




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 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']






						def __init__(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							assert Path(hparams.data_dir						).exists()
							snake_case_		   =					Path(hparams.output_dir						)
							self.output_dir.mkdir(exist_ok=__lowercase						)

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

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

							if hparams.length_penalty != -1:
								snake_case_		   =					hparams.length_penalty
							snake_case_		   =					hparams.teacher  # Use teacher's tokenizer
							super().__init__(__lowercase	,     model=__lowercase	,     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":
								snake_case_		   =					len(student.get_encoder().block						)
								snake_case_		   =					len(student.get_decoder().block						)
								snake_case_		   =					len(teacher.get_encoder().block						)
								snake_case_		   =					len(teacher.get_decoder().block						)
							else:
								snake_case_		   =					student.config.encoder_layers
								snake_case_		   =					student.config.decoder_layers
								snake_case_		   =					teacher.config.encoder_layers
								snake_case_		   =					teacher.config.decoder_layers

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

							snake_case_		   =					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:
									snake_case_		   =					get_layers_to_supervise(
									    n_student=len(self.e_layer_ids						)	,     n_teacher=__lowercase						)
									snake_case_		   =					get_layers_to_supervise(
									    n_student=len(self.d_layer_ids						)	,     n_teacher=__lowercase						)
								else:  # student layer should emulate hidden states of the teacher layer it was copied from
									snake_case_		   =					self.e_layer_ids
									snake_case_		   =					self.d_layer_ids
							else:
								snake_case_		   =					None
								snake_case_		   =					None

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






						def 	snake_case__						(						self							:					List[str]	,     __lowercase							:					Tuple	,     __lowercase							:					int	,     __lowercase							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					mask[:, :, None].expand_as(__lowercase						)
							snake_case_		   =					s_logits.size(-1						)
							snake_case_		   =					torch.masked_select(__lowercase	,     __lowercase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
							snake_case_		   =					torch.masked_select(__lowercase	,     __lowercase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
							snake_case_		   =					s_logits_slct.view(-1	,     __lowercase						)  # (bs * seq_length, voc_size) modulo the 1s in mask
							snake_case_		   =					t_logits_slct.view(-1	,     __lowercase						)  # (bs * seq_length, voc_size) modulo the 1s in mask
							assert t_logits_slct.size() == s_logits_slct.size()
							snake_case_		   =					(
							    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 	snake_case__						(						__lowercase							:					List[Any]	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[Any]	,     __lowercase							:					dict						):






							"""simple docstring"""






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

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

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

							snake_case_		   =					student_outputs[
							    "encoder_last_hidden_state"
							]  # use this unless self.different_base_models
							snake_case_		   =					zero_tensor(), zero_tensor()
							if self.different_encoder:  # compute encoder hidden state loss
								snake_case_		   =					self.teacher.get_encoder()(
								    __lowercase	,     attention_mask=__lowercase	,     output_hidden_states=self.do_calc_hidden_loss	,     )
								if self.different_base_models:
									snake_case_		   =					all_teacher_encoder_outputs["last_hidden_state"]
								elif self.do_calc_hidden_loss:
									snake_case_		   =					self.calc_hidden_loss(
									    __lowercase	,     student_outputs["encoder_hidden_states"]	,     all_teacher_encoder_outputs["hidden_states"]	,     self.e_matches	,     normalize_hidden=self.hparams.normalize_hidden	,     )

							snake_case_		   =					self.teacher(
							    __lowercase	,     attention_mask=__lowercase	,     encoder_outputs=(teacher_enc_outputs,)	,     decoder_input_ids=__lowercase	,     output_hidden_states=self.do_calc_hidden_loss	,     use_cache=__lowercase	,     )
							snake_case_		   =					decoder_input_ids.ne(__lowercase						)
							snake_case_		   =					self.calc_ce_loss(__lowercase	,     __lowercase	,     teacher_outputs["logits"]						)
							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
								snake_case_		   =					self.calc_hidden_loss(
								    __lowercase	,     student_outputs["decoder_hidden_states"]	,     teacher_outputs["decoder_hidden_states"]	,     self.d_matches	,     normalize_hidden=self.hparams.normalize_hidden	,     )

							snake_case_		   =					(
							    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 	snake_case__						(						__lowercase							:					Dict	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					int	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					"expected list or tuple for hidden_states, got tensor of shape: "
							assert not isinstance(__lowercase	,     torch.Tensor						), f"{msg}{hidden_states.shape}"
							assert not isinstance(__lowercase	,     torch.Tensor						), f"{msg}{hidden_states_T.shape}"
							snake_case_		   =					attention_mask.to(hidden_states[0]						)
							snake_case_		   =					mask.sum() * hidden_states[0].size(-1						)
							snake_case_		   =					torch.stack([hidden_states[i] for i in range(len(__lowercase						)						)]						)
							snake_case_		   =					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:
								snake_case_		   =					nn.functional.layer_norm(__lowercase	,     student_states.shape[1:]						)
								snake_case_		   =					nn.functional.layer_norm(__lowercase	,     teacher_states.shape[1:]						)
							snake_case_		   =					nn.functional.mse_loss(__lowercase	,     __lowercase	,     reduction="none"						)
							snake_case_		   =					(mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
							return masked_mse







def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	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 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       'translation'
						lowerCAmelCase_							=       ['bleu']
						lowerCAmelCase_							=       'bleu'






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






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[Any]	,     __lowercase							:					int	,     __lowercase							:					List[Any]						):






							"""simple docstring"""






							return calculate_bleu(__lowercase	,     __lowercase						)






						@staticmethod
						def 	snake_case__						(						__lowercase							:					int	,     __lowercase							:					Dict						):






							"""simple docstring"""






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







def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





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

	snake_case_		   =					create_module(_UpperCamelCase							)
	return ft_main(_UpperCamelCase					,     model=_UpperCamelCase							)


if __name__ == "__main__":
		lowercase__    :					str					=						argparse.ArgumentParser()
		lowercase__    :					Union[str, Any]					=						pl.Trainer.add_argparse_args(parser)
		lowercase__    :					List[Any]					=						SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
		lowercase__    :					Tuple					=						parser.parse_args()

		distill_main(args)






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							return datasets.DatasetInfo(features=self.config.features						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							if not self.config.data_files:
								raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"						)
							snake_case_		   =					dl_manager.download_and_extract(self.config.data_files						)
							if isinstance(__lowercase	,     (str, list, tuple)						):
								snake_case_		   =					data_files
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,     gen_kwargs={"files": files}						)]
							snake_case_		   =					[]
							for split_name, files in data_files.items():
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								# Infer features is they are stoed in the arrow schema
								if self.info.features is None:
									for file in itertools.chain.from_iterable(__lowercase						):
										with open(__lowercase	,     "rb"						) as f:
											snake_case_		   =					datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowercase						).schema						)
										break
								splits.append(datasets.SplitGenerator(name=__lowercase	,     gen_kwargs={"files": files}						)						)
							return splits






						def 	snake_case__						(						self							:					str	,     __lowercase							:					pa.Table						):






							"""simple docstring"""






							if self.info.features is not None:
								# more expensive cast to support nested features with keys in a different order
								# allows str <-> int/float or str to Audio for example
								snake_case_		   =					table_cast(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											snake_case_		   =					pa.Table.from_batches([record_batch]						)
											# Uncomment for debugging (will print the Arrow table size and elements)
											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
											yield f"{file_idx}_{batch_idx}", self._cast_table(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
