





"""simple docstring"""





# 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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0.999  ,			UpperCAmelCase_="cosine"  ,			)						->Any:





  """simple docstring"""


  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase_  ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

  elif alpha_transform_type == "exp":

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

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

  __UpperCAmelCase    :						int       =						[]
  for i in range(UpperCAmelCase_  ):
    __UpperCAmelCase    :						List[Any]       =						i / num_diffusion_timesteps
    __UpperCAmelCase    :						Optional[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							(			__UpperCAmelCase      ,  __UpperCAmelCase ):

      '''simple docstring'''



      _A     :       int         =       1

      @register_to_config
      def __init__(							self       :      Tuple						,				__lowercase       :      int = 1_000						,				__lowercase       :      float = 0.0_0_0_1						,				__lowercase       :      float = 0.0_2						,				__lowercase       :      str = "linear"						,				__lowercase       :      Optional[Union[np.ndarray, List[float]]] = None						,				__lowercase       :      bool = True						,				__lowercase       :      bool = True						,				__lowercase       :      int = 0						,				__lowercase       :      str = "epsilon"						,				__lowercase       :      float = 1.0						,				**__lowercase       :      List[Any]						,				):


        '''simple docstring'''




        if kwargs.get('''set_alpha_to_one'''						,				__lowercase ) is not None:
          __UpperCAmelCase    :						Dict       =						(
              '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
          )
          deprecate('''set_alpha_to_one'''						,				'''1.0.0'''						,				__lowercase						,				standard_warn=__lowercase )
          __UpperCAmelCase    :						Optional[Any]       =						kwargs['''set_alpha_to_one''']
        if trained_betas is not None:
          __UpperCAmelCase    :						Tuple       =						torch.tensor(__lowercase						,				dtype=torch.floataa )
        elif beta_schedule == "linear":
          __UpperCAmelCase    :						Any       =						torch.linspace(__lowercase						,				__lowercase						,				__lowercase						,				dtype=torch.floataa )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          __UpperCAmelCase    :						Tuple       =						(
              torch.linspace(beta_start**0.5						,				beta_end**0.5						,				__lowercase						,				dtype=torch.floataa ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          __UpperCAmelCase    :						int       =						betas_for_alpha_bar(__lowercase )
        else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

        __UpperCAmelCase    :						Union[str, Any]       =						1.0 - self.betas
        __UpperCAmelCase    :						Optional[Any]       =						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    :						Optional[Any]       =						torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        __UpperCAmelCase    :						Any       =						1.0

        # setable values
        __UpperCAmelCase    :						Tuple       =						None
        __UpperCAmelCase    :						Tuple       =						torch.from_numpy(np.arange(0						,				__lowercase ).copy().astype(np.intaa ) )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      Optional[int] = None ):


        '''simple docstring'''




        return sample

      def       A_					(							self       :      Tuple						,				__lowercase       :      int						,				__lowercase       :      Union[str, torch.device] = None ):


        '''simple docstring'''




        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    :						Dict       =						num_inference_steps
        __UpperCAmelCase    :						List[Any]       =						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    :						Optional[Any]       =						(np.arange(0						,				__lowercase ) * step_ratio).round().copy().astype(np.intaa )
        __UpperCAmelCase    :						Tuple       =						torch.from_numpy(__lowercase ).to(__lowercase )
        self.timesteps += self.config.steps_offset

      def       A_					(							self       :      List[str]						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      int						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      float = 0.0						,				__lowercase       :      bool = False						,				__lowercase       :      Optional[torch.FloatTensor] = None						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						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    :						int       =						self.alphas_cumprod[timestep]
        __UpperCAmelCase    :						Optional[Any]       =						(
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

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







      def __len__(							self       :      str ):


        '''simple docstring'''




        return self.config.num_train_timesteps






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				:int				   =       logging.get_logger(__name__)

lowercase__				:List[str]				   =       {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       'gpt_neox'

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any=50_432						,				__lowercase       :      int=6_144						,				__lowercase       :      List[str]=44						,				__lowercase       :      List[Any]=64						,				__lowercase       :      int=24_576						,				__lowercase       :      List[str]="gelu"						,				__lowercase       :      Optional[int]=0.2_5						,				__lowercase       :      Any=10_000						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      Union[str, Any]=0.1						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      Union[str, Any]=0.0_2						,				__lowercase       :      Optional[int]=1e-5						,				__lowercase       :      str=True						,				__lowercase       :      str=0						,				__lowercase       :      Tuple=2						,				__lowercase       :      Tuple=False						,				__lowercase       :      int=True						,				__lowercase       :      Any=None						,				**__lowercase       :      List[str]						,				):


        '''simple docstring'''




        super().__init__(bos_token_id=__lowercase						,				eos_token_id=__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						int       =						vocab_size
        __UpperCAmelCase    :						Union[str, Any]       =						max_position_embeddings
        __UpperCAmelCase    :						List[str]       =						hidden_size
        __UpperCAmelCase    :						Tuple       =						num_hidden_layers
        __UpperCAmelCase    :						Any       =						num_attention_heads
        __UpperCAmelCase    :						Union[str, Any]       =						intermediate_size
        __UpperCAmelCase    :						str       =						hidden_act
        __UpperCAmelCase    :						str       =						rotary_pct
        __UpperCAmelCase    :						str       =						rotary_emb_base
        __UpperCAmelCase    :						Dict       =						attention_dropout
        __UpperCAmelCase    :						str       =						hidden_dropout
        __UpperCAmelCase    :						Any       =						classifier_dropout
        __UpperCAmelCase    :						Tuple       =						initializer_range
        __UpperCAmelCase    :						Any       =						layer_norm_eps
        __UpperCAmelCase    :						Optional[int]       =						use_cache
        __UpperCAmelCase    :						List[Any]       =						tie_word_embeddings
        __UpperCAmelCase    :						Any       =						use_parallel_residual
        __UpperCAmelCase    :						List[str]       =						rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
          raise ValueError(
              '''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )







      def       A_					(							self       :      int ):


        '''simple docstring'''




        if self.rope_scaling is None:
          return

        if not isinstance(self.rope_scaling						,				__lowercase ) or len(self.rope_scaling ) != 2:
          raise ValueError(
              '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
              f'''got {self.rope_scaling}''' )
        __UpperCAmelCase    :						Union[str, Any]       =						self.rope_scaling.get('''type'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.rope_scaling.get('''factor'''						,				__lowercase )
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
          raise ValueError(
              f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
        if rope_scaling_factor is None or not isinstance(__lowercase						,				__lowercase ) or rope_scaling_factor <= 1.0:
          raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )