

'''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__			(				__A      ):
    _A			    =							["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]




    def __init__(					self				,     lowercase__			):






          """simple docstring"""






          assert Path(hparams.data_dir			).exists()
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       Path(hparams.output_dir			)
          self.output_dir.mkdir(exist_ok=lowerCamelCase__			)

          SCREAMING_SNAKE_CASE_      :			str     =       self.output_dir.joinpath("student"			)

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

          if hparams.length_penalty != -1:
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       hparams.length_penalty
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       hparams.teacher  # Use teacher's tokenizer
          super().__init__(lowerCamelCase__				,     model=lowerCamelCase__				,     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":
                SCREAMING_SNAKE_CASE_      :			Any     =       len(student.get_encoder().block			)
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       len(student.get_decoder().block			)
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       len(teacher.get_encoder().block			)
                SCREAMING_SNAKE_CASE_      :			List[str]     =       len(teacher.get_decoder().block			)
          else:
                SCREAMING_SNAKE_CASE_      :			Dict     =       student.config.encoder_layers
                SCREAMING_SNAKE_CASE_      :			Tuple     =       student.config.decoder_layers
                SCREAMING_SNAKE_CASE_      :			List[str]     =       teacher.config.encoder_layers
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       teacher.config.decoder_layers

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       not (hparams.student is None or hparams.teacher == hparams.student)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       (not self.different_base_models) and hparams.alpha_hid > 0
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
          # self.different_encoder determines whether we need to run the teacher encoder
          SCREAMING_SNAKE_CASE_      :			int     =       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:
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       list(range(lowerCamelCase__			)			)
          if d_layer_ids is None:
                SCREAMING_SNAKE_CASE_      :			Any     =       list(range(lowerCamelCase__			)			)

          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			List[Any]     =       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:
                      SCREAMING_SNAKE_CASE_      :			List[str]     =       get_layers_to_supervise(
                          n_student=len(self.e_layer_ids			)				,     n_teacher=lowerCamelCase__			)
                      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       get_layers_to_supervise(
                          n_student=len(self.d_layer_ids			)				,     n_teacher=lowerCamelCase__			)
                else:  # student layer should emulate hidden states of the teacher layer it was copied from
                      SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.e_layer_ids
                      SCREAMING_SNAKE_CASE_      :			Any     =       self.d_layer_ids
          else:
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       None
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       None

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       nn.KLDivLoss(reduction="batchmean"			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       2.0
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hparams.alpha_mlm
          SCREAMING_SNAKE_CASE_      :			Dict     =       hparams.alpha_ce
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       hparams.alpha_hid
          gc.collect()
          torch.cuda.empty_cache()




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       mask[:, :, None].expand_as(lowerCamelCase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       s_logits.size(-1			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.masked_select(lowerCamelCase__				,     lowerCamelCase__			)  # (bs * seq_length * voc_size) modulo the 1s in mask
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.masked_select(lowerCamelCase__				,     lowerCamelCase__			)  # (bs * seq_length * voc_size) modulo the 1s in mask
          SCREAMING_SNAKE_CASE_      :			Tuple     =       s_logits_slct.view(-1				,     lowerCamelCase__			)  # (bs * seq_length, voc_size) modulo the 1s in mask
          SCREAMING_SNAKE_CASE_      :			Dict     =       t_logits_slct.view(-1				,     lowerCamelCase__			)  # (bs * seq_length, voc_size) modulo the 1s in mask
          assert t_logits_slct.size() == s_logits_slct.size()
          SCREAMING_SNAKE_CASE_      :			List[str]     =       (
              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   __lowerCamelCase      (					lowercase__				,     lowercase__			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.tokenizer.pad_token_id
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			List[str]     =       batch["input_ids"], batch["attention_mask"], batch["labels"]
          if isinstance(self.model				,     lowerCamelCase__			):
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.model._shift_right(lowerCamelCase__			)
          else:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       shift_tokens_right(lowerCamelCase__				,     lowerCamelCase__			)

