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__						( __A ):
					A     	=							['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']

					def __init__(   self				:	str,_A				:	Union[str, Any]   ):







							"""simple docstring"""






							assert Path(hparams.data_dir   ).exists()
							SCREAMING_SNAKE_CASE_  :		int    				=   Path(hparams.output_dir   )
							self.output_dir.mkdir(exist_ok=_A   )

							SCREAMING_SNAKE_CASE_  :		str    				=   self.output_dir.joinpath("student"   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   str(_A   )  # Tell lightning we are training the student
							SCREAMING_SNAKE_CASE_  :		str    				=   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:
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
									use_task_specific_params(_A,hparams.task   )
									SCREAMING_SNAKE_CASE_  :		int    				=   None, None
							else:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   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:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   hparams.length_penalty
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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":
									SCREAMING_SNAKE_CASE_  :		Any    				=   len(student.get_encoder().block   )
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   len(student.get_decoder().block   )
									SCREAMING_SNAKE_CASE_  :		str    				=   len(teacher.get_encoder().block   )
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   len(teacher.get_decoder().block   )
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   student.config.encoder_layers
									SCREAMING_SNAKE_CASE_  :		str    				=   student.config.decoder_layers
									SCREAMING_SNAKE_CASE_  :		int    				=   teacher.config.encoder_layers
									SCREAMING_SNAKE_CASE_  :		Dict    				=   teacher.config.decoder_layers

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

							SCREAMING_SNAKE_CASE_  :		Dict    				=   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:
											SCREAMING_SNAKE_CASE_  :		Tuple    				=   get_layers_to_supervise(
											    n_student=len(self.e_layer_ids   ),n_teacher=_A   )
											SCREAMING_SNAKE_CASE_  :		Dict    				=   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
											SCREAMING_SNAKE_CASE_  :		Any    				=   self.e_layer_ids
											SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.d_layer_ids
							else:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   None
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   None

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

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	List[Any],_A				:	str,_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   mask[:, :, None].expand_as(_A   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   s_logits.size(-1   )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.masked_select(_A,_A   )  # (bs * seq_length * voc_size) modulo the 1s in mask
							SCREAMING_SNAKE_CASE_  :		str    				=   torch.masked_select(_A,_A   )  # (bs * seq_length * voc_size) modulo the 1s in mask
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   s_logits_slct.view(-1,_A   )  # (bs * seq_length, voc_size) modulo the 1s in mask
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   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()
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   (
							    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       (   _A				:	Any,_A				:	Tuple   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.tokenizer.pad_token_id
							SCREAMING_SNAKE_CASE_  :		str    				=   batch["input_ids"], batch["attention_mask"], batch["labels"]
							if isinstance(self.model,_A   ):
									SCREAMING_SNAKE_CASE_  :		Any    				=   self.model._shift_right(_A   )
							else:
									SCREAMING_SNAKE_CASE_  :		Dict    				=   shift_tokens_right(_A,_A   )

							# noinspection PyCallingNonCallable
							SCREAMING_SNAKE_CASE_  :		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,)
							SCREAMING_SNAKE_CASE_  :		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
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   nn.CrossEntropyLoss(ignore_index=_A   )
									SCREAMING_SNAKE_CASE_  :		int    				=   loss_fct(lm_logits.view(-1,lm_logits.shape[-1]   ),labels.view(-1   )   )
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   nn.functional.log_softmax(_A,dim=-1   )
									SCREAMING_SNAKE_CASE_  :		Optional[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   )

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   student_outputs[
							    "encoder_last_hidden_state"
							]  # use this unless self.different_base_models
							SCREAMING_SNAKE_CASE_  :		str    				=   zero_tensor(), zero_tensor()
							if self.different_encoder:  # compute encoder hidden state loss
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.teacher.get_encoder()(
									    _A,attention_mask=_A,output_hidden_states=self.do_calc_hidden_loss,)
									if self.different_base_models:
											SCREAMING_SNAKE_CASE_  :		int    				=   all_teacher_encoder_outputs["last_hidden_state"]
									elif self.do_calc_hidden_loss:
											SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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,)

							SCREAMING_SNAKE_CASE_  :		List[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,)
							SCREAMING_SNAKE_CASE_  :		Any    				=   decoder_input_ids.ne(_A   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.calc_ce_loss(_A,_A,teacher_outputs["logits"]   )
							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
									SCREAMING_SNAKE_CASE_  :		Optional[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,)

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   (
							    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       (   _A				:	Optional[int],_A				:	List[str],_A				:	Optional[int],_A				:	Any,_A				:	Dict   ):







							"""simple docstring"""






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







def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""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=1_2       ,			type=_a       ,			required=_a     )
		parser.add_argument("--student_encoder_layers"       ,			default=1_2       ,			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__						( __A ):
					A     	=							'translation'
					A     	=							['bleu']
					A     	=							'bleu'

					def __init__(   self				:	Optional[Any],_A				:	Optional[int],**_A				:	Union[str, Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Any,_A				:	List[Any],_A				:	Tuple   ):







							"""simple docstring"""






							return calculate_bleu(_A,_A   )






					@staticmethod
					def     __UpperCamelCase       (   _A				:	Any,_A				:	List[Any]   ):







							"""simple docstring"""






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







def 				_snake_case     (       lowerCAmelCase							:       Tuple     ):
		"""simple docstring"""







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




def 				_snake_case     (       lowerCAmelCase							:       List[Any]     ):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   create_module(_a     )
		return ft_main(_a       ,			model=_a     )


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

					distill_main(args)

from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


__lowerCamelCase :     str          =  AttentionProcessor






class 		a__						( A__ ):

					def __init__(   self				:	Tuple,*_A				:	Union[str, Any],**_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	str,*_A				:	Optional[Any],**_A				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	str,*_A				:	Dict,**_A				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	List[Any],*_A				:	List[str],**_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Optional[int],*_A				:	Any,**_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Union[str, Any],*_A				:	Union[str, Any],**_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Dict,*_A				:	Dict,**_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	int,*_A				:	Dict,**_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )
