



'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterable

class      UpperCamelCase_					:





	def __init__(    self						,						A						,						A       )     ->     int:
								UpperCAmelCase	,	UpperCAmelCase	:						Any					=						float(A       ), float(A       )





	def __eq__(    self						,						A       )     ->     str:
								return self.x == other.x and self.y == other.y





	def __ne__(    self						,						A       )     ->     Dict:
								return not self == other





	def __gt__(    self						,						A       )     ->     Dict:
								if self.x > other.x:
															return True
								elif self.x == other.x:
															return self.y > other.y
								return False





	def __lt__(    self						,						A       )     ->     str:
								return not self > other





	def __ge__(    self						,						A       )     ->     Union[str, Any]:
								if self.x > other.x:
															return True
								elif self.x == other.x:
															return self.y >= other.y
								return False





	def __le__(    self						,						A       )     ->     List[str]:
								if self.x < other.x:
															return True
								elif self.x == other.x:
															return self.y <= other.y
								return False





	def __repr__(    self       )     ->     Tuple:
								return f'''({self.x}, {self.y})'''






	def __hash__(    self       )     ->     List[str]:
								return hash(self.x       )




def  __lowerCamelCase    (   _lowercase      ,							) ->     list[Point]:
							UpperCAmelCase	:						list[Point]					=						[]
							if list_of_tuples:
														for p in list_of_tuples:
																					if isinstance(_lowercase      ,							_lowercase						):
																												points.append(_lowercase						)
																					else:
																												try:
																																			points.append(Point(p[0]      ,							p[1]						)						)
																												except (IndexError, TypeError):
																																			print(
																																			    F'''Ignoring deformed point {p}. All points'''
																																			    """ must have at least 2 coordinates."""						)
							return points



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:

							if not hasattr(_lowercase      ,							"""__iter__"""						):
														UpperCAmelCase	:						List[str]					=						F'''Expecting an iterable object but got an non-iterable type {points}'''
														raise ValueError(_lowercase						)

							if not points:
														UpperCAmelCase	:						Optional[int]					=						F'''Expecting a list of points but got {points}'''
														raise ValueError(_lowercase						)

							return _construct_points(_lowercase						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     float:
							UpperCAmelCase	:						Dict					=						(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
							return det



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:
							UpperCAmelCase	:						Optional[int]					=						sorted(_validate_input(_lowercase						)						)
							UpperCAmelCase	:						int					=						len(_lowercase						)
							UpperCAmelCase	:						Any					=						set()

							for i in range(n - 1						):
														for j in range(i + 1      ,							_lowercase						):
																					UpperCAmelCase	:						Optional[Any]					=						False
																					UpperCAmelCase	:						Optional[int]					=						True
																					for k in range(_lowercase						):
																												if k != i and k != j:
																																			UpperCAmelCase	:						Union[str, Any]					=						_det(points[i]      ,							points[j]      ,							points[k]						)

																																			if det_k > 0:
																																										UpperCAmelCase	:						Optional[int]					=						True
																																			elif det_k < 0:
																																										UpperCAmelCase	:						int					=						True
																																			else:
																																										# point[i], point[j], point[k] all lie on a straight line
																																										# if point[k] is to the left of point[i] or it's to the
																																										# right of point[j], then point[i], point[j] cannot be
																																										# part of the convex hull of A
																																										if points[k] < points[i] or points[k] > points[j]:
																																																	UpperCAmelCase	:						List[Any]					=						False
																																																	break

																												if points_left_of_ij and points_right_of_ij:
																																			UpperCAmelCase	:						Union[str, Any]					=						False
																																			break

																					if ij_part_of_convex_hull:
																												convex_set.update([points[i], points[j]]						)

							return sorted(_lowercase						)



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:
							UpperCAmelCase	:						List[str]					=						sorted(_validate_input(_lowercase						)						)
							UpperCAmelCase	:						Optional[Any]					=						len(_lowercase						)

							# divide all the points into an upper hull and a lower hull
							# the left most point and the right most point are definitely
							# members of the convex hull by definition.
							# use these two anchors to divide all the points into two hulls,
							# an upper hull and a lower hull.

