






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        _snake_case    (      __lowerCAmelCase			):
	_A 							= 42
	_A 							= None





def        lowercase_			(     __snake_case  :				Dict						,						__snake_case  :				Optional[Any]=0.9_9_9						,						__snake_case  :				Dict="cosine"						,						)  ->			Any:





				'''simple docstring'''


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__snake_case  :				List[str]   ):
												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(__snake_case  :				int   ):
												return math.exp(t * -1_2.0   )

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

				snake_case__   :Optional[Any]       = []
				for i in range(_UpperCamelCase   ):
								snake_case__   :Any       = i / num_diffusion_timesteps
								snake_case__   :Any       = (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        _snake_case    (      __lowerCAmelCase							,     __lowerCAmelCase			):
	_A 							= 1






	@register_to_config
	def __init__(		self       ,UpperCamelCase = 1_000       ,UpperCamelCase = 0.0001       ,UpperCamelCase = 0.02       ,UpperCamelCase = "linear"       ,UpperCamelCase = None       ,UpperCamelCase = True       ,UpperCamelCase = True       ,UpperCamelCase = 0       ,UpperCamelCase = "epsilon"       ,UpperCamelCase = 1.0       ,**UpperCamelCase       ,)    ->     Dict:
					if kwargs.get("set_alpha_to_one"       ,_SCREAMING_SNAKE_CASE							) is not None:
									snake_case__   :Union[str, Any]       = (
									    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
									)
									deprecate("set_alpha_to_one"       ,"1.0.0"       ,_SCREAMING_SNAKE_CASE       ,standard_warn=_SCREAMING_SNAKE_CASE							)
									snake_case__   :Optional[int]       = kwargs["set_alpha_to_one"]
					if trained_betas is not None:
									snake_case__   :Tuple       = torch.tensor(_SCREAMING_SNAKE_CASE       ,dtype=torch.floataa							)
					elif beta_schedule == "linear":
									snake_case__   :Any       = torch.linspace(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,dtype=torch.floataa							)
					elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									snake_case__   :Union[str, Any]       = (
									    torch.linspace(beta_start**0.5       ,beta_end**0.5       ,_SCREAMING_SNAKE_CASE       ,dtype=torch.floataa							) ** 2
									)
					elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									snake_case__   :List[str]       = betas_for_alpha_bar(_SCREAMING_SNAKE_CASE							)
					else:
									raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'							)

					snake_case__   :Optional[Any]       = 1.0 - self.betas
					snake_case__   :Optional[int]       = 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.
					snake_case__   :Dict       = torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

					# standard deviation of the initial noise distribution
					snake_case__   :Tuple       = 1.0

					# setable values
					snake_case__   :Optional[int]       = None
					snake_case__   :Optional[int]       = torch.from_numpy(np.arange(0       ,_SCREAMING_SNAKE_CASE							).copy().astype(np.intaa							)							)






	def  lowerCAmelCase_				(		self       ,UpperCamelCase       ,UpperCamelCase = None							)    ->     torch.FloatTensor:
					return sample






	def  lowerCAmelCase_				(		self       ,UpperCamelCase       ,UpperCamelCase = None							)    ->     Dict:

					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.'							)

					snake_case__   :List[Any]       = num_inference_steps
					snake_case__   :Optional[int]       = 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
					snake_case__   :str       = (np.arange(0       ,_SCREAMING_SNAKE_CASE							) * step_ratio).round().copy().astype(np.intaa							)
					snake_case__   :List[str]       = torch.from_numpy(_SCREAMING_SNAKE_CASE							).to(_SCREAMING_SNAKE_CASE							)
					self.timesteps += self.config.steps_offset






	def  lowerCAmelCase_				(		self       ,UpperCamelCase       ,UpperCamelCase       ,UpperCamelCase       ,UpperCamelCase = 0.0       ,UpperCamelCase = False       ,UpperCamelCase = None       ,UpperCamelCase = True       ,)    ->     Union[DDIMSchedulerOutput, Tuple]:
					# 1. get previous step value (=t+1)
					snake_case__   :str       = 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
					snake_case__   :int       = self.alphas_cumprod[timestep]
					snake_case__   :List[str]       = (
					    self.alphas_cumprod[prev_timestep]
					    if prev_timestep < self.config.num_train_timesteps
					    else self.final_alpha_cumprod
					)

					snake_case__   :List[Any]       = 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":
									snake_case__   :List[str]       = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									snake_case__   :str       = model_output
					elif self.config.prediction_type == "sample":
									snake_case__   :Optional[Any]       = model_output
									snake_case__   :Optional[int]       = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
									snake_case__   :List[Any]       = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									snake_case__   :Optional[Any]       = (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:
									snake_case__   :int       = 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
					snake_case__   :Union[str, Any]       = (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
					snake_case__   :Optional[int]       = 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=_SCREAMING_SNAKE_CASE       ,pred_original_sample=_SCREAMING_SNAKE_CASE							)






	def __len__(		self							)    ->     Dict:
					return self.config.num_train_timesteps





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_		:     Optional[Any]          =		{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		[
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   UpperCamelCase_		:     int          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


