"""simple docstring"""







import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[Any]    =    ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      assert Path(hparams.data_dir	).exists()
      __lowerCamelCase    		=					Path(hparams.output_dir	)
      self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.output_dir.joinpath('student'	)

      __lowerCamelCase    		=					str(_SCREAMING_SNAKE_CASE	)  # Tell lightning we are training the student
      __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher	).eval()
      use_task_specific_params(_SCREAMING_SNAKE_CASE ,				hparams.task	)  # We copy good generation parameters to student by default
      if hparams.student is not None:
         __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.student	)
         use_task_specific_params(_SCREAMING_SNAKE_CASE ,				hparams.task	)
         __lowerCamelCase		,__lowerCamelCase    		=					None, None
      else:
         __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					create_student_by_copying_alternating_layers(
             _SCREAMING_SNAKE_CASE ,				e=hparams.student_encoder_layers ,				d=hparams.student_decoder_layers ,				save_path=_SCREAMING_SNAKE_CASE	)

      if hparams.length_penalty != -1:
         __lowerCamelCase    		=					hparams.length_penalty
      __lowerCamelCase    		=					hparams.teacher  # Use teacher's tokenizer
      super().__init__(_SCREAMING_SNAKE_CASE ,				model=_SCREAMING_SNAKE_CASE ,				config=student.config	)
      assert student.config.model_type == teacher.config.model_type, (
          F'''teacher, student model types should be the same, got {student.config.model_type} !='''
          F''' {teacher.config.model_type}'''
      )

      if student.config.model_type == "t5":
         __lowerCamelCase    		=					len(student.get_encoder().block	)
         __lowerCamelCase    		=					len(student.get_decoder().block	)
         __lowerCamelCase    		=					len(teacher.get_encoder().block	)
         __lowerCamelCase    		=					len(teacher.get_decoder().block	)
      else:
         __lowerCamelCase    		=					student.config.encoder_layers
         __lowerCamelCase    		=					student.config.decoder_layers
         __lowerCamelCase    		=					teacher.config.encoder_layers
         __lowerCamelCase    		=					teacher.config.decoder_layers

      __lowerCamelCase    		=					not (hparams.student is None or hparams.teacher == hparams.student)
      __lowerCamelCase    		=					(not self.different_base_models) and hparams.alpha_hid > 0
      __lowerCamelCase    		=					self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      __lowerCamelCase    		=					teacher
      freeze_params(self.teacher	)

      if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
         try:
            del self.teacher.model.encoder
         except AttributeError:  # T5
            del self.teacher.encoder

      if e_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)
      if d_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)

      __lowerCamelCase		,__lowerCamelCase    		=					e_layer_ids, d_layer_ids  # type: List[int], List[int]

      if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
         if hparams.supervise_forward:
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.e_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.d_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
         else:  # student layer should emulate hidden states of the teacher layer it was copied from
            __lowerCamelCase    		=					self.e_layer_ids
            __lowerCamelCase    		=					self.d_layer_ids
      else:
         __lowerCamelCase    		=					None
         __lowerCamelCase    		=					None

      __lowerCamelCase    		=					nn.KLDivLoss(reduction='batchmean'	)
      __lowerCamelCase    		=					2.0
      __lowerCamelCase    		=					hparams.alpha_mlm
      __lowerCamelCase    		=					hparams.alpha_ce
      __lowerCamelCase    		=					hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					s_logits.size(-1	)
      __lowerCamelCase    		=					torch.masked_select(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					torch.masked_select(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					s_logits_slct.view(-1 ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					t_logits_slct.view(-1 ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      __lowerCamelCase    		=					(
          self.ce_loss_fct(
              nn.functional.log_softmax(s_logits_slct / self.temperature ,				dim=-1	) ,				nn.functional.softmax(t_logits_slct / self.temperature ,				dim=-1	) ,				)
          * (self.temperature) ** 2
      )
      return loss_ce







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      add_distill_args(_SCREAMING_SNAKE_CASE	)
      return parser







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.tokenizer.pad_token_id
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.model._shift_right(_SCREAMING_SNAKE_CASE	)
      else:
         __lowerCamelCase    		=					shift_tokens_right(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      # noinspection PyCallingNonCallable
      __lowerCamelCase    		=					self(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				output_attentions=_SCREAMING_SNAKE_CASE ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					student_outputs['logits']

