
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 							A__		(     __A):
      _UpperCAmelCase    :   Optional[int]   	=      ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]
      def __init__(				self   ,  __magic_name__				):
       assert Path(hparams.data_dir				).exists()
       lowerCamelCase		:     int          =				Path(hparams.output_dir				)
       self.output_dir.mkdir(exist_ok=__magic_name__				)

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

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

       if hparams.length_penalty != -1:
        lowerCamelCase		:     Optional[int]          =				hparams.length_penalty
       lowerCamelCase		:     Optional[int]          =				hparams.teacher  # Use teacher's tokenizer
       super().__init__(__magic_name__   ,  model=__magic_name__   ,  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		:     Any          =				len(student.get_encoder().block				)
        lowerCamelCase		:     Optional[int]          =				len(student.get_decoder().block				)
        lowerCamelCase		:     str          =				len(teacher.get_encoder().block				)
        lowerCamelCase		:     Optional[int]          =				len(teacher.get_decoder().block				)
       else:
        lowerCamelCase		:     Optional[Any]          =				student.config.encoder_layers
        lowerCamelCase		:     str          =				student.config.decoder_layers
        lowerCamelCase		:     int          =				teacher.config.encoder_layers
        lowerCamelCase		:     Dict          =				teacher.config.decoder_layers

       lowerCamelCase		:     List[str]          =				not (hparams.student is None or hparams.teacher == hparams.student)
       lowerCamelCase		:     Optional[Any]          =				(not self.different_base_models) and hparams.alpha_hid > 0
       lowerCamelCase		:     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
       lowerCamelCase		:     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:
        lowerCamelCase		:     List[Any]          =				list(range(__magic_name__				)				)
       if d_layer_ids is None:
        lowerCamelCase		:     Dict          =				list(range(__magic_name__				)				)

       lowerCamelCase		:     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:
         lowerCamelCase		:     Tuple          =				get_layers_to_supervise(
             n_student=len(self.e_layer_ids				)   ,  n_teacher=__magic_name__				)
         lowerCamelCase		:     Dict          =				get_layers_to_supervise(
             n_student=len(self.d_layer_ids				)   ,  n_teacher=__magic_name__				)
        else:  # student layer should emulate hidden states of the teacher layer it was copied from
         lowerCamelCase		:     Any          =				self.e_layer_ids
         lowerCamelCase		:     List[Any]          =				self.d_layer_ids
       else:
        lowerCamelCase		:     Union[str, Any]          =				None
        lowerCamelCase		:     Tuple          =				None

       lowerCamelCase		:     List[str]          =				nn.KLDivLoss(reduction="""batchmean"""				)
       lowerCamelCase		:     Union[str, Any]          =				2.0
       lowerCamelCase		:     str          =				hparams.alpha_mlm
       lowerCamelCase		:     Any          =				hparams.alpha_ce
       lowerCamelCase		:     Union[str, Any]          =				hparams.alpha_hid
       gc.collect()
       torch.cuda.empty_cache()
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     str          =				mask[:, :, None].expand_as(__magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				s_logits.size(-1				)
       lowerCamelCase		:     Optional[Any]          =				torch.masked_select(__magic_name__   ,  __magic_name__				)  # (bs * seq_length * voc_size) modulo the 1s in mask
       lowerCamelCase		:     str          =				torch.masked_select(__magic_name__   ,  __magic_name__				)  # (bs * seq_length * voc_size) modulo the 1s in mask
       lowerCamelCase		:     Optional[int]          =				s_logits_slct.view(-1   ,  __magic_name__				)  # (bs * seq_length, voc_size) modulo the 1s in mask
       lowerCamelCase		:     Tuple          =				t_logits_slct.view(-1   ,  __magic_name__				)  # (bs * seq_length, voc_size) modulo the 1s in mask
       assert t_logits_slct.size() == s_logits_slct.size()
       lowerCamelCase		:     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__							(				__magic_name__   ,  __magic_name__				):
       SummarizationModule.add_model_specific_args(__magic_name__   ,  __magic_name__				)
       add_distill_args(__magic_name__				)
       return parser
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     int          =				self.tokenizer.pad_token_id
       lowerCamelCase		:     str          =				batch["input_ids"], batch["attention_mask"], batch["labels"]
       if isinstance(self.model   ,  __magic_name__				):
        lowerCamelCase		:     Any          =				self.model._shift_right(__magic_name__				)
       else:
        lowerCamelCase		:     Dict          =				shift_tokens_right(__magic_name__   ,  __magic_name__				)

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

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

       lowerCamelCase		:     Tuple          =				student_outputs[
           "encoder_last_hidden_state"
       ]  # use this unless self.different_base_models
       lowerCamelCase		:     str          =				zero_tensor(), zero_tensor()
       if self.different_encoder:  # compute encoder hidden state loss
        lowerCamelCase		:     List[Any]          =				self.teacher.get_encoder()(
            __magic_name__   ,  attention_mask=__magic_name__   ,  output_hidden_states=self.do_calc_hidden_loss   ,  )
        if self.different_base_models:
         lowerCamelCase		:     int          =				all_teacher_encoder_outputs["last_hidden_state"]
        elif self.do_calc_hidden_loss:
         lowerCamelCase		:     Optional[int]          =				self.calc_hidden_loss(
             __magic_name__   ,  student_outputs["""encoder_hidden_states"""]   ,  all_teacher_encoder_outputs["""hidden_states"""]   ,  self.e_matches   ,  normalize_hidden=self.hparams.normalize_hidden   ,  )

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

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





def    _a (						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=_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 							A__		(     __A):
      _UpperCAmelCase    :   Union[str, Any]   	=      """translation"""
      _UpperCAmelCase    :   Dict   	=      ["""bleu"""]
      _UpperCAmelCase    :   List[str]   	=      """bleu"""
      def __init__(				self   ,  __magic_name__   ,  **__magic_name__				):
       super().__init__(__magic_name__   ,  **__magic_name__				)
       assert hparams.src_lang is not None
       assert hparams.tgt_lang is not None
       lowerCamelCase		:     str          =				hparams.src_lang
       lowerCamelCase		:     Union[str, Any]          =				hparams.tgt_lang
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,  __magic_name__				):
        lowerCamelCase		:     str          =				self.tokenizer.lang_code_to_id[hparams.tgt_lang]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       return calculate_bleu(__magic_name__   ,  __magic_name__				)






      @staticmethod
      def 						UpperCamelCase__							(				__magic_name__   ,  __magic_name__				):
       TranslationModule.add_model_specific_args(__magic_name__   ,  __magic_name__				)
       add_distill_args(__magic_name__				)
       return parser





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





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

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


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

      distill_main(args)






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
              """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
              """Not needed for `l0`"""
          ),
      )
      parser.add_argument(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