          # noinspection PyCallingNonCallable
          SCREAMING_SNAKE_CASE_      :			int     =       self(
              lowerCamelCase__				,     attention_mask=lowerCamelCase__				,     decoder_input_ids=lowerCamelCase__				,     output_hidden_states=self.do_calc_hidden_loss				,     output_attentions=lowerCamelCase__				,     use_cache=lowerCamelCase__				,     )
          SCREAMING_SNAKE_CASE_      :			Any     =       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
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       nn.CrossEntropyLoss(ignore_index=lowerCamelCase__			)
                SCREAMING_SNAKE_CASE_      :			List[str]     =       loss_fct(lm_logits.view(-1				,     lm_logits.shape[-1]			)				,     labels.view(-1			)			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       nn.functional.log_softmax(lowerCamelCase__				,     dim=-1			)
                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       label_smoothed_nll_loss(
                    lowerCamelCase__				,     lowerCamelCase__				,     self.hparams.label_smoothing				,     ignore_index=lowerCamelCase__			)

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

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       student_outputs[
              "encoder_last_hidden_state"
          ]  # use this unless self.different_base_models
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			List[str]     =       zero_tensor(), zero_tensor()
          if self.different_encoder:  # compute encoder hidden state loss
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.teacher.get_encoder()(
                    lowerCamelCase__				,     attention_mask=lowerCamelCase__				,     output_hidden_states=self.do_calc_hidden_loss				,     )
                if self.different_base_models:
                      SCREAMING_SNAKE_CASE_      :			List[str]     =       all_teacher_encoder_outputs["last_hidden_state"]
                elif self.do_calc_hidden_loss:
                      SCREAMING_SNAKE_CASE_      :			int     =       self.calc_hidden_loss(
                          lowerCamelCase__				,     student_outputs["encoder_hidden_states"]				,     all_teacher_encoder_outputs["hidden_states"]				,     self.e_matches				,     normalize_hidden=self.hparams.normalize_hidden				,     )

          SCREAMING_SNAKE_CASE_      :			Dict     =       self.teacher(
              lowerCamelCase__				,     attention_mask=lowerCamelCase__				,     encoder_outputs=(teacher_enc_outputs,)				,     decoder_input_ids=lowerCamelCase__				,     output_hidden_states=self.do_calc_hidden_loss				,     use_cache=lowerCamelCase__				,     )
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       decoder_input_ids.ne(lowerCamelCase__			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       self.calc_ce_loss(lowerCamelCase__				,     lowerCamelCase__				,     teacher_outputs["logits"]			)
          if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
                SCREAMING_SNAKE_CASE_      :			int     =       self.calc_hidden_loss(
                    lowerCamelCase__				,     student_outputs["decoder_hidden_states"]				,     teacher_outputs["decoder_hidden_states"]				,     self.d_matches				,     normalize_hidden=self.hparams.normalize_hidden				,     )

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (
              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   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       "expected list or tuple for hidden_states, got tensor of shape: "
          assert not isinstance(lowerCamelCase__				,     torch.Tensor			), F"{msg}{hidden_states.shape}"
          assert not isinstance(lowerCamelCase__				,     torch.Tensor			), F"{msg}{hidden_states_T.shape}"
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       attention_mask.to(hidden_states[0]			)
          SCREAMING_SNAKE_CASE_      :			Any     =       mask.sum() * hidden_states[0].size(-1			)
          SCREAMING_SNAKE_CASE_      :			int     =       torch.stack([hidden_states[i] for i in range(len(lowerCamelCase__			)			)]			)
          SCREAMING_SNAKE_CASE_      :			Any     =       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:
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       nn.functional.layer_norm(lowerCamelCase__				,     student_states.shape[1:]			)
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       nn.functional.layer_norm(lowerCamelCase__				,     teacher_states.shape[1:]			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       nn.functional.mse_loss(lowerCamelCase__				,     lowerCamelCase__				,     reduction="none"			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       (mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
          return masked_mse






