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'''



	lowerCamelCase_   :      List[str]       =	["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]

	def __init__(       self							,			__UpperCAmelCase   ):
			assert Path(hparams.data_dir   ).exists()
			__A							:   Tuple	  =				Path(hparams.output_dir   )
			self.output_dir.mkdir(exist_ok=__UpperCAmelCase   )

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

			__A							:   str	  =				str(__UpperCAmelCase   )  # Tell lightning we are training the student
			__A							:   Union[str, Any]	  =				AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher   ).eval()
			use_task_specific_params(__UpperCAmelCase							,			hparams.task   )  # We copy good generation parameters to student by default
			if hparams.student is not None:
					__A							:   Tuple	  =				AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
					use_task_specific_params(__UpperCAmelCase							,			hparams.task   )
					__A  ,       __A							:   Optional[Any]	  =				None, None
			else:
					__A  ,       __A  ,       __A							:   int	  =				create_student_by_copying_alternating_layers(
					    __UpperCAmelCase							,			e=hparams.student_encoder_layers							,			d=hparams.student_decoder_layers							,			save_path=__UpperCAmelCase   )

			if hparams.length_penalty != -1:
					__A							:   Union[str, Any]	  =				hparams.length_penalty
			__A							:   Optional[int]	  =				hparams.teacher  # Use teacher's tokenizer
			super().__init__(__UpperCAmelCase							,			model=__UpperCAmelCase							,			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":
					__A							:   Union[str, Any]	  =				len(student.get_encoder().block   )
					__A							:   Dict	  =				len(student.get_decoder().block   )
					__A							:   Dict	  =				len(teacher.get_encoder().block   )
					__A							:   int	  =				len(teacher.get_decoder().block   )
			else:
					__A							:   int	  =				student.config.encoder_layers
					__A							:   List[Any]	  =				student.config.decoder_layers
					__A							:   Optional[Any]	  =				teacher.config.encoder_layers
					__A							:   List[Any]	  =				teacher.config.decoder_layers

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

			__A  ,       __A							:   Optional[Any]	  =				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:
							__A							:   Any	  =				get_layers_to_supervise(
							    n_student=len(self.e_layer_ids   )							,			n_teacher=__UpperCAmelCase   )
							__A							:   int	  =				get_layers_to_supervise(
							    n_student=len(self.d_layer_ids   )							,			n_teacher=__UpperCAmelCase   )
					else:  # student layer should emulate hidden states of the teacher layer it was copied from
							__A							:   Union[str, Any]	  =				self.e_layer_ids
							__A							:   Dict	  =				self.d_layer_ids
			else:
					__A							:   Tuple	  =				None
					__A							:   Union[str, Any]	  =				None

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

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Tuple	  =				mask[:, :, None].expand_as(__UpperCAmelCase   )
			__A							:   int	  =				s_logits.size(-1   )
			__A							:   Tuple	  =				torch.masked_select(__UpperCAmelCase							,			__UpperCAmelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
			__A							:   Optional[int]	  =				torch.masked_select(__UpperCAmelCase							,			__UpperCAmelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
			__A							:   List[Any]	  =				s_logits_slct.view(-1							,			__UpperCAmelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
			__A							:   Optional[int]	  =				t_logits_slct.view(-1							,			__UpperCAmelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
			assert t_logits_slct.size() == s_logits_slct.size()
			__A							:   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 					__UpperCAmelCase(       __UpperCAmelCase							,			__UpperCAmelCase   ):
			SummarizationModule.add_model_specific_args(__UpperCAmelCase							,			__UpperCAmelCase   )
			add_distill_args(__UpperCAmelCase   )
			return parser

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   Optional[Any]	  =				self.tokenizer.pad_token_id
			__A  ,       __A  ,       __A							:   int	  =				batch["input_ids"], batch["attention_mask"], batch["labels"]
			if isinstance(self.model							,			__UpperCAmelCase   ):
					__A							:   Any	  =				self.model._shift_right(__UpperCAmelCase   )
			else:
					__A							:   Any	  =				shift_tokens_right(__UpperCAmelCase							,			__UpperCAmelCase   )

