





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(    __UpperCAmelCase			):
     __A							=							["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]

     def __init__(   self,							A   ):





            """simple docstring"""

            assert Path(hparams.data_dir   ).exists()
            _UpperCamelCase														=    Path(hparams.output_dir   )
            self.output_dir.mkdir(exist_ok=A   )

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

            _UpperCamelCase														=    str(A   )  # Tell lightning we are training the student
            _UpperCamelCase														=    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:
                   _UpperCamelCase														=    AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
                   use_task_specific_params(A,							hparams.task   )
                   _UpperCamelCase							,		_UpperCamelCase														=    None, None
            else:
                   _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    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:
                   _UpperCamelCase														=    hparams.length_penalty
            _UpperCamelCase														=    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":
                   _UpperCamelCase														=    len(student.get_encoder().block   )
                   _UpperCamelCase														=    len(student.get_decoder().block   )
                   _UpperCamelCase														=    len(teacher.get_encoder().block   )
                   _UpperCamelCase														=    len(teacher.get_decoder().block   )
            else:
                   _UpperCamelCase														=    student.config.encoder_layers
                   _UpperCamelCase														=    student.config.decoder_layers
                   _UpperCamelCase														=    teacher.config.encoder_layers
                   _UpperCamelCase														=    teacher.config.decoder_layers

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

            _UpperCamelCase							,		_UpperCamelCase														=    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:
                          _UpperCamelCase														=    get_layers_to_supervise(
                              n_student=len(self.e_layer_ids   ),							n_teacher=A   )
                          _UpperCamelCase														=    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
                          _UpperCamelCase														=    self.e_layer_ids
                          _UpperCamelCase														=    self.d_layer_ids
            else:
                   _UpperCamelCase														=    None
                   _UpperCamelCase														=    None

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

     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """simple docstring"""

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





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

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

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

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

            _UpperCamelCase														=    student_outputs[
                '''encoder_last_hidden_state'''
            ]  # use this unless self.different_base_models
            _UpperCamelCase							,		_UpperCamelCase														=    zero_tensor(), zero_tensor()
            if self.different_encoder:  # compute encoder hidden state loss
                   _UpperCamelCase														=    self.teacher.get_encoder()(
                       A,							attention_mask=A,							output_hidden_states=self.do_calc_hidden_loss,							)
                   if self.different_base_models:
                          _UpperCamelCase														=    all_teacher_encoder_outputs['''last_hidden_state''']
                   elif self.do_calc_hidden_loss:
                          _UpperCamelCase														=    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,							)

            _UpperCamelCase														=    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,							)
            _UpperCamelCase														=    decoder_input_ids.ne(A   )
            _UpperCamelCase														=    self.calc_ce_loss(A,							A,							teacher_outputs['''logits''']   )
            if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
                   _UpperCamelCase														=    self.calc_hidden_loss(
                       A,							student_outputs['''decoder_hidden_states'''],							teacher_outputs['''decoder_hidden_states'''],							self.d_matches,							normalize_hidden=self.hparams.normalize_hidden,							)

            _UpperCamelCase														=    (
                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   ):





            """simple docstring"""

            _UpperCamelCase														=    '''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}'''
            _UpperCamelCase														=    attention_mask.to(hidden_states[0]   )
            _UpperCamelCase														=    mask.sum() * hidden_states[0].size(-1   )
            _UpperCamelCase														=    torch.stack([hidden_states[i] for i in range(len(A   )   )]   )
            _UpperCamelCase														=    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:
                   _UpperCamelCase														=    nn.functional.layer_norm(A,							student_states.shape[1:]   )
                   _UpperCamelCase														=    nn.functional.layer_norm(A,							teacher_states.shape[1:]   )
            _UpperCamelCase														=    nn.functional.mse_loss(A,							A,							reduction='''none'''   )
            _UpperCamelCase														=    (mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
            return masked_mse






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

class 			__A(    __UpperCAmelCase			):
     __A							=							"translation"
     __A							=							["bleu"]
     __A							=							"bleu"

