code: 
  
  
  
  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 	lowercase		(				lowercase__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[Any]	     =							["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]
  
  
  
  
  	def __init__(			self			:		Dict		,						__UpperCAmelCase			:		List[str]  )    ->				Dict:
  							assert Path(hparams.data_dir  ).exists()
  							UpperCAmelCase_=     Path(hparams.output_dir  )
  							self.output_dir.mkdir(exist_ok=_A  )
  
  							UpperCAmelCase_=     self.output_dir.joinpath("""student"""  )
  
  							UpperCAmelCase_=     str(_A  )  # Tell lightning we are training the student
  							UpperCAmelCase_=     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_=     AutoModelForSeqaSeqLM.from_pretrained(hparams.student  )
  													use_task_specific_params(_A		,						hparams.task  )
  													UpperCAmelCase_=     None, None
  							else:
  													UpperCAmelCase_=     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_=     hparams.length_penalty
  							UpperCAmelCase_=     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_=     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(_A  )  )
  							if d_layer_ids is None:
  													UpperCAmelCase_=     list(range(_A  )  )
  
  							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=_A  )
  																			UpperCAmelCase_=     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_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Optional[Any]  )    ->				List[str]:
  							UpperCAmelCase_=     mask[:, :, None].expand_as(_A  )
  							UpperCAmelCase_=     s_logits.size(-1  )
  							UpperCAmelCase_=     torch.masked_select(_A		,						_A  )  # (bs * seq_length * voc_size) modulo the 1s in mask
  							UpperCAmelCase_=     torch.masked_select(_A		,						_A  )  # (bs * seq_length * voc_size) modulo the 1s in mask
  							UpperCAmelCase_=     s_logits_slct.view(-1		,						_A  )  # (bs * seq_length, voc_size) modulo the 1s in mask
  							UpperCAmelCase_=     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_=     (
  							    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 						_SCREAMING_SNAKE_CASE	(			__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Any  )    ->				List[str]:
  							SummarizationModule.add_model_specific_args(_A		,						_A  )
  							add_distill_args(_A  )
  							return parser
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		dict  )    ->				tuple:
  							UpperCAmelCase_=     self.tokenizer.pad_token_id
  							UpperCAmelCase_=     batch['input_ids'], batch['attention_mask'], batch['labels']
  							if isinstance(self.model		,						_A  ):
  													UpperCAmelCase_=     self.model._shift_right(_A  )
  							else:
  													UpperCAmelCase_=     shift_tokens_right(_A		,						_A  )
  
  							# noinspection PyCallingNonCallable
  							UpperCAmelCase_=     self(
  							    _A		,						attention_mask=_A		,						decoder_input_ids=_A		,						output_hidden_states=self.do_calc_hidden_loss		,						output_attentions=_A		,						use_cache=_A		,						)
  							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=_A  )
  													UpperCAmelCase_=     loss_fct(lm_logits.view(-1		,						lm_logits.shape[-1]  )		,						labels.view(-1  )  )
  							else:
  													UpperCAmelCase_=     nn.functional.log_softmax(_A		,						dim=-1  )
  													UpperCAmelCase_=     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_=     student_outputs[
  							    'encoder_last_hidden_state'
  							]  # use this unless self.different_base_models
  							UpperCAmelCase_=     zero_tensor(), zero_tensor()
  							if self.different_encoder:  # compute encoder hidden state loss
  													UpperCAmelCase_=     self.teacher.get_encoder()(
  													    _A		,						attention_mask=_A		,						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(
  																			    _A		,						student_outputs["""encoder_hidden_states"""]		,						all_teacher_encoder_outputs["""hidden_states"""]		,						self.e_matches		,						normalize_hidden=self.hparams.normalize_hidden		,						)
  
  							UpperCAmelCase_=     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_=     decoder_input_ids.ne(_A  )
  							UpperCAmelCase_=     self.calc_ce_loss(_A		,						_A		,						teacher_outputs["""logits"""]  )
  							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  													UpperCAmelCase_=     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_=     (
  							    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 						_SCREAMING_SNAKE_CASE	(			__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Any:
  							UpperCAmelCase_=     '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_=     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(_A  )  )]  )
  							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(_A		,						student_states.shape[1:]  )
  													UpperCAmelCase_=     nn.functional.layer_norm(_A		,						teacher_states.shape[1:]  )
  							UpperCAmelCase_=     nn.functional.mse_loss(_A		,						_A		,						reduction="""none"""  )
  							UpperCAmelCase_=     (mse * mask.unsqueeze(0  ).unsqueeze(-1  )).sum() / valid_count
  							return masked_mse
  