      # Same cross entropy vs. label smoothing logic as finetune.py
      assert lm_logits.shape[-1] == self.model.config.vocab_size
      if self.hparams.label_smoothing == 0:
         # Same behavior as modeling_bart.py, besides ignoring pad_token_id
         __lowerCamelCase    		=					nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					loss_fct(lm_logits.view(-1 ,				lm_logits.shape[-1]	) ,				labels.view(-1	)	)
      else:
         __lowerCamelCase    		=					nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,				dim=-1	)
         __lowerCamelCase		,__lowerCamelCase    		=					label_smoothed_nll_loss(
             _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				self.hparams.label_smoothing ,				ignore_index=_SCREAMING_SNAKE_CASE	)

      def zero_tensor():
         return torch.tensor(0.0	).type_as(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					student_outputs[
          'encoder_last_hidden_state'
      ]  # use this unless self.different_base_models
      __lowerCamelCase		,__lowerCamelCase    		=					zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
         __lowerCamelCase    		=					self.teacher.get_encoder()(
             _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				)
         if self.different_base_models:
            __lowerCamelCase    		=					all_teacher_encoder_outputs['last_hidden_state']
         elif self.do_calc_hidden_loss:
            __lowerCamelCase    		=					self.calc_hidden_loss(
                _SCREAMING_SNAKE_CASE ,				student_outputs['encoder_hidden_states'] ,				all_teacher_encoder_outputs['hidden_states'] ,				self.e_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

      __lowerCamelCase    		=					self.teacher(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				encoder_outputs=(teacher_enc_outputs,) ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					decoder_input_ids.ne(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					self.calc_ce_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				teacher_outputs['logits']	)
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
         __lowerCamelCase    		=					self.calc_hidden_loss(
             _SCREAMING_SNAKE_CASE ,				student_outputs['decoder_hidden_states'] ,				teacher_outputs['decoder_hidden_states'] ,				self.d_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

      __lowerCamelCase    		=					(
          self.alpha_ce * loss_ce
          + self.alpha_mlm * student_lm_loss
          + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
      )
      return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					'expected list or tuple for hidden_states, got tensor of shape: '
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states.shape}'''
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states_T.shape}'''
      __lowerCamelCase    		=					attention_mask.to(hidden_states[0]	)
      __lowerCamelCase    		=					mask.sum() * hidden_states[0].size(-1	)
      __lowerCamelCase    		=					torch.stack([hidden_states[i] for i in range(len(_SCREAMING_SNAKE_CASE	)	)]	)
      __lowerCamelCase    		=					torch.stack([hidden_states_T[j] for j in matches]	)
      assert student_states.shape == teacher_states.shape, F'''{student_states.shape} != {teacher_states.shape}'''
      if normalize_hidden:
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				student_states.shape[1:]	)
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				teacher_states.shape[1:]	)
      __lowerCamelCase    		=					nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				reduction='none'	)
      __lowerCamelCase    		=					(mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
      return masked_mse



def       A__    (	lowercase_     :	List[Any]						)  ->							List[Any]:
   # NOTE: if --student argument was specified and the teacher and student base models
   # are different, the models still have to have the same tokenizer, specified by
   # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
   # from bart to t5. This s because if the tokenizers are different, the output space
   # for the two models is also different and their logits are not comparable.
   parser.add_argument('--teacher'      , type=lowercase_						)
   parser.add_argument('--alpha_ce'      , default=0.8      , type=lowercase_						)
   parser.add_argument('--alpha_mlm'      , default=0.2      , type=lowercase_						)
   parser.add_argument('--alpha_hid'      , default=0.0      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student'      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_decoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_encoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--no_teacher'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--length_penalty'      , type=lowercase_      , default=-1						)
   parser.add_argument('--supervise_forward'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--normalize_hidden'      , action='store_true'      , default=lowercase_						)






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    "translation"
   __UpperCAmelCase	:							List[Any]    =    ["bleu"]
   __UpperCAmelCase	:							Dict    =    "bleu"







   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      __lowerCamelCase    		=					hparams.src_lang
      __lowerCamelCase    		=					hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.tokenizer.lang_code_to_id[hparams.tgt_lang]







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return calculate_bleu(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      add_distill_args(_SCREAMING_SNAKE_CASE	)
      return parser



def       A__    (	lowercase_     :	str						)  ->							Any:
   if args.no_teacher:
      __lowerCamelCase    		=					TranslationModule if 'translation' in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      __lowerCamelCase    		=					TranslationDistiller if 'translation' in args.task else SummarizationDistiller
   __lowerCamelCase    		=					module_cls.__name__
   print(F'''using module {args.setup_cls}'''						)
   __lowerCamelCase    		=					module_cls(lowercase_						)
   return model



def       A__    (	lowercase_     :	Dict						)  ->							List[Any]:
   Path(args.output_dir						).mkdir(exist_ok=lowercase_						)
   check_output_dir(lowercase_      , expected_items=3						)