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





            """simple docstring"""

            super().__init__(A,							**A   )
            assert hparams.src_lang is not None
            assert hparams.tgt_lang is not None
            _UpperCamelCase														=    hparams.src_lang
            _UpperCamelCase														=    hparams.tgt_lang
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer,							A   ):
                   _UpperCamelCase														=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            return calculate_bleu(A,							A   )



     @staticmethod
     def 						_UpperCamelCase  (   A,							A   ):





            """simple docstring"""

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






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       if args.no_teacher:
              _UpperCamelCase														=    TranslationModule if '''translation''' in args.task else SummarizationModule
       else:  # DISTILL WITH TEACHER
              _UpperCamelCase														=    TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
       _UpperCamelCase														=    module_cls.__name__
       print(f'''using module {args.setup_cls}'''       )
       _UpperCamelCase														=    module_cls(lowerCAmelCase       )
       return model


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       Path(args.output_dir       ).mkdir(exist_ok=lowerCAmelCase       )
       check_output_dir(lowerCAmelCase     ,							expected_items=3       )

       _UpperCamelCase														=    create_module(lowerCAmelCase       )
       return ft_main(lowerCAmelCase     ,							model=lowerCAmelCase       )


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

       distill_main(args)













import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 			__A(    unittest.TestCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    tempfile.mkdtemp()

            _UpperCamelCase														=    CLIPImageProcessor()
            _UpperCamelCase														=    BertTokenizer.from_pretrained(
                '''hf-internal-testing/tiny-random-BertModel''',							model_input_names=['''input_ids''', '''attention_mask''']   )

            _UpperCamelCase														=    GitProcessor(A,							A   )

            processor.save_pretrained(self.tmpdirname   )

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,							**A   ).tokenizer

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,							**A   ).image_processor

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    [np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta   )]

            _UpperCamelCase														=    [Image.fromarray(np.moveaxis(A,							0,							-1   )   ) for x in image_inputs]

            return image_inputs

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GitProcessor(tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor()   )
            processor.save_pretrained(self.tmpdirname   )

            _UpperCamelCase														=    self.get_tokenizer(bos_token='''(BOS)''',							eos_token='''(EOS)'''   )
            _UpperCamelCase														=    self.get_image_processor(do_normalize=A,							padding_value=1.0   )

            _UpperCamelCase														=    GitProcessor.from_pretrained(
                self.tmpdirname,							bos_token='''(BOS)''',							eos_token='''(EOS)''',							do_normalize=A,							padding_value=1.0   )

            self.assertEqual(processor.tokenizer.get_vocab(),							tokenizer_add_kwargs.get_vocab()   )
            self.assertIsInstance(processor.tokenizer,							A   )

            self.assertEqual(processor.image_processor.to_json_string(),							image_processor_add_kwargs.to_json_string()   )
            self.assertIsInstance(processor.image_processor,							A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    image_processor(A,							return_tensors='''np'''   )
            _UpperCamelCase														=    processor(images=A,							return_tensors='''np'''   )

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum(),							input_processor[key].sum(),							delta=1E-2   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''

            _UpperCamelCase														=    processor(text=A   )

            _UpperCamelCase														=    tokenizer(A,							return_token_type_ids=A   )

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key],							encoded_processor[key]   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''
            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    processor(text=A,							images=A   )

            self.assertListEqual(list(inputs.keys()   ),							['''input_ids''', '''attention_mask''', '''pixel_values''']   )

            # test if it raises when no input is passed
            with pytest.raises(A   ):
                   processor()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _UpperCamelCase														=    processor.batch_decode(A   )
            _UpperCamelCase														=    tokenizer.batch_decode(A   )

            self.assertListEqual(A,							A   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''
            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    processor(text=A,							images=A   )

            # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
            self.assertListEqual(list(inputs.keys()   ),							['''input_ids''', '''attention_mask''', '''pixel_values''']   )






