



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   torch.FloatTensor
							UpperCamelCase__					:   torch.FloatTensor




def       A_   (						__a  :					Dict			,		__a  :					Optional[int]=0.9_9_9			,		__a  :					Dict="cosine"			,		):

				"""simple docstring"""
				if alpha_transform_type == "cosine":

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

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

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




class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Any       =       1


							@register_to_config
							def __init__(				self	,	a_ = 1_000	,	a_ = 0.0_001	,	a_ = 0.02	,	a_ = "linear"	,	a_ = 0.0	,	a_ = None	,	a_ = True	,	):
											if trained_betas is not None:
															a__					   =		torch.from_numpy(a_		)
											elif beta_schedule == "linear":
															a__					   =		torch.linspace(a_	,	a_	,	a_	,	dtype=torch.floataa		)
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															a__					   =		(
															    torch.linspace(beta_start**0.5	,	beta_end**0.5	,	a_	,	dtype=torch.floataa		) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															a__					   =		betas_for_alpha_bar(a_		)
											elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															a__					   =		torch.linspace(-6	,	6	,	a_		)
															a__					   =		torch.sigmoid(a_		) * (beta_end - beta_start) + beta_start
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

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

											a__					   =		torch.tensor(1.0		)

											# standard deviation of the initial noise distribution
											a__					   =		1.0

											# setable values
											a__					   =		None
											a__					   =		torch.from_numpy(np.arange(0	,	a_		)[::-1].copy()		)

											a__					   =		eta


							def _a   (				self	,	a_	,	a_ = None		):
											return sample


							def _a   (				self	,	a_	,	a_ = 10	,	a_ = 10	,	a_ = None	,	):
											a__					   =		min(self.config.num_train_timesteps	,	a_		)
											a__					   =		num_inference_steps

											a__					   =		[]

											a__					   =		{}
											for j in range(0	,	num_inference_steps - jump_length	,	a_		):
															a__					   =		jump_n_sample - 1

											a__					   =		num_inference_steps
											while t >= 1:
															a__					   =		t - 1
															timesteps.append(a_		)

															if jumps.get(a_	,	0		) > 0:
																			a__					   =		jumps[t] - 1
																			for _ in range(a_		):
																							a__					   =		t + 1
																							timesteps.append(a_		)

											a__					   =		np.array(a_		) * (self.config.num_train_timesteps // self.num_inference_steps)
											a__					   =		torch.from_numpy(a_		).to(a_		)


							def _a   (				self	,	a_		):
											a__					   =		t - self.config.num_train_timesteps // self.num_inference_steps

											a__					   =		self.alphas_cumprod[t]
											a__					   =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											a__					   =		1 - alpha_prod_t
											a__					   =		1 - alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from
											# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
											# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
											# variance to pred_sample
											# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
											# without eta.
											# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
											a__					   =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	a_	,	a_ = None	,	a_ = True	,	):
											a__					   =		timestep
											a__					   =		timestep - self.config.num_train_timesteps // self.num_inference_steps

											# 1. compute alphas, betas
											a__					   =		self.alphas_cumprod[t]
											a__					   =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											a__					   =		1 - alpha_prod_t

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											a__					   =		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
															a__					   =		torch.clamp(a_	,	-1	,	1		)

											# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
											# substitute formula (7) in the algorithm coming from DDPM paper
											# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
											# DDIM schedule gives the same results as DDPM with eta = 1.0
											# Noise is being reused in 7. and 8., but no impact on quality has
											# been observed.

											# 5. Add noise
											a__					   =		model_output.device
											a__					   =		randn_tensor(model_output.shape	,	generator=a_	,	device=a_	,	dtype=model_output.dtype		)
											a__					   =		self.eta * self._get_variance(a_		) ** 0.5

											a__					   =		0
											if t > 0 and self.eta > 0:
															a__					   =		std_dev_t * noise

											# 6. compute "direction pointing to x_t" of formula (12)
											# from https://arxiv.org/pdf/2010.02502.pdf
											a__					   =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

											# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											a__					   =		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

											# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
											a__					   =		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

											# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
											a__					   =		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

											if not return_dict:
															return (
															    pred_prev_sample,
															    pred_original_sample,
															)

											return RePaintSchedulerOutput(prev_sample=a_	,	pred_original_sample=a_		)


							def _a   (				self	,	a_	,	a_	,	a_=None		):
											a__					   =		self.config.num_train_timesteps // self.num_inference_steps

											for i in range(a_		):
															a__					   =		self.betas[timestep + i]
															if sample.device.type == "mps":
																			# randn does not work reproducibly on mps
																			a__					   =		randn_tensor(sample.shape	,	dtype=sample.dtype	,	generator=a_		)
																			a__					   =		noise.to(sample.device		)
															else:
																			a__					   =		randn_tensor(sample.shape	,	generator=a_	,	device=sample.device	,	dtype=sample.dtype		)

															# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
															a__					   =		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

											return sample


							def _a   (				self	,	a_	,	a_	,	a_	,	):
											raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""		)







							def __len__(				self		):
											return self.config.num_train_timesteps



"""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      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				str   			=      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







       def __init__(       self				,lowerCamelCase_		):



          '''simple docstring'''

          assert Path(hparams.data_dir		).exists()
          _UpperCAmelCase 						=       Path(hparams.output_dir		)
          self.output_dir.mkdir(exist_ok=lowerCamelCase_		)

          _UpperCAmelCase 						=       self.output_dir.joinpath("student"		)

          _UpperCAmelCase 						=       str(lowerCamelCase_		)  # Tell lightning we are training the student
          _UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       AutoModelForSeqaSeqLM.from_pretrained(hparams.student		)
             use_task_specific_params(lowerCamelCase_				,hparams.task		)
             _UpperCAmelCase						,				_UpperCAmelCase 						=       None, None
          else:
             _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       hparams.length_penalty
          _UpperCAmelCase 						=       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":
             _UpperCAmelCase 						=       len(student.get_encoder().block		)
             _UpperCAmelCase 						=       len(student.get_decoder().block		)
             _UpperCAmelCase 						=       len(teacher.get_encoder().block		)
             _UpperCAmelCase 						=       len(teacher.get_decoder().block		)
          else:
             _UpperCAmelCase 						=       student.config.encoder_layers
             _UpperCAmelCase 						=       student.config.decoder_layers
             _UpperCAmelCase 						=       teacher.config.encoder_layers
             _UpperCAmelCase 						=       teacher.config.decoder_layers

          _UpperCAmelCase 						=       not (hparams.student is None or hparams.teacher == hparams.student)
          _UpperCAmelCase 						=       (not self.different_base_models) and hparams.alpha_hid > 0
          _UpperCAmelCase 						=       self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
          # self.different_encoder determines whether we need to run the teacher encoder
          _UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       list(range(lowerCamelCase_		)		)
          if d_layer_ids is None:
             _UpperCAmelCase 						=       list(range(lowerCamelCase_		)		)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       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:
                _UpperCAmelCase 						=       get_layers_to_supervise(
                    n_student=len(self.e_layer_ids		)				,n_teacher=lowerCamelCase_		)
                _UpperCAmelCase 						=       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
                _UpperCAmelCase 						=       self.e_layer_ids
                _UpperCAmelCase 						=       self.d_layer_ids
          else:
             _UpperCAmelCase 						=       None
             _UpperCAmelCase 						=       None

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







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       mask[:, :, None].expand_as(lowerCamelCase_		)
          _UpperCAmelCase 						=       s_logits.size(-1		)
          _UpperCAmelCase 						=       torch.masked_select(lowerCamelCase_				,lowerCamelCase_		)  # (bs * seq_length * voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       torch.masked_select(lowerCamelCase_				,lowerCamelCase_		)  # (bs * seq_length * voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       s_logits_slct.view(-1				,lowerCamelCase_		)  # (bs * seq_length, voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       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()
          _UpperCAmelCase 						=       (
              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  _snake_case      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.tokenizer.pad_token_id
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       batch["input_ids"], batch["attention_mask"], batch["labels"]
          if isinstance(self.model				,lowerCamelCase_		):
             _UpperCAmelCase 						=       self.model._shift_right(lowerCamelCase_		)
          else:
             _UpperCAmelCase 						=       shift_tokens_right(lowerCamelCase_				,lowerCamelCase_		)

