




"""simple docstring"""







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  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


						def __init__(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										assert Path(hparams.data_dir   ).exists()
										lowerCAmelCase		=			Path(hparams.output_dir   )
										self.output_dir.mkdir(exist_ok=lowerCAmelCase   )

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

										lowerCAmelCase		=			str(lowerCAmelCase   )  # Tell lightning we are training the student
										lowerCAmelCase		=			AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher   ).eval()
										use_task_specific_params(lowerCAmelCase  ,  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(lowerCAmelCase  ,  hparams.task   )
														lowerCAmelCase	,  lowerCAmelCase		=			None, None
										else:
														lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			create_student_by_copying_alternating_layers(
														    lowerCAmelCase  ,  e=hparams.student_encoder_layers  ,  d=hparams.student_decoder_layers  ,  save_path=lowerCAmelCase   )

										if hparams.length_penalty != -1:
														lowerCAmelCase		=			hparams.length_penalty
										lowerCAmelCase		=			hparams.teacher  # Use teacher's tokenizer
										super().__init__(lowerCAmelCase  ,  model=lowerCAmelCase  ,  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(lowerCAmelCase   )   )
										if d_layer_ids is None:
														lowerCAmelCase		=			list(range(lowerCAmelCase   )   )

										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=lowerCAmelCase   )
																		lowerCAmelCase		=			get_layers_to_supervise(
																		    n_student=len(self.d_layer_ids   )  ,  n_teacher=lowerCAmelCase   )
														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 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     List[Any]   ):
										lowerCAmelCase		=			mask[:, :, None].expand_as(lowerCAmelCase   )
										lowerCAmelCase		=			s_logits.size(-1   )
										lowerCAmelCase		=			torch.masked_select(lowerCAmelCase  ,  lowerCAmelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCAmelCase		=			torch.masked_select(lowerCAmelCase  ,  lowerCAmelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCAmelCase		=			s_logits_slct.view(-1  ,  lowerCAmelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
										lowerCAmelCase		=			t_logits_slct.view(-1  ,  lowerCAmelCase   )  # (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 					__lowercase	(							lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     Union[str, Any]   ):
										SummarizationModule.add_model_specific_args(lowerCAmelCase  ,  lowerCAmelCase   )
										add_distill_args(lowerCAmelCase   )
										return parser


						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     dict   ):
										lowerCAmelCase		=			self.tokenizer.pad_token_id
										lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
										if isinstance(self.model  ,  lowerCAmelCase   ):
														lowerCAmelCase		=			self.model._shift_right(lowerCAmelCase   )
										else:
														lowerCAmelCase		=			shift_tokens_right(lowerCAmelCase  ,  lowerCAmelCase   )

										# noinspection PyCallingNonCallable
										lowerCAmelCase		=			self(
										    lowerCAmelCase  ,  attention_mask=lowerCAmelCase  ,  decoder_input_ids=lowerCAmelCase  ,  output_hidden_states=self.do_calc_hidden_loss  ,  output_attentions=lowerCAmelCase  ,  use_cache=lowerCAmelCase  ,  )
										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=lowerCAmelCase   )
														lowerCAmelCase		=			loss_fct(lm_logits.view(-1  ,  lm_logits.shape[-1]   )  ,  labels.view(-1   )   )
										else:
														lowerCAmelCase		=			nn.functional.log_softmax(lowerCAmelCase  ,  dim=-1   )
														lowerCAmelCase	,  lowerCAmelCase		=			label_smoothed_nll_loss(
														    lowerCAmelCase  ,  lowerCAmelCase  ,  self.hparams.label_smoothing  ,  ignore_index=lowerCAmelCase   )

