"""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							(			UpperCAmelCase				):



   '''simple docstring'''



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







   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      assert Path(hparams.data_dir	).exists()
      __lowerCamelCase    		=					Path(hparams.output_dir	)
      self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)

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

      __lowerCamelCase    		=					str(_SCREAMING_SNAKE_CASE	)  # Tell lightning we are training the student
      __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher	).eval()
      use_task_specific_params(_SCREAMING_SNAKE_CASE ,				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(_SCREAMING_SNAKE_CASE ,				hparams.task	)
         __lowerCamelCase		,__lowerCamelCase    		=					None, None
      else:
         __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					create_student_by_copying_alternating_layers(
             _SCREAMING_SNAKE_CASE ,				e=hparams.student_encoder_layers ,				d=hparams.student_decoder_layers ,				save_path=_SCREAMING_SNAKE_CASE	)

      if hparams.length_penalty != -1:
         __lowerCamelCase    		=					hparams.length_penalty
      __lowerCamelCase    		=					hparams.teacher  # Use teacher's tokenizer
      super().__init__(_SCREAMING_SNAKE_CASE ,				model=_SCREAMING_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":
         __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(_SCREAMING_SNAKE_CASE	)	)
      if d_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)

      __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=_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.d_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
         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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.tokenizer.pad_token_id
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.model._shift_right(_SCREAMING_SNAKE_CASE	)
      else:
         __lowerCamelCase    		=					shift_tokens_right(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

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

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

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

      __lowerCamelCase    		=					self.teacher(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				encoder_outputs=(teacher_enc_outputs,) ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					decoder_input_ids.ne(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					self.calc_ce_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				teacher_outputs['logits']	)
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
         __lowerCamelCase    		=					self.calc_hidden_loss(
             _SCREAMING_SNAKE_CASE ,				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    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					'expected list or tuple for hidden_states, got tensor of shape: '
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states.shape}'''
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				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(_SCREAMING_SNAKE_CASE	)	)]	)
      __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(_SCREAMING_SNAKE_CASE ,				student_states.shape[1:]	)
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				teacher_states.shape[1:]	)
      __lowerCamelCase    		=					nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				reduction='none'	)
      __lowerCamelCase    		=					(mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
      return masked_mse



def       A__    (	lowercase_     :	List[Any]						)  ->							List[Any]:
   # 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=lowercase_						)
   parser.add_argument('--alpha_ce'      , default=0.8      , type=lowercase_						)
   parser.add_argument('--alpha_mlm'      , default=0.2      , type=lowercase_						)
   parser.add_argument('--alpha_hid'      , default=0.0      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student'      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_decoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_encoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--no_teacher'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--length_penalty'      , type=lowercase_      , default=-1						)
   parser.add_argument('--supervise_forward'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--normalize_hidden'      , action='store_true'      , default=lowercase_						)






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    "translation"
   __UpperCAmelCase	:							List[Any]    =    ["bleu"]
   __UpperCAmelCase	:							Dict    =    "bleu"







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


      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)
      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 ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.tokenizer.lang_code_to_id[hparams.tgt_lang]







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return calculate_bleu(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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



def       A__    (	lowercase_     :	str						)  ->							Any:
   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(lowercase_						)
   return model



def       A__    (	lowercase_     :	Dict						)  ->							List[Any]:
   Path(args.output_dir						).mkdir(exist_ok=lowercase_						)
   check_output_dir(lowercase_      , expected_items=3						)

   __lowerCamelCase    		=					create_module(lowercase_						)
   return ft_main(lowercase_      , model=lowercase_						)


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

     distill_main(args)













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





