





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 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']




	def __init__(	self       :    Optional[int]				,			__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




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

			__lowercase					=    self.output_dir.joinpath('student'      )

			__lowercase					=    str(__lowerCamelCase      )  # Tell lightning we are training the student
			__lowercase					=    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:
					__lowercase					=    AutoModelForSeqaSeqLM.from_pretrained(hparams.student      )
					use_task_specific_params(__lowerCamelCase				,			hparams.task      )
					__lowercase	,			__lowercase					=    None, None
			else:
					__lowercase	,			__lowercase	,			__lowercase					=    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:
					__lowercase					=    hparams.length_penalty
			__lowercase					=    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":
					__lowercase					=    len(student.get_encoder().block      )
					__lowercase					=    len(student.get_decoder().block      )
					__lowercase					=    len(teacher.get_encoder().block      )
					__lowercase					=    len(teacher.get_decoder().block      )
			else:
					__lowercase					=    student.config.encoder_layers
					__lowercase					=    student.config.decoder_layers
					__lowercase					=    teacher.config.encoder_layers
					__lowercase					=    teacher.config.decoder_layers

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

			__lowercase	,			__lowercase					=    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:
							__lowercase					=    get_layers_to_supervise(
							    n_student=len(self.e_layer_ids      )				,			n_teacher=__lowerCamelCase      )
							__lowercase					=    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
							__lowercase					=    self.e_layer_ids
							__lowercase					=    self.d_layer_ids
			else:
					__lowercase					=    None
					__lowercase					=    None

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




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    List[str]				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   List[str]:

			'''simple docstring'''




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

			'''simple docstring'''




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




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    dict      )					->   tuple:

			'''simple docstring'''




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

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

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

			__lowercase					=    student_outputs[
			    'encoder_last_hidden_state'
			]  # use this unless self.different_base_models
			__lowercase	,			__lowercase					=    zero_tensor(), zero_tensor()
			if self.different_encoder:  # compute encoder hidden state loss
					__lowercase					=    self.teacher.get_encoder()(
					    __lowerCamelCase				,			attention_mask=__lowerCamelCase				,			output_hidden_states=self.do_calc_hidden_loss				,			)
					if self.different_base_models:
							__lowercase					=    all_teacher_encoder_outputs['last_hidden_state']
					elif self.do_calc_hidden_loss:
							__lowercase					=    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				,			)

			__lowercase					=    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				,			)
			__lowercase					=    decoder_input_ids.ne(__lowerCamelCase      )
			__lowercase					=    self.calc_ce_loss(__lowerCamelCase				,			__lowerCamelCase				,			teacher_outputs['logits']      )
			if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
					__lowercase					=    self.calc_hidden_loss(
					    __lowerCamelCase				,			student_outputs['decoder_hidden_states']				,			teacher_outputs['decoder_hidden_states']				,			self.d_matches				,			normalize_hidden=self.hparams.normalize_hidden				,			)

			__lowercase					=    (
			    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 (	__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    List[Any]				,			__lowerCamelCase       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




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





def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Dict:
		# 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=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 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''translation'''
	__UpperCamelCase        =		['''bleu''']
	__UpperCamelCase        =		'''bleu'''




	def __init__(	self       :    Tuple				,			__lowerCamelCase       :    Any				,			**__lowerCamelCase       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




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




	def       UpperCAmelCase (	self       :    int				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    Dict      )					->   dict:

			'''simple docstring'''




			return calculate_bleu(__lowerCamelCase				,			__lowerCamelCase      )






	@staticmethod
	def       UpperCAmelCase (	__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




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





def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						List[Any]:
		if args.no_teacher:
				__lowercase					=    TranslationModule if 'translation' in args.task else SummarizationModule
		else:  # DISTILL WITH TEACHER
				__lowercase					=    TranslationDistiller if 'translation' in args.task else SummarizationDistiller
		__lowercase					=    module_cls.__name__
		print(F"using module {args.setup_cls}"       )
		__lowercase					=    module_cls(snake_case       )
		return model



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		Path(args.output_dir       ).mkdir(exist_ok=snake_case       )
		check_output_dir(snake_case	,      expected_items=3       )

		__lowercase					=    create_module(snake_case       )
		return ft_main(snake_case	,      model=snake_case       )


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

		distill_main(args)











import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(VQDiffusionScheduler,)




	def       UpperCAmelCase (	self       :    int				,			**__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    {
			    'num_vec_classes': 4_097,
			    'num_train_timesteps': 100,
			}

			config.update(**__lowerCamelCase      )
			return config




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    8
			__lowercase					=    8

			__lowercase					=    torch.randint(0				,			__lowerCamelCase				,			(batch_size, height * width)      )

			return sample




	@property
	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			assert False




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   Dict:

			'''simple docstring'''




			def model(__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[Any]				,			*__lowerCamelCase       :    List[Any]      ):
					__lowercase	,			__lowercase					=    sample.shape
					__lowercase					=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
					__lowercase					=    F.log_softmax(logits.double()				,			dim=1      ).float()
					return return_value

			return model




	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			for timesteps in [2, 5, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			for num_vec_classes in [5, 100, 1_000, 4_000]:
					self.check_over_configs(num_vec_classes=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			for t in [0, 50, 99]:
					self.check_over_forward(time_step=__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			pass




