



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)









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]')




