'''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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   str							=				['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']






		def __init__( self		,    __A					)			->  Dict:
								assert Path(hparams.data_dir					).exists()
								_lowerCAmelCase					    =Path(hparams.output_dir					)
								self.output_dir.mkdir(exist_ok=__A					)

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

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

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

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

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

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






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






		def 		UpperCamelCase__							( self		,    __A					)			->  tuple:
								_lowerCAmelCase					    =self.tokenizer.pad_token_id
								_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =batch['input_ids'], batch['attention_mask'], batch['labels']
								if isinstance(self.model		,    __A					):
														_lowerCAmelCase					    =self.model._shift_right(__A					)
								else:
														_lowerCAmelCase					    =shift_tokens_right(__A		,    __A					)

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

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

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

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

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



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						parser.add_argument('--teacher'      ,   type=a__   )
						parser.add_argument('--alpha_ce'      ,   default=0.8      ,   type=a__   )
						parser.add_argument('--alpha_mlm'      ,   default=0.2      ,   type=a__   )
						parser.add_argument('--alpha_hid'      ,   default=0.0      ,   type=a__      ,   required=a__   )
						parser.add_argument('--student'      ,   type=a__      ,   required=a__   )
						parser.add_argument('--student_decoder_layers'      ,   default=1_2      ,   type=a__      ,   required=a__   )
						parser.add_argument('--student_encoder_layers'      ,   default=1_2      ,   type=a__      ,   required=a__   )
						parser.add_argument('--no_teacher'      ,   action='store_true'      ,   default=a__   )
						parser.add_argument('--length_penalty'      ,   type=a__      ,   default=-1   )
						parser.add_argument('--supervise_forward'      ,   action='store_true'      ,   default=a__   )
						parser.add_argument('--normalize_hidden'      ,   action='store_true'      ,   default=a__   )
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Dict							=				'translation'
		lowercase :   Tuple							=				['bleu']
		lowercase :   Optional[int]							=				'bleu'






		def __init__( self		,    __A		,    **__A					)			->  int:
								super().__init__(__A		,    **__A					)
								assert hparams.src_lang is not None
								assert hparams.tgt_lang is not None
								_lowerCAmelCase					    =hparams.src_lang
								_lowerCAmelCase					    =hparams.tgt_lang
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,    __A					):
														_lowerCAmelCase					    =self.tokenizer.lang_code_to_id[hparams.tgt_lang]






		def 		UpperCamelCase__							( self		,    __A		,    __A					)			->  dict:
								return calculate_bleu(__A		,    __A					)






		@staticmethod
		def 		UpperCamelCase__							( __A		,    __A					)			->  Optional[int]:
								TranslationModule.add_model_specific_args(__A		,    __A					)
								add_distill_args(__A					)
								return parser



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






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



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






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

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


if __name__ == "__main__":
							lowercase_  		=			argparse.ArgumentParser()
							lowercase_  		=			pl.Trainer.add_argparse_args(parser)
							lowercase_  		=			SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
							lowercase_  		=			parser.parse_args()

							distill_main(args)






'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

								self.register_modules(vae=__A		,    text_encoder=__A		,    tokenizer=__A		,    unet=__A		,    scheduler=__A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






		def 		UpperCamelCase__							( self		,    __A					)			->  Dict:
								_lowerCAmelCase					    =(1 / 0.18_215) * latent
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.decode(__A					).sample
								_lowerCAmelCase					    =(img / 2 + 0.5).clamp(0		,    1					)
								_lowerCAmelCase					    =img.detach().cpu().permute(0		,    2		,    3		,    1					).numpy()
								_lowerCAmelCase					    =(img * 255).round().astype('uint8'					)
								return Image.fromarray(img[0]					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

								_lowerCAmelCase					    =self.tokenizer(
								    ''		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

								return torch.cat([uncond_embedding, text_embedding]					)






		def __call__( self		,    __A		,    __A		,    __A = 0.3		,    __A = 0.6		,    __A = 0.5		,    __A = 42		,    __A = 50		,    __A = 7.5		,    )			->  Image.Image:
								_lowerCAmelCase					    =steps - int(kmin * steps					)
								_lowerCAmelCase					    =steps - int(kmax * steps					)

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

								torch.manual_seed(__A					)
								_lowerCAmelCase					    =torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8)		,    ).to(self.device					)

								_lowerCAmelCase					    =self.scheduler.add_noise(
								    __A		,    __A		,    timesteps=self.scheduler.timesteps[tmax]		,    )

								_lowerCAmelCase					    =torch.cat([latents] * 2					)

								_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    self.scheduler.timesteps[tmax]					)

								with torch.no_grad():
														_lowerCAmelCase					    =self.unet(
														    __A		,    self.scheduler.timesteps[tmax]		,    encoder_hidden_states=__A		,    ).sample

								_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
								_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

								_lowerCAmelCase					    =self.scheduler.step(__A		,    self.scheduler.timesteps[tmax]		,    __A					).prev_sample

								for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
														if i > tmax:
																				if i < tmin:  # layout generation phase
																										_lowerCAmelCase					    =self.scheduler.add_noise(
																										    __A		,    __A		,    timesteps=__A		,    )

																										_lowerCAmelCase					    =(mix_factor * latents) + (
																										    1 - mix_factor
																										) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																										_lowerCAmelCase					    =torch.cat([input] * 2					)

																				else:  # content generation phase
																										_lowerCAmelCase					    =torch.cat([latents] * 2					)

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

																				with torch.no_grad():
																										_lowerCAmelCase					    =self.unet(
																										    __A		,    __A		,    encoder_hidden_states=__A		,    ).sample

																				_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
																				_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)