							# all points to the left (above) the line joining the extreme points belong to the
							# upper hull
							# all points to the right (below) the line joining the extreme points below to the
							# lower hull
							# ignore all points on the line joining the extreme points since they cannot be
							# part of the convex hull

							UpperCAmelCase	:						List[str]					=						points[0]
							UpperCAmelCase	:						Union[str, Any]					=						points[n - 1]

							UpperCAmelCase	:						int					=						{left_most_point, right_most_point}
							UpperCAmelCase	:						Dict					=						[]
							UpperCAmelCase	:						Any					=						[]

							for i in range(1      ,							n - 1						):
														UpperCAmelCase	:						Any					=						_det(_lowercase      ,							_lowercase      ,							points[i]						)

														if det > 0:
																					upper_hull.append(points[i]						)
														elif det < 0:
																					lower_hull.append(points[i]						)

							_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)
							_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)

							return sorted(_lowercase						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     None:
							if points:
														UpperCAmelCase	:						Union[str, Any]					=						None
														UpperCAmelCase	:						int					=						float("""-inf"""						)
														UpperCAmelCase	:						Tuple					=						[]

														for p in points:
																					UpperCAmelCase	:						List[Any]					=						_det(_lowercase      ,							_lowercase      ,							_lowercase						)

																					if det > 0:
																												candidate_points.append(_lowercase						)

																												if det > extreme_point_distance:
																																			UpperCAmelCase	:						List[str]					=						det
																																			UpperCAmelCase	:						Optional[Any]					=						p

														if extreme_point:
																					_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)
																					convex_set.add(_lowercase						)
																					_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:
							UpperCAmelCase	:						Any					=						sorted(_validate_input(_lowercase						)						)
							UpperCAmelCase	:						Union[str, Any]					=						len(_lowercase						)

							UpperCAmelCase	:						Dict					=						points[:2]
							for i in range(2      ,							_lowercase						):
														UpperCAmelCase	:						Dict					=						_det(convex_hull[1]      ,							convex_hull[0]      ,							points[i]						)
														if det > 0:
																					convex_hull.insert(0      ,							points[i]						)
																					break
														elif det < 0:
																					convex_hull.append(points[i]						)
																					break
														else:
																					UpperCAmelCase	:						Dict					=						points[i]
							i += 1

							for j in range(_lowercase      ,							_lowercase						):
														if (
														    _det(convex_hull[0]      ,							convex_hull[-1]      ,							points[j]						) > 0
														    and _det(convex_hull[-1]      ,							convex_hull[0]      ,							points[1]						) < 0
														):
																					# The point lies within the convex hull
																					continue

														convex_hull.insert(0      ,							points[j]						)
														convex_hull.append(points[j]						)
														while _det(convex_hull[0]      ,							convex_hull[1]      ,							convex_hull[2]						) >= 0:
																					del convex_hull[1]
														while _det(convex_hull[-1]      ,							convex_hull[-2]      ,							convex_hull[-3]						) <= 0:
																					del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
							return sorted(convex_hull[1:] if len(_lowercase						) > 3 else convex_hull						)



def  __lowerCamelCase    (   ) ->     Tuple:
							UpperCAmelCase	:						Tuple					=						[
							    (0, 3),
							    (2, 2),
							    (1, 1),
							    (2, 1),
							    (3, 0),
							    (0, 0),
							    (3, 3),
							    (2, -1),
							    (2, -4),
							    (1, -3),
							]
							# the convex set of points is
							# [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
							UpperCAmelCase	:						Tuple					=						convex_hull_bf(_lowercase						)

							UpperCAmelCase	:						List[Any]					=						convex_hull_recursive(_lowercase						)
							assert results_bf == results_recursive

							UpperCAmelCase	:						Union[str, Any]					=						convex_hull_melkman(_lowercase						)
							assert results_bf == results_melkman

							print(_lowercase						)


if __name__ == "__main__":
							main()






'''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      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']





	def __init__(    self						,						A       )     ->     Any:
								assert Path(hparams.data_dir       ).exists()
								UpperCAmelCase	:						Dict					=						Path(hparams.output_dir       )
								self.output_dir.mkdir(exist_ok=A       )

								UpperCAmelCase	:						int					=						self.output_dir.joinpath("""student"""       )

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

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

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

								UpperCAmelCase	,	UpperCAmelCase	:						str					=						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	:						List[str]					=						get_layers_to_supervise(
																						    n_student=len(self.e_layer_ids       )						,						n_teacher=A       )
																						UpperCAmelCase	:						int					=						get_layers_to_supervise(
																						    n_student=len(self.d_layer_ids       )						,						n_teacher=A       )
															else:  # student layer should emulate hidden states of the teacher layer it was copied from
																						UpperCAmelCase	:						Dict					=						self.e_layer_ids
																						UpperCAmelCase	:						List[Any]					=						self.d_layer_ids
								else:
															UpperCAmelCase	:						Tuple					=						None
															UpperCAmelCase	:						Union[str, Any]					=						None

