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    ):
       A							:	Tuple          =	["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]

       def __init__(							self					:     Optional[Any]			,			_lowerCAmelCase					:     Any			):
           assert Path(hparams.data_dir			).exists()
           __snake_case       :				Union[str, Any] 			=    Path(hparams.output_dir			)
           self.output_dir.mkdir(exist_ok=_lowerCAmelCase			)

           __snake_case       :				Tuple 			=    self.output_dir.joinpath("""student"""			)

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

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

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

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

           __snake_case       :				List[Any] 			=    nn.KLDivLoss(reduction="""batchmean"""			)
           __snake_case       :				Union[str, Any] 			=    2.0
           __snake_case       :				Optional[int] 			=    hparams.alpha_mlm
           __snake_case       :				Any 			=    hparams.alpha_ce
           __snake_case       :				Dict 			=    hparams.alpha_hid
           gc.collect()
           torch.cuda.empty_cache()

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Union[str, Any]			,			_lowerCAmelCase					:     List[str]			,			_lowerCAmelCase					:     int			):
           __snake_case       :				List[str] 			=    mask[:, :, None].expand_as(_lowerCAmelCase			)
           __snake_case       :				Union[str, Any] 			=    s_logits.size(-1			)
           __snake_case       :				Tuple 			=    torch.masked_select(_lowerCAmelCase			,			_lowerCAmelCase			)  # (bs * seq_length * voc_size) modulo the 1s in mask
           __snake_case       :				Dict 			=    torch.masked_select(_lowerCAmelCase			,			_lowerCAmelCase			)  # (bs * seq_length * voc_size) modulo the 1s in mask
           __snake_case       :				Optional[int] 			=    s_logits_slct.view(-1			,			_lowerCAmelCase			)  # (bs * seq_length, voc_size) modulo the 1s in mask
           __snake_case       :				Optional[int] 			=    t_logits_slct.view(-1			,			_lowerCAmelCase			)  # (bs * seq_length, voc_size) modulo the 1s in mask
           assert t_logits_slct.size() == s_logits_slct.size()
           __snake_case       :				int 			=    (
               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 			snake_case__    (							_lowerCAmelCase					:     Tuple			,			_lowerCAmelCase					:     Union[str, Any]			):
           SummarizationModule.add_model_specific_args(_lowerCAmelCase			,			_lowerCAmelCase			)
           add_distill_args(_lowerCAmelCase			)
           return parser

       def 			snake_case__    (							self					:     Tuple			,			_lowerCAmelCase					:     dict			):
           __snake_case       :				List[str] 			=    self.tokenizer.pad_token_id
           __snake_case			, __snake_case			, __snake_case       :				Optional[int] 			=    batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
           if isinstance(self.model			,			_lowerCAmelCase			):
               __snake_case       :				Dict 			=    self.model._shift_right(_lowerCAmelCase			)
           else:
               __snake_case       :				Union[str, Any] 			=    shift_tokens_right(_lowerCAmelCase			,			_lowerCAmelCase			)

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

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

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

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

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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple   ):







    '''simple docstring'''
    # 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=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--alpha_ce"""		,							default=0.8		,							type=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--alpha_mlm"""		,							default=0.2		,							type=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--alpha_hid"""		,							default=0.0		,							type=__SCREAMING_SNAKE_CASE		,							required=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--student"""		,							type=__SCREAMING_SNAKE_CASE		,							required=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--student_decoder_layers"""		,							default=1_2		,							type=__SCREAMING_SNAKE_CASE		,							required=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--student_encoder_layers"""		,							default=1_2		,							type=__SCREAMING_SNAKE_CASE		,							required=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--no_teacher"""		,							action="""store_true"""		,							default=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--length_penalty"""		,							type=__SCREAMING_SNAKE_CASE		,							default=-1   )
    parser.add_argument("""--supervise_forward"""		,							action="""store_true"""		,							default=__SCREAMING_SNAKE_CASE   )
    parser.add_argument("""--normalize_hidden"""		,							action="""store_true"""		,							default=__SCREAMING_SNAKE_CASE   )







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	int          =	"translation"
       A							:	Tuple          =	["bleu"]
       A							:	Dict          =	"bleu"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]			,			**_lowerCAmelCase					:     Optional[int]			):
           super().__init__(_lowerCAmelCase			,			**_lowerCAmelCase			)
           assert hparams.src_lang is not None
           assert hparams.tgt_lang is not None
           __snake_case       :				Any 			=    hparams.src_lang
           __snake_case       :				Dict 			=    hparams.tgt_lang
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,			_lowerCAmelCase			):
               __snake_case       :				Optional[Any] 			=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]

       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     Union[str, Any]			,			_lowerCAmelCase					:     Optional[Any]			):
           return calculate_bleu(_lowerCAmelCase			,			_lowerCAmelCase			)





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Dict			,			_lowerCAmelCase					:     Union[str, Any]			):
           TranslationModule.add_model_specific_args(_lowerCAmelCase			,			_lowerCAmelCase			)
           add_distill_args(_lowerCAmelCase			)
           return parser







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]   ):







    '''simple docstring'''
    if args.no_teacher:
        __snake_case       :				Any 			=    TranslationModule if """translation""" in args.task else SummarizationModule
    else:  # DISTILL WITH TEACHER
        __snake_case       :				Tuple 			=    TranslationDistiller if """translation""" in args.task else SummarizationDistiller
    __snake_case       :				str 			=    module_cls.__name__
    print(F'''using module {args.setup_cls}'''   )
    __snake_case       :				int 			=    module_cls(__SCREAMING_SNAKE_CASE   )
    return model







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    Path(args.output_dir   ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE   )
    check_output_dir(__SCREAMING_SNAKE_CASE		,							expected_items=3   )

    __snake_case       :				str 			=    create_module(__SCREAMING_SNAKE_CASE   )
    return ft_main(__SCREAMING_SNAKE_CASE		,							model=__SCREAMING_SNAKE_CASE   )


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

     distill_main(args)




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



