

"""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    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[Any]  						=      ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]


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

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

    __lowercase								=		str(UpperCAmelCase__      )  # Tell lightning we are training the student
    __lowercase								=		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:
       __lowercase								=		AutoModelForSeqaSeqLM.from_pretrained(hparams.student      )
       use_task_specific_params(UpperCAmelCase__,   hparams.task      )
       __lowercase						,__lowercase								=		None, None
    else:
       __lowercase						,__lowercase						,__lowercase								=		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:
       __lowercase								=		hparams.length_penalty
    __lowercase								=		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":
       __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(UpperCAmelCase__      )      )
    if d_layer_ids is None:
       __lowercase								=		list(range(UpperCAmelCase__      )      )

    __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=UpperCAmelCase__      )
          __lowercase								=		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
          __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    _lowercase					(     self				:     Dict,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Any      ):
    __lowercase								=		mask[:, :, None].expand_as(UpperCAmelCase__      )
    __lowercase								=		s_logits.size(-1      )
    __lowercase								=		torch.masked_select(UpperCAmelCase__,   UpperCAmelCase__      )  # (bs * seq_length * voc_size) modulo the 1s in mask
    __lowercase								=		torch.masked_select(UpperCAmelCase__,   UpperCAmelCase__      )  # (bs * seq_length * voc_size) modulo the 1s in mask
    __lowercase								=		s_logits_slct.view(-1,   UpperCAmelCase__      )  # (bs * seq_length, voc_size) modulo the 1s in mask
    __lowercase								=		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()
    __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    _lowercase					(     UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     Optional[Any]      ):
    SummarizationModule.add_model_specific_args(UpperCAmelCase__,   UpperCAmelCase__      )
    add_distill_args(UpperCAmelCase__      )
    return parser


 def    _lowercase					(     self				:     Tuple,   UpperCAmelCase__				:     dict      ):
    __lowercase								=		self.tokenizer.pad_token_id
    __lowercase						,__lowercase						,__lowercase								=		batch["input_ids"], batch["attention_mask"], batch["labels"]
    if isinstance(self.model,   UpperCAmelCase__      ):
       __lowercase								=		self.model._shift_right(UpperCAmelCase__      )
    else:
       __lowercase								=		shift_tokens_right(UpperCAmelCase__,   UpperCAmelCase__      )

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

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

    __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()(
           UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   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(
              UpperCAmelCase__,   student_outputs["encoder_hidden_states"],   all_teacher_encoder_outputs["hidden_states"],   self.e_matches,   normalize_hidden=self.hparams.normalize_hidden,   )

    __lowercase								=		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__,   )
    __lowercase								=		decoder_input_ids.ne(UpperCAmelCase__      )
    __lowercase								=		self.calc_ce_loss(UpperCAmelCase__,   UpperCAmelCase__,   teacher_outputs["logits"]      )
    if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       __lowercase								=		self.calc_hidden_loss(
           UpperCAmelCase__,   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    _lowercase					(     UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Tuple      ):
    __lowercase								=		"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}"""
    __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(UpperCAmelCase__      )      )]      )
    __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(UpperCAmelCase__,   student_states.shape[1:]      )
       __lowercase								=		nn.functional.layer_norm(UpperCAmelCase__,   teacher_states.shape[1:]      )
    __lowercase								=		nn.functional.mse_loss(UpperCAmelCase__,   UpperCAmelCase__,   reduction="none"      )
    __lowercase								=		(mse * mask.unsqueeze(0      ).unsqueeze(-1      )).sum() / valid_count
    return masked_mse







def     _A    (    UpperCamelCase_	:			str)      ->		str:
   '''simple docstring'''


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




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[Any]  						=      "translation"
 __UpperCAmelCase      :   Optional[int]  						=      ["bleu"]
 __UpperCAmelCase      :   Any  						=      "bleu"


 def __init__(     self				:     List[Any],   UpperCAmelCase__				:     Dict,   **UpperCAmelCase__				:     Optional[int]      ):
    super().__init__(UpperCAmelCase__,   **UpperCAmelCase__      )
    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,   UpperCAmelCase__      ):
       __lowercase								=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]


 def    _lowercase					(     self				:     Optional[int],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     str      ):
    return calculate_bleu(UpperCAmelCase__,   UpperCAmelCase__      )



 @staticmethod
 def    _lowercase					(     UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     int      ):
    TranslationModule.add_model_specific_args(UpperCAmelCase__,   UpperCAmelCase__      )
    add_distill_args(UpperCAmelCase__      )
    return parser







def     _A    (    UpperCamelCase_	:			Dict)      ->		Union[str, Any]:
   '''simple docstring'''


   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(UpperCamelCase_)
   return model




def     _A    (    UpperCamelCase_	:			int)      ->		int:
   '''simple docstring'''


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

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


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)







"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def     _A    (    UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
   __lowercase								=		np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   # modify the pix within [0, 255]
   __lowercase								=		dst_x * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_y * 255 / np.max(UpperCamelCase_)

   __lowercase								=		np.sqrt((np.square(UpperCamelCase_)) + (np.square(UpperCamelCase_)))
   __lowercase								=		dst_xy * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_xy.astype(np.uinta)

   __lowercase								=		np.arctana(UpperCamelCase_,     UpperCamelCase_)
   return dst, theta


if __name__ == "__main__":
   # read original image
   _a					 =				imread('../image_data/lena.jpg')
   # turn image in gray scale value
   _a					 =				cvtColor(img, COLOR_BGR2GRAY)

   _a ,		_a					 =				sobel_filter(gray)

   # show result images
   imshow('sobel filter', sobel_grad)
   imshow('sobel theta', sobel_theta)
   waitKey(0)




