




'''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    A						(					_lowerCamelCase						):




				'''simple docstring'''





				A       =							['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']
				def __init__(self					,    _UpperCAmelCase     )      ->			Optional[Any]:
							assert Path(hparams.data_dir     ).exists()
							__UpperCamelCase :     Union[str, Any]										=  Path(hparams.output_dir     )
							self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )

							__UpperCamelCase :     Any										=  self.output_dir.joinpath("student"     )

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

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

							__UpperCamelCase :     Tuple										=  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 :     Optional[int]										=  get_layers_to_supervise(
													    n_student=len(self.e_layer_ids     )					,    n_teacher=_SCREAMING_SNAKE_CASE     )
													__UpperCamelCase :     Any										=  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
													__UpperCamelCase :     Tuple										=  self.e_layer_ids
													__UpperCamelCase :     Union[str, Any]										=  self.d_layer_ids
							else:
										__UpperCamelCase :     Tuple										=  None
										__UpperCamelCase :     str										=  None

							__UpperCamelCase :     List[str]										=  nn.KLDivLoss(reduction="batchmean"     )
							__UpperCamelCase :     Dict										=  2.0
							__UpperCamelCase :     str										=  hparams.alpha_mlm
							__UpperCamelCase :     int										=  hparams.alpha_ce
							__UpperCamelCase :     Union[str, Any]										=  hparams.alpha_hid
							gc.collect()
							torch.cuda.empty_cache()
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     List[Any]										=  mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE     )
							__UpperCamelCase :     Dict										=  s_logits.size(-1     )
							__UpperCamelCase :     Optional[int]										=  torch.masked_select(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE     )  # (bs * seq_length * voc_size) modulo the 1s in mask
							__UpperCamelCase :     Union[str, Any]										=  torch.masked_select(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE     )  # (bs * seq_length * voc_size) modulo the 1s in mask
							__UpperCamelCase :     List[str]										=  s_logits_slct.view(-1					,    _SCREAMING_SNAKE_CASE     )  # (bs * seq_length, voc_size) modulo the 1s in mask
							__UpperCamelCase :     Optional[int]										=  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()
							__UpperCamelCase :     int										=  (
							    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 			a_			(_UpperCAmelCase					,    _UpperCAmelCase     )      ->			List[Any]:
							SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE     )
							add_distill_args(_SCREAMING_SNAKE_CASE     )
							return parser
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     List[Any]										=  self.tokenizer.pad_token_id
							__UpperCamelCase :     Optional[Any]										=  batch['input_ids'], batch['attention_mask'], batch['labels']
							if isinstance(self.model					,    _SCREAMING_SNAKE_CASE     ):
										__UpperCamelCase :     Any										=  self.model._shift_right(_SCREAMING_SNAKE_CASE     )
							else:
										__UpperCamelCase :     str										=  shift_tokens_right(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE     )

							# noinspection PyCallingNonCallable
							__UpperCamelCase :     List[str]										=  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					,    )
							__UpperCamelCase :     Dict										=  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 :     Optional[Any]										=  nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE     )
										__UpperCamelCase :     List[str]										=  loss_fct(lm_logits.view(-1					,    lm_logits.shape[-1]     )					,    labels.view(-1     )     )
							else:
										__UpperCamelCase :     Any										=  nn.functional.log_softmax(_SCREAMING_SNAKE_CASE					,    dim=-1     )
										__UpperCamelCase :     List[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     )

							__UpperCamelCase :     Optional[Any]										=  student_outputs[
							    'encoder_last_hidden_state'
							]  # use this unless self.different_base_models
							__UpperCamelCase :     Dict										=  zero_tensor(), zero_tensor()
							if self.different_encoder:  # compute encoder hidden state loss
										__UpperCamelCase :     List[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:
													__UpperCamelCase :     Dict										=  all_teacher_encoder_outputs['last_hidden_state']
										elif self.do_calc_hidden_loss:
													__UpperCamelCase :     int										=  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					,    )

							__UpperCamelCase :     Optional[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					,    )
							__UpperCamelCase :     Any										=  decoder_input_ids.ne(_SCREAMING_SNAKE_CASE     )
							__UpperCamelCase :     Tuple										=  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
										__UpperCamelCase :     Any										=  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					,    )