			# noinspection PyCallingNonCallable
			__A							:   Optional[int]	  =				self(
			    __UpperCAmelCase							,			attention_mask=__UpperCAmelCase							,			decoder_input_ids=__UpperCAmelCase							,			output_hidden_states=self.do_calc_hidden_loss							,			output_attentions=__UpperCAmelCase							,			use_cache=__UpperCAmelCase							,			)
			__A							:   Optional[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
					__A							:   Tuple	  =				nn.CrossEntropyLoss(ignore_index=__UpperCAmelCase   )
					__A							:   Dict	  =				loss_fct(lm_logits.view(-1							,			lm_logits.shape[-1]   )							,			labels.view(-1   )   )
			else:
					__A							:   Optional[int]	  =				nn.functional.log_softmax(__UpperCAmelCase							,			dim=-1   )
					__A  ,       __A							:   List[Any]	  =				label_smoothed_nll_loss(
					    __UpperCAmelCase							,			__UpperCAmelCase							,			self.hparams.label_smoothing							,			ignore_index=__UpperCAmelCase   )

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

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

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

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



def    lowerCamelCase_   (  _lowercase    )							->	Any:
		# 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=_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 _a       (	lowerCAmelCase__ ):

	'''simple docstring'''



	lowerCamelCase_   :      List[str]       =	"""translation"""
	lowerCamelCase_   :      List[str]       =	["""bleu"""]
	lowerCamelCase_   :      Optional[Any]       =	"""bleu"""

	def __init__(       self							,			__UpperCAmelCase							,			**__UpperCAmelCase   ):
			super().__init__(__UpperCAmelCase							,			**__UpperCAmelCase   )
			assert hparams.src_lang is not None
			assert hparams.tgt_lang is not None
			__A							:   Dict	  =				hparams.src_lang
			__A							:   Dict	  =				hparams.tgt_lang
			if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,			__UpperCAmelCase   ):
					__A							:   List[str]	  =				self.tokenizer.lang_code_to_id[hparams.tgt_lang]

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			return calculate_bleu(__UpperCAmelCase							,			__UpperCAmelCase   )







	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase							,			__UpperCAmelCase   ):
			TranslationModule.add_model_specific_args(__UpperCAmelCase							,			__UpperCAmelCase   )
			add_distill_args(__UpperCAmelCase   )
			return parser



def    lowerCamelCase_   (  _lowercase    )							->	Union[str, Any]:
		if args.no_teacher:
				__A							:   Union[str, Any]	  =				TranslationModule if "translation" in args.task else SummarizationModule
		else:  # DISTILL WITH TEACHER
				__A							:   Tuple	  =				TranslationDistiller if "translation" in args.task else SummarizationDistiller
		__A							:   str	  =				module_cls.__name__
		print(F"using module {args.setup_cls}"    )
		__A							:   Any	  =				module_cls(_lowercase    )
		return model



def    lowerCamelCase_   (  _lowercase    )							->	Union[str, Any]:
		Path(args.output_dir    ).mkdir(exist_ok=_lowercase    )
		check_output_dir(_lowercase ,     expected_items=3    )

		__A							:   Optional[Any]	  =				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)






def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Tuple:
		__A							:   Union[str, Any]	  =				index
		__A							:   int	  =				2 * index + 1
		__A							:   Dict	  =				2 * index + 2
		if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
				__A							:   Dict	  =				left_index

		if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
				__A							:   int	  =				right_index

		if largest != index:
				__A  ,       __A							:   Dict	  =				unsorted[index], unsorted[largest]
				heapify(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	Tuple:
		__A							:   Dict	  =				len(_lowercase    )
		for i in range(n // 2 - 1 ,     -1 ,     -1    ):
				heapify(_lowercase ,     _lowercase ,     _lowercase    )
		for i in range(n - 1 ,     0 ,     -1    ):
				__A  ,       __A							:   Dict	  =				unsorted[i], unsorted[0]
				heapify(_lowercase ,     0 ,     _lowercase    )
		return unsorted


if __name__ == "__main__":
					UpperCamelCase      			=     input('Enter numbers separated by a comma:\n').strip()
					UpperCamelCase      			=     [int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))





