self.config.prediction_type == "epsilon":
             UpperCAmelCase_										=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             UpperCAmelCase_										=					model_output
         elif self.config.prediction_type == "sample":
             UpperCAmelCase_										=					model_output
             UpperCAmelCase_										=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
             UpperCAmelCase_										=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             UpperCAmelCase_										=					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                 ''' `v_prediction`'''    )

         # 4. Clip or threshold "predicted x_0"
         if self.config.clip_sample:
             UpperCAmelCase_										=					pred_original_sample.clamp(
                 -self.config.clip_sample_range ,   self.config.clip_sample_range    )

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         UpperCAmelCase_										=					(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

         # 6. 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 not return_dict:
             return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=__snake_case ,   pred_original_sample=__snake_case    )




     def __len__(     self       :      Optional[Any]    ):
         return self.config.num_train_timesteps











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__)