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







def      __lowerCAmelCase   (       snake_case__		):
			# NOTE: if --student argument was specified and the teacher and student base models
			# are different, the models still have to have the same tokenizer, specified by
			# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
			# from bart to t5. This s because if the tokenizers are different, the output space
			# for the two models is also different and their logits are not comparable.
			parser.add_argument("--teacher"   , type=_UpperCamelCase		)
			parser.add_argument("--alpha_ce"   , default=0.8   , type=_UpperCamelCase		)
			parser.add_argument("--alpha_mlm"   , default=0.2   , type=_UpperCamelCase		)
			parser.add_argument("--alpha_hid"   , default=0.0   , type=_UpperCamelCase   , required=_UpperCamelCase		)
			parser.add_argument("--student"   , type=_UpperCamelCase   , required=_UpperCamelCase		)
			parser.add_argument("--student_decoder_layers"   , default=12   , type=_UpperCamelCase   , required=_UpperCamelCase		)
			parser.add_argument("--student_encoder_layers"   , default=12   , type=_UpperCamelCase   , required=_UpperCamelCase		)
			parser.add_argument("--no_teacher"   , action="store_true"   , default=_UpperCamelCase		)
			parser.add_argument("--length_penalty"   , type=_UpperCamelCase   , default=-1		)
			parser.add_argument("--supervise_forward"   , action="store_true"   , default=_UpperCamelCase		)
			parser.add_argument("--normalize_hidden"   , action="store_true"   , default=_UpperCamelCase		)



class    A						(					_lowerCamelCase						):




				'''simple docstring'''





				A       =							'translation'
				A       =							['bleu']
				A       =							'bleu'
				def __init__(self					,    _UpperCAmelCase					,    **_UpperCAmelCase     )      ->			str:
							super().__init__(_SCREAMING_SNAKE_CASE					,    **_SCREAMING_SNAKE_CASE     )
							assert hparams.src_lang is not None
							assert hparams.tgt_lang is not None
							__UpperCamelCase :     str										=  hparams.src_lang
							__UpperCamelCase :     Optional[int]										=  hparams.tgt_lang
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer					,    _SCREAMING_SNAKE_CASE     ):
										__UpperCamelCase :     str										=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Tuple:
							return calculate_bleu(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE     )





				@staticmethod
				def 			a_			(_UpperCAmelCase					,    _UpperCAmelCase     )      ->			Dict:
							TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE     )
							add_distill_args(_SCREAMING_SNAKE_CASE     )
							return parser







def      __lowerCAmelCase   (       snake_case__		):
			if args.no_teacher:
						__UpperCamelCase :     Tuple										=  TranslationModule if 'translation' in args.task else SummarizationModule
			else:  # DISTILL WITH TEACHER
						__UpperCamelCase :     List[str]										=  TranslationDistiller if 'translation' in args.task else SummarizationDistiller
			__UpperCamelCase :     str										=  module_cls.__name__
			print(F"using module {args.setup_cls}"		)
			__UpperCamelCase :     Optional[int]										=  module_cls(_UpperCamelCase		)
			return model

def      __lowerCAmelCase   (       snake_case__		):
			Path(args.output_dir		).mkdir(exist_ok=_UpperCamelCase		)
			check_output_dir(_UpperCamelCase   , expected_items=3		)

			__UpperCamelCase :     List[Any]										=  create_module(_UpperCamelCase		)
			return ft_main(_UpperCamelCase   , model=_UpperCamelCase		)


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)





"""simple docstring"""







from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = (None, None)			,						_SCREAMING_SNAKE_CASE = None			,						):
			super().__init__(
			    model=_SCREAMING_SNAKE_CASE			,						args=_SCREAMING_SNAKE_CASE			,						data_collator=_SCREAMING_SNAKE_CASE			,						train_dataset=_SCREAMING_SNAKE_CASE			,						eval_dataset=_SCREAMING_SNAKE_CASE			,						tokenizer=_SCREAMING_SNAKE_CASE			,						model_init=_SCREAMING_SNAKE_CASE			,						compute_metrics=_SCREAMING_SNAKE_CASE			,						callbacks=_SCREAMING_SNAKE_CASE			,						optimizers=_SCREAMING_SNAKE_CASE			,						preprocess_logits_for_metrics=_SCREAMING_SNAKE_CASE			,						)

			# Override self.model.generation_config if a GenerationConfig is specified in args.
			# Priority: args.generation_config > model.generation_config > default GenerationConfig.
			if self.args.generation_config is not None:
				__lowerCAmelCase     :		Tuple		     = self.load_generation_config(self.args.generation_config					)
				__lowerCAmelCase     :		int		     = gen_config

		@staticmethod
		def 				__lowerCamelCase				( _SCREAMING_SNAKE_CASE					):