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

										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()(
														    lowerCAmelCase  ,  attention_mask=lowerCAmelCase  ,  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(
																		    lowerCAmelCase  ,  student_outputs["""encoder_hidden_states"""]  ,  all_teacher_encoder_outputs["""hidden_states"""]  ,  self.e_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

										lowerCAmelCase		=			self.teacher(
										    lowerCAmelCase  ,  attention_mask=lowerCAmelCase  ,  encoder_outputs=(teacher_enc_outputs,)  ,  decoder_input_ids=lowerCAmelCase  ,  output_hidden_states=self.do_calc_hidden_loss  ,  use_cache=lowerCAmelCase  ,  )
										lowerCAmelCase		=			decoder_input_ids.ne(lowerCAmelCase   )
										lowerCAmelCase		=			self.calc_ce_loss(lowerCAmelCase  ,  lowerCAmelCase  ,  teacher_outputs["""logits"""]   )
										if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
														lowerCAmelCase		=			self.calc_hidden_loss(
														    lowerCAmelCase  ,  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 					__lowercase	(							lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			"""expected list or tuple for hidden_states, got tensor of shape: """
										assert not isinstance(lowerCAmelCase  ,  torch.Tensor   ), f'''{msg}{hidden_states.shape}'''
										assert not isinstance(lowerCAmelCase  ,  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(lowerCAmelCase   )   )]   )
										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(lowerCAmelCase  ,  student_states.shape[1:]   )
														lowerCAmelCase		=			nn.functional.layer_norm(lowerCAmelCase  ,  teacher_states.shape[1:]   )
										lowerCAmelCase		=			nn.functional.mse_loss(lowerCAmelCase  ,  lowerCAmelCase  ,  reduction="""none"""   )
										lowerCAmelCase		=			(mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
										return masked_mse





def 							lowercase   (snake_case__				:	List[Any]	)       ->					str:


				'''simple docstring'''






				parser.add_argument("""--teacher""" ,    type=snake_case__	)
				parser.add_argument("""--alpha_ce""" ,    default=0.8 ,    type=snake_case__	)
				parser.add_argument("""--alpha_mlm""" ,    default=0.2 ,    type=snake_case__	)
				parser.add_argument("""--alpha_hid""" ,    default=0.0 ,    type=snake_case__ ,    required=snake_case__	)
				parser.add_argument("""--student""" ,    type=snake_case__ ,    required=snake_case__	)
				parser.add_argument("""--student_decoder_layers""" ,    default=12 ,    type=snake_case__ ,    required=snake_case__	)
				parser.add_argument("""--student_encoder_layers""" ,    default=12 ,    type=snake_case__ ,    required=snake_case__	)
				parser.add_argument("""--no_teacher""" ,    action="""store_true""" ,    default=snake_case__	)
				parser.add_argument("""--length_penalty""" ,    type=snake_case__ ,    default=-1	)
				parser.add_argument("""--supervise_forward""" ,    action="""store_true""" ,    default=snake_case__	)
				parser.add_argument("""--normalize_hidden""" ,    action="""store_true""" ,    default=snake_case__	)






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  'translation'
						_a        =  ['bleu']
						_a        =  'bleu'


						def __init__(							self			:     Dict  ,  lowerCAmelCase			:     Tuple  ,  **lowerCAmelCase			:     Optional[Any]   ):
										super().__init__(lowerCAmelCase  ,  **lowerCAmelCase   )
										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  ,  lowerCAmelCase   ):
														lowerCAmelCase		=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]


						def 					__lowercase	(							self			:     List[str]  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     Tuple   ):
										return calculate_bleu(lowerCAmelCase  ,  lowerCAmelCase   )




						@staticmethod
						def 					__lowercase	(							lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     Optional[int]   ):
										TranslationModule.add_model_specific_args(lowerCAmelCase  ,  lowerCAmelCase   )
										add_distill_args(lowerCAmelCase   )
										return parser





def 							lowercase   (snake_case__				:	List[Any]	)       ->					Dict:


				'''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(snake_case__	)
				return model





def 							lowercase   (snake_case__				:	Union[str, Any]	)       ->					List[str]:


				'''simple docstring'''






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

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


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)











"""simple docstring"""







def 							lowercase   (snake_case__				:	list	)       ->					bool:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [2, 4, 8]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				if len(snake_case__	) == 1:
								return True
				try:
								lowerCAmelCase		=			series[1] / series[0]
								for index in range(len(snake_case__	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True





def 							lowercase   (snake_case__				:	list	)       ->					float:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [2, 4, 8]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				lowerCAmelCase		=			1
				for value in series:
								answer *= value
				return pow(snake_case__ ,    1 / len(snake_case__	)	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





