



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  _lowercase				(a_ ):
				'''simple docstring'''







				lowercase__				  =  ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]

				def __init__(	self       ,	snake_case__      ):

									'''simple docstring'''


									assert Path(hparams.data_dir      ).exists()
									UpperCamelCase_											=				Path(hparams.output_dir      )
									self.output_dir.mkdir(exist_ok=snake_case__      )

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

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

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

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

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

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

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


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

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

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

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

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

									UpperCamelCase_											=				(
									    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  _lowerCamelCase   (	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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




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


class  _lowercase				(a_ ):
				'''simple docstring'''







				lowercase__				  =  """translation"""
				lowercase__				  =  ["""bleu"""]
				lowercase__				  =  """bleu"""

				def __init__(	self       ,	snake_case__       ,	**snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									return calculate_bleu(snake_case__       ,	snake_case__      )

				@staticmethod
				def  _lowerCamelCase   (	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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




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



def 				_lowerCAmelCase      (_lowerCAmelCase):
					Path(args.output_dir).mkdir(exist_ok=_lowerCAmelCase)
					check_output_dir(_lowerCAmelCase				,		expected_items=3)

					UpperCamelCase_											=				create_module(_lowerCAmelCase)
					return ft_main(_lowerCAmelCase				,		model=_lowerCAmelCase)


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

	distill_main(args)





from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
	from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