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any    )							->   int:
      """simple docstring"""

      parser.add_argument("--teacher"  ,  type=a__    )
      parser.add_argument("--alpha_ce"  ,  default=0.8  ,  type=a__    )
      parser.add_argument("--alpha_mlm"  ,  default=0.2  ,  type=a__    )
      parser.add_argument("--alpha_hid"  ,  default=0.0  ,  type=a__  ,  required=a__    )
      parser.add_argument("--student"  ,  type=a__  ,  required=a__    )
      parser.add_argument("--student_decoder_layers"  ,  default=1_2  ,  type=a__  ,  required=a__    )
      parser.add_argument("--student_encoder_layers"  ,  default=1_2  ,  type=a__  ,  required=a__    )
      parser.add_argument("--no_teacher"  ,  action="store_true"  ,  default=a__    )
      parser.add_argument("--length_penalty"  ,  type=a__  ,  default=-1    )
      parser.add_argument("--supervise_forward"  ,  action="store_true"  ,  default=a__    )
      parser.add_argument("--normalize_hidden"  ,  action="store_true"  ,  default=a__    )
class 	SCREAMING_SNAKE_CASE__			(				__A      ):
    _A			    =							"""translation"""
    _A			    =							["""bleu"""]
    _A			    =							"""bleu"""




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






          """simple docstring"""






          super().__init__(lowerCamelCase__				,     **lowerCamelCase__			)
          assert hparams.src_lang is not None
          assert hparams.tgt_lang is not None
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       hparams.src_lang
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       hparams.tgt_lang
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,     lowerCamelCase__			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       self.tokenizer.lang_code_to_id[hparams.tgt_lang]




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          return calculate_bleu(lowerCamelCase__				,     lowerCamelCase__			)







    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__			):






          """simple docstring"""






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






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[Any]    )							->   Optional[int]:
      """simple docstring"""

      if args.no_teacher:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       TranslationModule if "translation" in args.task else SummarizationModule
      else:  # DISTILL WITH TEACHER
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       TranslationDistiller if "translation" in args.task else SummarizationDistiller
      SCREAMING_SNAKE_CASE_      :			Any     =       module_cls.__name__
      print(F"using module {args.setup_cls}"    )
      SCREAMING_SNAKE_CASE_      :			Tuple     =       module_cls(a__    )
      return model


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[Any]    )							->   str:
      """simple docstring"""

      Path(args.output_dir    ).mkdir(exist_ok=a__    )
      check_output_dir(a__  ,  expected_items=3    )

      SCREAMING_SNAKE_CASE_      :			Dict     =       create_module(a__    )
      return ft_main(a__  ,  model=a__    )


if __name__ == "__main__":
      snake_case_       =       argparse.ArgumentParser()
      snake_case_       =       pl.Trainer.add_argparse_args(parser)
      snake_case_       =       SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
      snake_case_       =       parser.parse_args()

      distill_main(args)







'''simple docstring'''
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					(HeunDiscreteScheduler,)
       snake_case_ :     Any    =					10

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					int:

           """simple docstring"""



           __lowercase							=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**lowerCamelCase__     )
           return config

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,		[0.00_02, 0.0_02, 0.02]     ):
               self.check_over_configs(beta_start=lowerCamelCase__					,		beta_end=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCamelCase__					,		clip_sample=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config(prediction_type="""v_prediction"""     )
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if str(lowerCamelCase__     ).startswith("""cpu"""     ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           elif str(lowerCamelCase__     ).startswith("""mps"""     ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.00_02     ) < 1E-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__					,		use_karras_sigmas=lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           assert abs(result_sum.item() - 0.0_00_15     ) < 1E-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7     ) < 1E-2