   __lowerCamelCase    		=					create_module(lowercase_						)
   return ft_main(lowercase_      , model=lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Any   =   argparse.ArgumentParser()
     _SCREAMING_SNAKE_CASE	:		List[Any]   =   pl.Trainer.add_argparse_args(parser)
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
     _SCREAMING_SNAKE_CASE	:		Any   =   parser.parse_args()

     distill_main(args)







"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def       A__    (	lowercase_     :	Any      , lowercase_     :	int=0.9_9_9      , lowercase_     :	Dict="cosine"      , )  ->							Optional[int]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowercase_     :	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(lowercase_     :	Union[str, Any]						):
         return math.exp(t * -1_2.0						)

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

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






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							int    =    [e.name for e in KarrasDiffusionSchedulers]
   __UpperCAmelCase	:							Tuple    =    2







   @register_to_config
   def __init__(	self ,				_SCREAMING_SNAKE_CASE = 1000 ,				_SCREAMING_SNAKE_CASE = 0.0_0085 ,				_SCREAMING_SNAKE_CASE = 0.012 ,				_SCREAMING_SNAKE_CASE = "linear" ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = "epsilon" ,				_SCREAMING_SNAKE_CASE = "linspace" ,				_SCREAMING_SNAKE_CASE = 0 ,				):


      '''simple docstring'''

      if trained_betas is not None:
         __lowerCamelCase    		=					torch.tensor(_SCREAMING_SNAKE_CASE ,				dtype=torch.floataa	)
      elif beta_schedule == "linear":
         __lowerCamelCase    		=					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.
         __lowerCamelCase    		=					(
             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
         __lowerCamelCase    		=					betas_for_alpha_bar(_SCREAMING_SNAKE_CASE	)
      else:
         raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

      #  set all values
      self.set_timesteps(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      if schedule_timesteps is None:
         __lowerCamelCase    		=					self.timesteps

      __lowerCamelCase    		=					(schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter	) == 0:
         __lowerCamelCase    		=					1 if len(_SCREAMING_SNAKE_CASE	) > 1 else 0
      else:
         __lowerCamelCase    		=					timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE	) else timestep
         __lowerCamelCase    		=					self._index_counter[timestep_int]

      return indices[pos].item()







   @property
   def    _A				(	self	):


      '''simple docstring'''

      if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.index_for_timestep(_SCREAMING_SNAKE_CASE	)

      if self.state_in_first_order:
         __lowerCamelCase    		=					self.sigmas[step_index]
      else:
         __lowerCamelCase    		=					self.sigmas_interpol[step_index - 1]

      __lowerCamelCase    		=					sample / ((sigma**2 + 1) ** 0.5)
      return sample







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					num_inference_steps

      __lowerCamelCase    		=					num_train_timesteps or self.config.num_train_timesteps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
         __lowerCamelCase    		=					np.linspace(0 ,				num_train_timesteps - 1 ,				_SCREAMING_SNAKE_CASE ,				dtype=_SCREAMING_SNAKE_CASE	)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         __lowerCamelCase    		=					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
         __lowerCamelCase    		=					(np.arange(0 ,				_SCREAMING_SNAKE_CASE	) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE	)
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         __lowerCamelCase    		=					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
         __lowerCamelCase    		=					(np.arange(_SCREAMING_SNAKE_CASE ,				0 ,				-step_ratio	)).round().copy().astype(_SCREAMING_SNAKE_CASE	)
         timesteps -= 1
      else:
         raise ValueError(
             F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

      __lowerCamelCase    		=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
      __lowerCamelCase    		=					torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE	)	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					np.interp(_SCREAMING_SNAKE_CASE ,				np.arange(0 ,				len(_SCREAMING_SNAKE_CASE	)	) ,				_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
      __lowerCamelCase    		=					torch.from_numpy(_SCREAMING_SNAKE_CASE	).to(device=_SCREAMING_SNAKE_CASE	)

      # compute up and down sigmas
      __lowerCamelCase    		=					sigmas.roll(-1	)
      __lowerCamelCase    		=					0.0
      __lowerCamelCase    		=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
      __lowerCamelCase    		=					(sigmas_next**2 - sigmas_up**2) ** 0.5
      __lowerCamelCase    		=					0.0

      # compute interpolated sigmas
      __lowerCamelCase    		=					sigmas.log().lerp(sigmas_down.log() ,				0.5	).exp()
      __lowerCamelCase    		=					0.0

