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






			def __init__(self     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  Optional[Any] )					->  Optional[Any]:





				"""simple docstring"""


				assert Path(hparams.data_dir ).exists()
				lowerCAmelCase				       =     Path(hparams.output_dir )
				self.output_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     self.output_dir.joinpath('''student''' )

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

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

				lowerCAmelCase       ,					lowerCAmelCase				       =     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				       =     get_layers_to_supervise(
						    n_student=len(self.e_layer_ids )			,n_teacher=SCREAMING_SNAKE_CASE_ )
						lowerCAmelCase				       =     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				       =     self.e_layer_ids
						lowerCAmelCase				       =     self.d_layer_ids
				else:
					lowerCAmelCase				       =     None
					lowerCAmelCase				       =     None

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






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  Tuple			,SCREAMING_SNAKE_CASE_     :  List[str] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     mask[:, :, None].expand_as(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     s_logits.size(-1 )
				lowerCAmelCase				       =     torch.masked_select(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )  # (bs * seq_length * voc_size) modulo the 1s in mask
				lowerCAmelCase				       =     torch.masked_select(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )  # (bs * seq_length * voc_size) modulo the 1s in mask
				lowerCAmelCase				       =     s_logits_slct.view(-1			,SCREAMING_SNAKE_CASE_ )  # (bs * seq_length, voc_size) modulo the 1s in mask
				lowerCAmelCase				       =     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				       =     (
				    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_     :  Dict			,SCREAMING_SNAKE_CASE_     :  List[str] )					->  List[Any]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  List[Any] )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     self.tokenizer.pad_token_id
				lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
				if isinstance(self.model			,SCREAMING_SNAKE_CASE_ ):
					lowerCAmelCase				       =     self.model._shift_right(SCREAMING_SNAKE_CASE_ )
				else:
					lowerCAmelCase				       =     shift_tokens_right(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

				# noinspection PyCallingNonCallable
				lowerCAmelCase				       =     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				       =     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				       =     nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     loss_fct(lm_logits.view(-1			,lm_logits.shape[-1] )			,labels.view(-1 ) )
				else:
					lowerCAmelCase				       =     nn.functional.log_softmax(SCREAMING_SNAKE_CASE_			,dim=-1 )
					lowerCAmelCase       ,					lowerCAmelCase				       =     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				       =     student_outputs[
				    '''encoder_last_hidden_state'''
				]  # use this unless self.different_base_models
				lowerCAmelCase       ,					lowerCAmelCase				       =     zero_tensor(), zero_tensor()
				if self.different_encoder:  # compute encoder hidden state loss
					lowerCAmelCase				       =     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				       =     all_teacher_encoder_outputs['''last_hidden_state''']
					elif self.do_calc_hidden_loss:
						lowerCAmelCase				       =     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				       =     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				       =     decoder_input_ids.ne(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     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				       =     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				       =     (
				    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_     :  Any			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Any			,SCREAMING_SNAKE_CASE_     :  str			,SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Tuple:





				"""simple docstring"""


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





def __magic_name__  (							_lowerCamelCase:		str )				->	str:

	'''simple docstring'''




	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=12,				type=lowercase__,				required=lowercase__ )
	parser.add_argument('''--student_encoder_layers''',				default=12,				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  lowercase				(   A_		):
			lowercase			     =    '''translation'''
			lowercase			     =    ['''bleu''']
			lowercase			     =    '''bleu'''






			def __init__(self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Optional[int]			,**SCREAMING_SNAKE_CASE_     :  int )					->  Any:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Tuple			,SCREAMING_SNAKE_CASE_     :  Dict )					->  Optional[int]:





				"""simple docstring"""


				return calculate_bleu(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Dict			,SCREAMING_SNAKE_CASE_     :  str )					->  Tuple:





				"""simple docstring"""


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





def __magic_name__  (							_lowerCamelCase:		str )				->	Optional[int]:

	'''simple docstring'''




	if args.no_teacher:
		lowerCAmelCase				       =     TranslationModule if '''translation''' in args.task else SummarizationModule
	else:  # DISTILL WITH TEACHER
		lowerCAmelCase				       =     TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
	lowerCAmelCase				       =     module_cls.__name__
	print(F"""using module {args.setup_cls}""" )
	lowerCAmelCase				       =     module_cls(lowercase__ )
	return model




def __magic_name__  (							_lowerCamelCase:		int )				->	Any:

	'''simple docstring'''




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

	lowerCAmelCase				       =     create_module(lowercase__ )
	return ft_main(lowercase__,				model=lowercase__ )


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

					distill_main(args)




"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  lowercase				(   lowercase__		):
			lowercase			     =    '''AutoFeatureExtractor'''
			lowercase			     =    '''Speech2Text2Tokenizer'''






			def __init__(self     :  Tuple			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  int:





				"""simple docstring"""


				super().__init__(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     self.feature_extractor
				lowerCAmelCase				       =     False






			def __call__(self     :  Tuple			,*SCREAMING_SNAKE_CASE_     :  Optional[Any]			,**SCREAMING_SNAKE_CASE_     :  Dict )					->  Any:





				"""simple docstring"""


				if self._in_target_context_manager:
					return self.current_processor(*SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )

				if "raw_speech" in kwargs:
					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
					lowerCAmelCase				       =     kwargs.pop('''raw_speech''' )
				else:
					lowerCAmelCase				       =     kwargs.pop('''audio'''			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     kwargs.pop('''sampling_rate'''			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     kwargs.pop('''text'''			,SCREAMING_SNAKE_CASE_ )
				if len(SCREAMING_SNAKE_CASE_ ) > 0:
					lowerCAmelCase				       =     args[0]
					lowerCAmelCase				       =     args[1:]

				if audio is None and text is None:
					raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )

				if audio is not None:
					lowerCAmelCase				       =     self.feature_extractor(SCREAMING_SNAKE_CASE_			,*SCREAMING_SNAKE_CASE_			,sampling_rate=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )
				if text is not None:
					lowerCAmelCase				       =     self.tokenizer(SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )

				if text is None:
					return inputs
				elif audio is None:
					return encodings
				else:
					lowerCAmelCase				       =     encodings['''input_ids''']
					return inputs






			def 				UpperCAmelCase       (self     :  Optional[int]			,*SCREAMING_SNAKE_CASE_     :  Any			,**SCREAMING_SNAKE_CASE_     :  List[Any] )					->  str:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[int]			,*SCREAMING_SNAKE_CASE_     :  int			,**SCREAMING_SNAKE_CASE_     :  Any )					->  Dict:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			@contextmanager
			def 				UpperCAmelCase       (self     :  Optional[int] )					->  Union[str, Any]:





				"""simple docstring"""


				warnings.warn(
				    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
				    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
				    '''your audio inputs, or in a separate call.''' )
				lowerCAmelCase				       =     True
				lowerCAmelCase				       =     self.tokenizer
				yield
				lowerCAmelCase				       =     self.feature_extractor
				lowerCAmelCase				       =     False