								UpperCAmelCase	:						Any					=						nn.KLDivLoss(reduction="""batchmean"""       )
								UpperCAmelCase	:						Tuple					=						2.0
								UpperCAmelCase	:						Tuple					=						hparams.alpha_mlm
								UpperCAmelCase	:						List[str]					=						hparams.alpha_ce
								UpperCAmelCase	:						Dict					=						hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Any:
								UpperCAmelCase	:						Union[str, Any]					=						mask[:, :, None].expand_as(A       )
								UpperCAmelCase	:						str					=						s_logits.size(-1       )
								UpperCAmelCase	:						Optional[Any]					=						torch.masked_select(A						,						A       )  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCAmelCase	:						List[str]					=						torch.masked_select(A						,						A       )  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCAmelCase	:						List[str]					=						s_logits_slct.view(-1						,						A       )  # (bs * seq_length, voc_size) modulo the 1s in mask
								UpperCAmelCase	:						Optional[Any]					=						t_logits_slct.view(-1						,						A       )  # (bs * seq_length, voc_size) modulo the 1s in mask
								assert t_logits_slct.size() == s_logits_slct.size()
								UpperCAmelCase	:						int					=						(
								    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 					_lowercase(    A						,						A       )     ->     Optional[int]:
								SummarizationModule.add_model_specific_args(A						,						A       )
								add_distill_args(A       )
								return parser





	def 					_lowercase(    self						,						A       )     ->     tuple:
								UpperCAmelCase	:						Union[str, Any]					=						self.tokenizer.pad_token_id
								UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						Optional[int]					=						batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
								if isinstance(self.model						,						A       ):
															UpperCAmelCase	:						List[str]					=						self.model._shift_right(A       )
								else:
															UpperCAmelCase	:						str					=						shift_tokens_right(A						,						A       )

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

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

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

								UpperCAmelCase	:						str					=						self.teacher(
								    A						,						attention_mask=A						,						encoder_outputs=(teacher_enc_outputs,)						,						decoder_input_ids=A						,						output_hidden_states=self.do_calc_hidden_loss						,						use_cache=A						,						)
								UpperCAmelCase	:						Union[str, Any]					=						decoder_input_ids.ne(A       )
								UpperCAmelCase	:						Union[str, Any]					=						self.calc_ce_loss(A						,						A						,						teacher_outputs["""logits"""]       )
								if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
															UpperCAmelCase	:						List[Any]					=						self.calc_hidden_loss(
															    A						,						student_outputs["""decoder_hidden_states"""]						,						teacher_outputs["""decoder_hidden_states"""]						,						self.d_matches						,						normalize_hidden=self.hparams.normalize_hidden						,						)

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




def  __lowerCamelCase    (   _lowercase						) ->     Dict:
							# 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=1_2      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student_encoder_layers"""      ,							default=1_2      ,							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      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					'translation'
	lowercase		=					['bleu']
	lowercase		=					'bleu'





	def __init__(    self						,						A						,						**A       )     ->     str:
								super().__init__(A						,						**A       )
								assert hparams.src_lang is not None
								assert hparams.tgt_lang is not None
								UpperCAmelCase	:						Any					=						hparams.src_lang
								UpperCAmelCase	:						Optional[Any]					=						hparams.tgt_lang
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,						A       ):
															UpperCAmelCase	:						Union[str, Any]					=						self.tokenizer.lang_code_to_id[hparams.tgt_lang]





	def 					_lowercase(    self						,						A						,						A       )     ->     dict:
								return calculate_bleu(A						,						A       )






	@staticmethod
	def 					_lowercase(    A						,						A       )     ->     Dict:
								TranslationModule.add_model_specific_args(A						,						A       )
								add_distill_args(A       )
								return parser




def  __lowerCamelCase    (   _lowercase						) ->     Tuple:
							if args.no_teacher:
														UpperCAmelCase	:						Optional[int]					=						TranslationModule if """translation""" in args.task else SummarizationModule
							else:  # DISTILL WITH TEACHER
														UpperCAmelCase	:						List[Any]					=						TranslationDistiller if """translation""" in args.task else SummarizationDistiller
							UpperCAmelCase	:						str					=						module_cls.__name__
							print(F'''using module {args.setup_cls}'''						)
							UpperCAmelCase	:						Optional[Any]					=						module_cls(_lowercase						)
							return model



def  __lowerCamelCase    (   _lowercase						) ->     Optional[int]:
							Path(args.output_dir						).mkdir(exist_ok=_lowercase						)
							check_output_dir(_lowercase      ,							expected_items=3						)

							UpperCAmelCase	:						int					=						create_module(_lowercase						)
							return ft_main(_lowercase      ,							model=_lowercase						)


if __name__ == "__main__":
							a  :     Any       =       argparse.ArgumentParser()
							a  :     Tuple       =       pl.Trainer.add_argparse_args(parser)
							a  :     Optional[Any]       =       SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
							a  :     Any       =       parser.parse_args()

							distill_main(args)