      # set sigmas
      __lowerCamelCase    		=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
      __lowerCamelCase    		=					torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
      __lowerCamelCase    		=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
      __lowerCamelCase    		=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

      if str(_SCREAMING_SNAKE_CASE	).startswith('mps'	):
         # mps does not support float64
         __lowerCamelCase    		=					torch.from_numpy(_SCREAMING_SNAKE_CASE	).to(_SCREAMING_SNAKE_CASE ,				dtype=torch.floataa	)
      else:
         __lowerCamelCase    		=					torch.from_numpy(_SCREAMING_SNAKE_CASE	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.sigma_to_t(_SCREAMING_SNAKE_CASE	).to(_SCREAMING_SNAKE_CASE ,				dtype=timesteps.dtype	)
      __lowerCamelCase    		=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,				dim=-1	).flatten()

      __lowerCamelCase    		=					torch.cat([timesteps[:1], interleaved_timesteps]	)

      __lowerCamelCase    		=					None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      __lowerCamelCase    		=					defaultdict(_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					sigma.log()

      # get distribution
      __lowerCamelCase    		=					log_sigma - self.log_sigmas[:, None]

      # get sigmas range
      __lowerCamelCase    		=					dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
      __lowerCamelCase    		=					low_idx + 1

      __lowerCamelCase    		=					self.log_sigmas[low_idx]
      __lowerCamelCase    		=					self.log_sigmas[high_idx]

      # interpolate sigmas
      __lowerCamelCase    		=					(low - log_sigma) / (low - high)
      __lowerCamelCase    		=					w.clamp(0 ,				1	)

      # transform interpolation to time range
      __lowerCamelCase    		=					(1 - w) * low_idx + w * high_idx
      __lowerCamelCase    		=					t.view(sigma.shape	)
      return t







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.sample is None







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = True ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.index_for_timestep(_SCREAMING_SNAKE_CASE	)

      # advance index counter by 1
      __lowerCamelCase    		=					timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE	) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
         __lowerCamelCase    		=					self.sigmas[step_index]
         __lowerCamelCase    		=					self.sigmas_interpol[step_index]
         __lowerCamelCase    		=					self.sigmas_up[step_index]
         __lowerCamelCase    		=					self.sigmas_down[step_index - 1]
      else:
         # 2nd order / KPDM2's method
         __lowerCamelCase    		=					self.sigmas[step_index - 1]
         __lowerCamelCase    		=					self.sigmas_interpol[step_index - 1]
         __lowerCamelCase    		=					self.sigmas_up[step_index - 1]
         __lowerCamelCase    		=					self.sigmas_down[step_index - 1]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      __lowerCamelCase    		=					0
      __lowerCamelCase    		=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      __lowerCamelCase    		=					model_output.device
      __lowerCamelCase    		=					randn_tensor(model_output.shape ,				dtype=model_output.dtype ,				device=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE	)

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
         __lowerCamelCase    		=					sigma_hat if self.state_in_first_order else sigma_interpol
         __lowerCamelCase    		=					sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         __lowerCamelCase    		=					sigma_hat if self.state_in_first_order else sigma_interpol
         __lowerCamelCase    		=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
      elif self.config.prediction_type == "sample":
         raise NotImplementedError('prediction_type not implemented yet: sample'	)
      else:
         raise ValueError(
             F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

      if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         __lowerCamelCase    		=					(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __lowerCamelCase    		=					sigma_interpol - sigma_hat

         # store for 2nd order step
         __lowerCamelCase    		=					sample
         __lowerCamelCase    		=					dt
         __lowerCamelCase    		=					sample + derivative * dt
      else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         __lowerCamelCase    		=					(sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         __lowerCamelCase    		=					sigma_down - sigma_hat

         __lowerCamelCase    		=					self.sample
         __lowerCamelCase    		=					None

         __lowerCamelCase    		=					sample + derivative * dt
         __lowerCamelCase    		=					prev_sample + noise * sigma_up

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.sigmas.to(device=original_samples.device ,				dtype=original_samples.dtype	)
      if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE	):
         # mps does not support float64
         __lowerCamelCase    		=					self.timesteps.to(original_samples.device ,				dtype=torch.floataa	)
         __lowerCamelCase    		=					timesteps.to(original_samples.device ,				dtype=torch.floataa	)
      else:
         __lowerCamelCase    		=					self.timesteps.to(original_samples.device	)
         __lowerCamelCase    		=					timesteps.to(original_samples.device	)

      __lowerCamelCase    		=					[self.index_for_timestep(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	) for t in timesteps]

      __lowerCamelCase    		=					sigmas[step_indices].flatten()
      while len(sigma.shape	) < len(original_samples.shape	):
         __lowerCamelCase    		=					sigma.unsqueeze(-1	)

      __lowerCamelCase    		=					original_samples + noise * sigma
      return noisy_samples







   def __len__(	self	):


      '''simple docstring'''

      return self.config.num_train_timesteps