          # noinspection PyCallingNonCallable
          _UpperCAmelCase 						=       self(
              lowerCamelCase_				,attention_mask=lowerCamelCase_				,decoder_input_ids=lowerCamelCase_				,output_hidden_states=self.do_calc_hidden_loss				,output_attentions=lowerCamelCase_				,use_cache=lowerCamelCase_				,)
          _UpperCAmelCase 						=       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
             _UpperCAmelCase 						=       nn.CrossEntropyLoss(ignore_index=lowerCamelCase_		)
             _UpperCAmelCase 						=       loss_fct(lm_logits.view(-1				,lm_logits.shape[-1]		)				,labels.view(-1		)		)
          else:
             _UpperCAmelCase 						=       nn.functional.log_softmax(lowerCamelCase_				,dim=-1		)
             _UpperCAmelCase						,				_UpperCAmelCase 						=       label_smoothed_nll_loss(
                 lowerCamelCase_				,lowerCamelCase_				,self.hparams.label_smoothing				,ignore_index=lowerCamelCase_		)

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

          _UpperCAmelCase 						=       student_outputs[
              "encoder_last_hidden_state"
          ]  # use this unless self.different_base_models
          _UpperCAmelCase						,				_UpperCAmelCase 						=       zero_tensor(), zero_tensor()
          if self.different_encoder:  # compute encoder hidden state loss
             _UpperCAmelCase 						=       self.teacher.get_encoder()(
                 lowerCamelCase_				,attention_mask=lowerCamelCase_				,output_hidden_states=self.do_calc_hidden_loss				,)
             if self.different_base_models:
                _UpperCAmelCase 						=       all_teacher_encoder_outputs["last_hidden_state"]
             elif self.do_calc_hidden_loss:
                _UpperCAmelCase 						=       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				,)

          _UpperCAmelCase 						=       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_				,)
          _UpperCAmelCase 						=       decoder_input_ids.ne(lowerCamelCase_		)
          _UpperCAmelCase 						=       self.calc_ce_loss(lowerCamelCase_				,lowerCamelCase_				,teacher_outputs["logits"]		)
          if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
             _UpperCAmelCase 						=       self.calc_hidden_loss(
                 lowerCamelCase_				,student_outputs["decoder_hidden_states"]				,teacher_outputs["decoder_hidden_states"]				,self.d_matches				,normalize_hidden=self.hparams.normalize_hidden				,)

          _UpperCAmelCase 						=       (
              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  _snake_case      (       lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       "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}'''
          _UpperCAmelCase 						=       attention_mask.to(hidden_states[0]		)
          _UpperCAmelCase 						=       mask.sum() * hidden_states[0].size(-1		)
          _UpperCAmelCase 						=       torch.stack([hidden_states[i] for i in range(len(lowerCamelCase_		)		)]		)
          _UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       nn.functional.layer_norm(lowerCamelCase_				,student_states.shape[1:]		)
             _UpperCAmelCase 						=       nn.functional.layer_norm(lowerCamelCase_				,teacher_states.shape[1:]		)
          _UpperCAmelCase 						=       nn.functional.mse_loss(lowerCamelCase_				,lowerCamelCase_				,reduction="none"		)
          _UpperCAmelCase 						=       (mse * mask.unsqueeze(0		).unsqueeze(-1		)).sum() / valid_count
          return masked_mse



def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							Tuple:
   # 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=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=12     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student_encoder_layers"     ,	default=12     ,	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      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Union[str, Any]   			=      'translation'
       _lowerCamelCase				:				Dict   			=      ['bleu']
       _lowerCamelCase				:				Any   			=      'bleu'







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



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          return calculate_bleu(lowerCamelCase_				,lowerCamelCase_		)







       @staticmethod
       def  _snake_case      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

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



def       __UpperCAmelCase		(		a_      :		str					)  ->							Any:
   if args.no_teacher:
      _UpperCAmelCase 						=       TranslationModule if "translation" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      _UpperCAmelCase 						=       TranslationDistiller if "translation" in args.task else SummarizationDistiller
   _UpperCAmelCase 						=       module_cls.__name__
   print(F'''using module {args.setup_cls}'''					)
   _UpperCAmelCase 						=       module_cls(a_					)
   return model
def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							str:
   Path(args.output_dir					).mkdir(exist_ok=a_					)
   check_output_dir(a_     ,	expected_items=3					)

   _UpperCAmelCase 						=       create_module(a_					)
   return ft_main(a_     ,	model=a_					)


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

  distill_main(args)