			# GenerationConfig provided, nothing to do
			if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
				return deepcopy(_SCREAMING_SNAKE_CASE					)

			# str or Path
			__lowerCAmelCase     :		Dict		     = Path(_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else gen_config_arg
			__lowerCAmelCase     :		Any		     = None

			# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
			# This step is required in order to determine config_file_name
			if pretrained_model_name.is_file():
				__lowerCAmelCase     :		List[Any]		     = pretrained_model_name.name
				__lowerCAmelCase     :		Tuple		     = pretrained_model_name.parent
			# dir path
			elif pretrained_model_name.is_dir():
				pass
			# model id or URL
			else:
				__lowerCAmelCase     :		List[Any]		     = gen_config_arg

			__lowerCAmelCase     :		Optional[int]		     = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			return gen_config

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "eval"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Any		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		Tuple		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Optional[int]		     = gen_kwargs

			return super().evaluate(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "test"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Dict		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Tuple		     = gen_kwargs

			return super().predict(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):

			if not self.args.predict_with_generate or prediction_loss_only:
				return super().prediction_step(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						prediction_loss_only=_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = 'labels' in inputs
			__lowerCAmelCase     :		List[str]		     = self._prepare_inputs(_SCREAMING_SNAKE_CASE					)

			# XXX: adapt synced_gpus for fairscale as well
			# Priority (handled in generate):
			# gen_kwargs > model.generation_config > default GenerationConfig()

			if len(_SCREAMING_SNAKE_CASE					) == 0 and hasattr(self			,						'_gen_kwargs'					):
				__lowerCAmelCase     :		Dict		     = self._gen_kwargs.copy()

			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Tuple		     = self.model.config.max_length
			__lowerCAmelCase     :		Optional[Any]		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.model.config.num_beams
			)
			__lowerCAmelCase     :		Union[str, Any]		     = True if is_deepspeed_zeroa_enabled() else False
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'					) is not None else default_synced_gpus
			)

			# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
			# (otherwise, it would continue generating from the padded `decoder_input_ids`)
			if (
			    "labels" in inputs
			    and "decoder_input_ids" in inputs
			    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
			):
				__lowerCAmelCase     :		Optional[int]		     = {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
			__lowerCAmelCase     :		Union[str, Any]		     = self.model.generate(**_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

			# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
			# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
			# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
			if self.model.generation_config._from_model_config:
				__lowerCAmelCase     :		str		     = False

			# Retrieves GenerationConfig from model.generation_config
			__lowerCAmelCase     :		List[Any]		     = self.model.generation_config
			# in case the batch is shorter than max length, the output should be padded
			if generated_tokens.shape[-1] < gen_config.max_length:
				__lowerCAmelCase     :		Any		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
			elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
				__lowerCAmelCase     :		Optional[Any]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)

			with torch.no_grad():
				if has_labels:
					with self.compute_loss_context_manager():
						__lowerCAmelCase     :		List[Any]		     = model(**_SCREAMING_SNAKE_CASE					)
					if self.label_smoother is not None:
						__lowerCAmelCase     :		Optional[int]		     = self.label_smoother(_SCREAMING_SNAKE_CASE			,						inputs['labels']					).mean().detach()
					else:
						__lowerCAmelCase     :		Optional[int]		     = (outputs['loss'] if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else outputs[0]).mean().detach()
				else:
					__lowerCAmelCase     :		Optional[int]		     = None

			if self.args.prediction_loss_only:
				return loss, None, None

			if has_labels:
				__lowerCAmelCase     :		Tuple		     = inputs['labels']
				if labels.shape[-1] < gen_config.max_length:
					__lowerCAmelCase     :		List[str]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
				elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
					__lowerCAmelCase     :		int		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)
			else:
				__lowerCAmelCase     :		Optional[int]		     = None

			return loss, generated_tokens, labels







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if self.tokenizer is not None and hasattr(self.tokenizer			,						'pad_token_id'					):
				# If PAD token is not defined at least EOS token has to be defined
				__lowerCAmelCase     :		Any		     = (
				    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
				)
			else:
				if self.model.config.pad_token_id is not None:
					__lowerCAmelCase     :		List[str]		     = self.model.config.pad_token_id
				else:
					raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'					)

			__lowerCAmelCase     :		Dict		     = pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)			,						dtype=tensor.dtype			,						device=tensor.device					)
			__lowerCAmelCase     :		Tuple		     = tensor
			return padded_tensor