

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							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          =['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']
			def __init__(     self							:	str , a							:	Union[str, Any]		)							->    Optional[Any]:

				"""simple docstring"""

				assert Path(hparams.data_dir		).exists()
				SCREAMING_SNAKE_CASE				: int			       =    Path(hparams.output_dir		)
				self.output_dir.mkdir(exist_ok=a		)

				SCREAMING_SNAKE_CASE				: str			       =    self.output_dir.joinpath("student"		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    str(a		)  # Tell lightning we are training the student
				SCREAMING_SNAKE_CASE				: str			       =    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:
					SCREAMING_SNAKE_CASE				: List[Any]			       =    AutoModelForSeqaSeqLM.from_pretrained(hparams.student		)
					use_task_specific_params(a , hparams.task		)
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: int			       =    None, None
				else:
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    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:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    hparams.length_penalty
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    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":
					SCREAMING_SNAKE_CASE				: Any			       =    len(student.get_encoder().block		)
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    len(student.get_decoder().block		)
					SCREAMING_SNAKE_CASE				: str			       =    len(teacher.get_encoder().block		)
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    len(teacher.get_decoder().block		)
				else:
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    student.config.encoder_layers
					SCREAMING_SNAKE_CASE				: str			       =    student.config.decoder_layers
					SCREAMING_SNAKE_CASE				: int			       =    teacher.config.encoder_layers
					SCREAMING_SNAKE_CASE				: Dict			       =    teacher.config.decoder_layers

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

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Dict			       =    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:
						SCREAMING_SNAKE_CASE				: Tuple			       =    get_layers_to_supervise(
						    n_student=len(self.e_layer_ids		) , n_teacher=a		)
						SCREAMING_SNAKE_CASE				: Dict			       =    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
						SCREAMING_SNAKE_CASE				: Any			       =    self.e_layer_ids
						SCREAMING_SNAKE_CASE				: List[Any]			       =    self.d_layer_ids
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    None
					SCREAMING_SNAKE_CASE				: Tuple			       =    None

				SCREAMING_SNAKE_CASE				: List[str]			       =    nn.KLDivLoss(reduction="batchmean"		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    2.0
				SCREAMING_SNAKE_CASE				: str			       =    hparams.alpha_mlm
				SCREAMING_SNAKE_CASE				: Any			       =    hparams.alpha_ce
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    hparams.alpha_hid
				gc.collect()
				torch.cuda.empty_cache()
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	List[Any] , a							:	str , a							:	str		)							->    List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				SummarizationModule.add_model_specific_args(a , a		)
				add_distill_args(a		)
				return parser
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	dict		)							->    tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.tokenizer.pad_token_id
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: str			       =    batch["input_ids"], batch["attention_mask"], batch["labels"]
				if isinstance(self.model , a		):
					SCREAMING_SNAKE_CASE				: Any			       =    self.model._shift_right(a		)
				else:
					SCREAMING_SNAKE_CASE				: Dict			       =    shift_tokens_right(a , a		)

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

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

				SCREAMING_SNAKE_CASE				: Tuple			       =    student_outputs[
				    "encoder_last_hidden_state"
				]  # use this unless self.different_base_models
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: str			       =    zero_tensor(), zero_tensor()
				if self.different_encoder:  # compute encoder hidden state loss
					SCREAMING_SNAKE_CASE				: List[Any]			       =    self.teacher.get_encoder()(
					    a , attention_mask=a , output_hidden_states=self.do_calc_hidden_loss , )
					if self.different_base_models:
						SCREAMING_SNAKE_CASE				: int			       =    all_teacher_encoder_outputs["last_hidden_state"]
					elif self.do_calc_hidden_loss:
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    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 , )

				SCREAMING_SNAKE_CASE				: List[str]			       =    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 , )
				SCREAMING_SNAKE_CASE				: Any			       =    decoder_input_ids.ne(a		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.calc_ce_loss(a , a , teacher_outputs["logits"]		)
				if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.calc_hidden_loss(
					    a , student_outputs["decoder_hidden_states"] , teacher_outputs["decoder_hidden_states"] , self.d_matches , normalize_hidden=self.hparams.normalize_hidden , )

				SCREAMING_SNAKE_CASE				: List[Any]			       =    (
				    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							:	Optional[int] , a							:	List[str] , a							:	Optional[int] , a							:	Any , a							:	Dict		)							->    Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    "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}"
				SCREAMING_SNAKE_CASE				: str			       =    attention_mask.to(hidden_states[0]		)
				SCREAMING_SNAKE_CASE				: Dict			       =    mask.sum() * hidden_states[0].size(-1		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.stack([hidden_states[i] for i in range(len(a		)		)]		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    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:
					SCREAMING_SNAKE_CASE				: int			       =    nn.functional.layer_norm(a , student_states.shape[1:]		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    nn.functional.layer_norm(a , teacher_states.shape[1:]		)
				SCREAMING_SNAKE_CASE				: Any			       =    nn.functional.mse_loss(a , a , reduction="none"		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (mse * mask.unsqueeze(0		).unsqueeze(-1		)).sum() / valid_count
				return masked_mse

def 	lowerCamelCase__	(   _a):
	# 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=_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=12							,     type=_a							,     required=_a)
	parser.add_argument("--student_encoder_layers"							,     default=12							,     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 _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='translation'
			lowerCamelCase__          =['bleu']
			lowerCamelCase__          ='bleu'
			def __init__(     self							:	Optional[Any] , a							:	Optional[int] , **a							:	Union[str, Any]		)							->    List[Any]:

				"""simple docstring"""

				super().__init__(a , **a		)
				assert hparams.src_lang is not None
				assert hparams.tgt_lang is not None
				SCREAMING_SNAKE_CASE				: str			       =    hparams.src_lang
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    hparams.tgt_lang
				if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , a		):
					SCREAMING_SNAKE_CASE				: str			       =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
			def 							__UpperCamelCase	(     self							:	Any , a							:	List[Any] , a							:	Tuple		)							->    dict:

				"""simple docstring"""

				return calculate_bleu(a , a		)






			@staticmethod
			def 							__UpperCamelCase	(     a							:	Any , a							:	List[Any]		)							->    int:

				"""simple docstring"""

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

def 	lowerCamelCase__	(   _a):
	if args.no_teacher:
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    TranslationModule if "translation" in args.task else SummarizationModule
	else:  # DISTILL WITH TEACHER
		SCREAMING_SNAKE_CASE				: str			       =    TranslationDistiller if "translation" in args.task else SummarizationDistiller
	SCREAMING_SNAKE_CASE				: str			       =    module_cls.__name__
	print(f"using module {args.setup_cls}")
	SCREAMING_SNAKE_CASE				: Dict			       =    module_cls(_a)
	return model

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

	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    create_module(_a)
	return ft_main(_a							,     model=_a)


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

			distill_main(args)


import math
import os

import cva
import numpy as np

a_         =      255.0

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(_a))

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.dirname(os.path.realpath(_a))
	# Loading images (original image and compressed image)
	SCREAMING_SNAKE_CASE				: Any			       =    cva.imread(os.path.join(_a							,     "image_data/original_image.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(os.path.join(_a							,     "image_data/compressed_image.png")							,     1)

	SCREAMING_SNAKE_CASE				: List[Any]			       =    cva.imread(os.path.join(_a							,     "image_data/PSNR-example-base.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(
	    os.path.join(_a							,     "image_data/PSNR-example-comp-10.jpg")							,     1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")


if __name__ == "__main__":
			main()