
"""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__      (      snake_case_       ):





			'''simple docstring'''
			lowerCamelCase						      =	['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']



			def __init__(							self  ,  __UpperCAmelCase						)				->  List[str]:
				assert Path(hparams.data_dir						).exists()
				_lowerCAmelCase			=Path(hparams.output_dir						)
				self.output_dir.mkdir(exist_ok=__UpperCAmelCase						)

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

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

				_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=__UpperCAmelCase						)
						_lowerCAmelCase			=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
						_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       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  str:
				_lowerCAmelCase			=mask[:, :, None].expand_as(__UpperCAmelCase						)
				_lowerCAmelCase			=s_logits.size(-1						)
				_lowerCAmelCase			=torch.masked_select(__UpperCAmelCase  ,  __UpperCAmelCase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
				_lowerCAmelCase			=torch.masked_select(__UpperCAmelCase  ,  __UpperCAmelCase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
				_lowerCAmelCase			=s_logits_slct.view(-1  ,  __UpperCAmelCase						)  # (bs * seq_length, voc_size) modulo the 1s in mask
				_lowerCAmelCase			=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()
				_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       _lowerCAmelCase	(							__UpperCAmelCase  ,  __UpperCAmelCase						)				->  Union[str, Any]:
				SummarizationModule.add_model_specific_args(__UpperCAmelCase  ,  __UpperCAmelCase						)
				add_distill_args(__UpperCAmelCase						)
				return parser



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  tuple:
				_lowerCAmelCase			=self.tokenizer.pad_token_id
				_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
				if isinstance(self.model  ,  __UpperCAmelCase						):
					_lowerCAmelCase			=self.model._shift_right(__UpperCAmelCase						)
				else:
					_lowerCAmelCase			=shift_tokens_right(__UpperCAmelCase  ,  __UpperCAmelCase						)

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

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

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

				_lowerCAmelCase			=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  ,  )
				_lowerCAmelCase			=decoder_input_ids.ne(__UpperCAmelCase						)
				_lowerCAmelCase			=self.calc_ce_loss(__UpperCAmelCase  ,  __UpperCAmelCase  ,  teacher_outputs["""logits"""]						)
				if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
					_lowerCAmelCase			=self.calc_hidden_loss(
					    __UpperCAmelCase  ,  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       _lowerCAmelCase	(							__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Tuple:
				_lowerCAmelCase			="""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}'''
				_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(__UpperCAmelCase						)						)]						)
				_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(__UpperCAmelCase  ,  student_states.shape[1:]						)
					_lowerCAmelCase			=nn.functional.layer_norm(__UpperCAmelCase  ,  teacher_states.shape[1:]						)
				_lowerCAmelCase			=nn.functional.mse_loss(__UpperCAmelCase  ,  __UpperCAmelCase  ,  reduction="""none"""						)
				_lowerCAmelCase			=(mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
				return masked_mse







def 					_lowerCamelCase(__UpperCamelCase )       ->				str:
	# 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 	lowerCamelCase__      (      snake_case_       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''translation'''
			lowerCamelCase						      =	['''bleu''']
			lowerCamelCase						      =	'''bleu'''



			def __init__(							self  ,  __UpperCAmelCase  ,  **__UpperCAmelCase						)				->  Any:
				super().__init__(__UpperCAmelCase  ,  **__UpperCAmelCase						)
				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  ,  __UpperCAmelCase						):
					_lowerCAmelCase			=self.tokenizer.lang_code_to_id[hparams.tgt_lang]



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  dict:
				return calculate_bleu(__UpperCAmelCase  ,  __UpperCAmelCase						)



			@staticmethod
			def       _lowerCAmelCase	(							__UpperCAmelCase  ,  __UpperCAmelCase						)				->  Any:
				TranslationModule.add_model_specific_args(__UpperCAmelCase  ,  __UpperCAmelCase						)
				add_distill_args(__UpperCAmelCase						)
				return parser







def 					_lowerCamelCase(__UpperCamelCase )       ->				Tuple:
	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(__lowerCamelCase )
	return model



def 					_lowerCamelCase(__UpperCamelCase )       ->				str:
	Path(args.output_dir ).mkdir(exist_ok=__lowerCamelCase )
	check_output_dir(__lowerCamelCase					,				expected_items=3 )

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


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"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



