


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				(							_lowercase):
	_a    :    Union[str, Any]        =				['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']




	def __init__(   self		:						str						,   _SCREAMING_SNAKE_CASE		:						Tuple						)->			Any:
			assert Path(hparams.data_dir						).exists()
			lowerCAmelCase__  :     str       	=   Path(hparams.output_dir						)
			self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     List[str]       	=   self.output_dir.joinpath('''student'''						)

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

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

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

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

			lowerCAmelCase__  :     Any       	=   nn.KLDivLoss(reduction='''batchmean'''						)
			lowerCAmelCase__  :     List[str]       	=   2.0
			lowerCAmelCase__  :     int       	=   hparams.alpha_mlm
			lowerCAmelCase__  :     List[Any]       	=   hparams.alpha_ce
			lowerCAmelCase__  :     Optional[Any]       	=   hparams.alpha_hid
			gc.collect()
			torch.cuda.empty_cache()




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




	def 				UpperCAmelCase__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						dict						)->			tuple:
			lowerCAmelCase__  :     str       	=   self.tokenizer.pad_token_id
			lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__  :     Union[str, Any]       	=   batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
			if isinstance(self.model						,   _SCREAMING_SNAKE_CASE						):
					lowerCAmelCase__  :     List[str]       	=   self.model._shift_right(_SCREAMING_SNAKE_CASE						)
			else:
					lowerCAmelCase__  :     str       	=   shift_tokens_right(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

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

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

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

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

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






def 	lowerCamelCase_       (			_a			):




		"""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=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				(							_lowercase):
	_a    :    int        =				'''translation'''
	_a    :    int        =				['''bleu''']
	_a    :    str        =				'''bleu'''




	def __init__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						List[Any]						,   **_SCREAMING_SNAKE_CASE		:						int						)->			int:
			super().__init__(_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)
			assert hparams.src_lang is not None
			assert hparams.tgt_lang is not None
			lowerCAmelCase__  :     int       	=   hparams.src_lang
			lowerCAmelCase__  :     str       	=   hparams.tgt_lang
			if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,   _SCREAMING_SNAKE_CASE						):
					lowerCAmelCase__  :     Tuple       	=   self.tokenizer.lang_code_to_id[hparams.tgt_lang]




	def 				UpperCAmelCase__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						Tuple						,   _SCREAMING_SNAKE_CASE		:						List[Any]						)->			dict:
			return calculate_bleu(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)




	@staticmethod
	def 				UpperCAmelCase__(   _SCREAMING_SNAKE_CASE		:						Any						,   _SCREAMING_SNAKE_CASE		:						Any						)->			int:
			TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)
			add_distill_args(_SCREAMING_SNAKE_CASE						)
			return parser






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if args.no_teacher:
				lowerCAmelCase__  :     Any       	=   TranslationModule if '''translation''' in args.task else SummarizationModule
		else:  # DISTILL WITH TEACHER
				lowerCAmelCase__  :     Optional[int]       	=   TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
		lowerCAmelCase__  :     str       	=   module_cls.__name__
		print(f'using module {args.setup_cls}'			)
		lowerCAmelCase__  :     Any       	=   module_cls(_a			)
		return model






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






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

		lowerCAmelCase__  :     str       	=   create_module(_a			)
		return ft_main(_a      ,  model=_a			)


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

		distill_main(args)








from __future__ import annotations

from string import ascii_letters






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a = None			):




		"""simple docstring"""






		lowerCAmelCase__  :     List[Any]       	=   alphabet or ascii_letters

		# The final result string
		lowerCAmelCase__  :     Optional[Any]       	=   ''''''

		for character in input_string:
				if character not in alpha:
						# Append without encryption if character is not in the alphabet
						result += character
				else:
						# Get the index of the new key and make sure it isn't too large
						lowerCAmelCase__  :     int       	=   (alpha.index(_a			) + key) % len(_a			)

						# Append the encoded character to the alphabet
						result += alpha[new_key]

		return result






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a = None			):




		"""simple docstring"""






		key *= -1

		return encrypt(_a      ,  _a      ,  _a			)






def 	lowerCamelCase_       (			_a      ,  _a = None			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   alphabet or ascii_letters

		# To store data on all the combinations
		lowerCAmelCase__  :     Optional[int]       	=   {}

		# Cycle through each combination
		for key in range(1      ,  len(_a			) + 1			):
				# Decrypt the message and store the result in the data
				lowerCAmelCase__  :     Optional[int]       	=   decrypt(_a      ,  _a      ,  _a			)

		return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 10}\n Menu\n{"-" * 10}''')
				print(*['''1.Encrypt''', '''2.Decrypt''', '''3.BruteForce''', '''4.Quit'''], sep='''\n''')

				# get user input
				lowerCamelCase		=						input('''\nWhat would you like to do?: ''').strip() or '''4'''

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print('''Invalid choice, please enter a valid choice''')
				elif choice == "1":
						lowerCamelCase		=						input('''Please enter the string to be encrypted: ''')
						lowerCamelCase		=						int(input('''Please enter off-set: ''').strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						lowerCamelCase		=						input('''Please enter the string to be decrypted: ''')
						lowerCamelCase		=						int(input('''Please enter off-set: ''').strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						lowerCamelCase		=						input('''Please enter the string to be decrypted: ''')
						lowerCamelCase		=						brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f'''Key: {key} | Message: {value}''')

				elif choice == "4":
						print('''Goodbye.''')
						break