  
  
  def  __a  (					lowerCAmelCase_     :      List[str]       )   ->				Optional[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						parser.add_argument("""--teacher"""    ,type=lowerCAmelCase_       )
  						parser.add_argument("""--alpha_ce"""    ,default=0.8    ,type=lowerCAmelCase_       )
  						parser.add_argument("""--alpha_mlm"""    ,default=0.2    ,type=lowerCAmelCase_       )
  						parser.add_argument("""--alpha_hid"""    ,default=0.0    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_       )
  						parser.add_argument("""--student"""    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_       )
  						parser.add_argument("""--student_decoder_layers"""    ,default=12    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_       )
  						parser.add_argument("""--student_encoder_layers"""    ,default=12    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_       )
  						parser.add_argument("""--no_teacher"""    ,action="""store_true"""    ,default=lowerCAmelCase_       )
  						parser.add_argument("""--length_penalty"""    ,type=lowerCAmelCase_    ,default=-1       )
  						parser.add_argument("""--supervise_forward"""    ,action="""store_true"""    ,default=lowerCAmelCase_       )
  						parser.add_argument("""--normalize_hidden"""    ,action="""store_true"""    ,default=lowerCAmelCase_       )
  
  
  
  
  
  
  class 	lowercase		(				lowercase__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Optional[Any]	     =							"""translation"""
  	a__   :      List[Any]	     =							["""bleu"""]
  	a__   :      Any	     =							"""bleu"""
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Any		,						**__UpperCAmelCase			:		Optional[int]  )    ->				str:
  							super().__init__(_A		,						**_A  )
  							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		,						_A  ):
  													UpperCAmelCase_=     self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		List[str]  )    ->				dict:
  							return calculate_bleu(_A		,						_A  )
  
  
  
  
  	@staticmethod
  	def 						_SCREAMING_SNAKE_CASE	(			__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		int  )    ->				Any:
  							TranslationModule.add_model_specific_args(_A		,						_A  )
  							add_distill_args(_A  )
  							return parser
  
  
  
  def  __a  (					lowerCAmelCase_     :      List[str]       )   ->				str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						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(lowerCAmelCase_       )
  						return model
  
  
  
  def  __a  (					lowerCAmelCase_     :      int       )   ->				Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						Path(args.output_dir       ).mkdir(exist_ok=lowerCAmelCase_       )
  						check_output_dir(lowerCAmelCase_    ,expected_items=3       )
  
  						UpperCAmelCase_=     create_module(lowerCAmelCase_       )
  						return ft_main(lowerCAmelCase_    ,model=lowerCAmelCase_       )
  
  
  if __name__ == "__main__":
  						__A       					=		argparse.ArgumentParser()
  						__A       					=		pl.Trainer.add_argparse_args(parser)
  						__A       					=		SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  						__A       					=		parser.parse_args()
  
  						distill_main(args)
  
  
  
  
  
style_context: 
  
  
  
  from math import radians as angle_to_radians
  from math import sin
  
  # Acceleration Constant on Earth (unit m/s^2)
  __A       					=		9.8_0_6_6_5
  
  
  
  def  __a  (					lowerCAmelCase_     :      float    ,lowerCAmelCase_     :      float       )   ->				None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if not isinstance(lowerCAmelCase_    ,(int, float)       ):
  												raise TypeError("""Invalid velocity. Should be a positive number."""       )
  
  						if not isinstance(lowerCAmelCase_    ,(int, float)       ):
  												raise TypeError("""Invalid angle. Range is 1-90 degrees."""       )
  
  						# Ensure valid angle
  						if angle > 90 or angle < 1:
  												raise ValueError("""Invalid angle. Range is 1-90 degrees."""       )
  
  						# Ensure valid velocity
  						if init_velocity < 0:
  												raise ValueError("""Invalid velocity. Should be a positive number."""       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      float    ,lowerCAmelCase_     :      float       )   ->				float:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						check_args(lowerCAmelCase_    ,lowerCAmelCase_       )
  						UpperCAmelCase_=     angle_to_radians(2 * angle       )
  						return round(init_velocity**2 * sin(lowerCAmelCase_       ) / g    ,2       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      float    ,lowerCAmelCase_     :      float       )   ->				float:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						check_args(lowerCAmelCase_    ,lowerCAmelCase_       )
  						UpperCAmelCase_=     angle_to_radians(lowerCAmelCase_       )
  						return round(init_velocity**2 * sin(lowerCAmelCase_       ) ** 2 / (2 * g)    ,2       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      float    ,lowerCAmelCase_     :      float       )   ->				float:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						check_args(lowerCAmelCase_    ,lowerCAmelCase_       )
  						UpperCAmelCase_=     angle_to_radians(lowerCAmelCase_       )
  						return round(2 * init_velocity * sin(lowerCAmelCase_       ) / g    ,2       )
  
  
  
  def  __a  (					)   ->				None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_,     UpperCAmelCase_=     25, 20
  						assert horizontal_distance(lowerCAmelCase_    ,lowerCAmelCase_       ) == 40.97
  						assert max_height(lowerCAmelCase_    ,lowerCAmelCase_       ) == 3.73
  						assert total_time(lowerCAmelCase_    ,lowerCAmelCase_       ) == 1.74
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod()
  
  	# Get input from user
  	__A       					=		float(input('''Initial Velocity: ''').strip())
  
  	# Get input from user
  	__A       					=		float(input('''angle: ''').strip())
  
  	# Print results
  	print()
  	print('''Results: ''')
  	print(f'Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]')
  	print(f'Maximum Height: {max_height(init_vel, angle)!s} [m]')
  	print(f'Total Time: {total_time(init_vel, angle)!s} [s]')
  
  
  
  
  