"""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        __UpperCAmelCase						(     _a     ):
				A__				:       List[Any]  							=	['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']

				def __init__(				self     ,			_lowerCamelCase				):
										assert Path(hparams.data_dir				).exists()
										lowerCamelCase__ =Path(hparams.output_dir				)
										self.output_dir.mkdir(exist_ok=_lowerCamelCase				)

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

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

										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=_lowerCamelCase				)
																						lowerCamelCase__ =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
																						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     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =mask[:, :, None].expand_as(_lowerCamelCase				)
										lowerCamelCase__ =s_logits.size(-1				)
										lowerCamelCase__ =torch.masked_select(_lowerCamelCase     ,			_lowerCamelCase				)  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCamelCase__ =torch.masked_select(_lowerCamelCase     ,			_lowerCamelCase				)  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCamelCase__ =s_logits_slct.view(-1     ,			_lowerCamelCase				)  # (bs * seq_length, voc_size) modulo the 1s in mask
										lowerCamelCase__ =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()
										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     (				_lowerCamelCase     ,			_lowerCamelCase				):
										SummarizationModule.add_model_specific_args(_lowerCamelCase     ,			_lowerCamelCase				)
										add_distill_args(_lowerCamelCase				)
										return parser

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =self.tokenizer.pad_token_id
										lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ =batch["input_ids"], batch["attention_mask"], batch["labels"]
										if isinstance(self.model     ,			_lowerCamelCase				):
																lowerCamelCase__ =self.model._shift_right(_lowerCamelCase				)
										else:
																lowerCamelCase__ =shift_tokens_right(_lowerCamelCase     ,			_lowerCamelCase				)

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

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

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

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





def       lowerCamelCase_			( __lowerCAmelCase    )							-> str:
						parser.add_argument("--teacher"       ,   type=snake_case__    )
						parser.add_argument("--alpha_ce"       ,   default=0.8       ,   type=snake_case__    )
						parser.add_argument("--alpha_mlm"       ,   default=0.2       ,   type=snake_case__    )
						parser.add_argument("--alpha_hid"       ,   default=0.0       ,   type=snake_case__       ,   required=snake_case__    )
						parser.add_argument("--student"       ,   type=snake_case__       ,   required=snake_case__    )
						parser.add_argument("--student_decoder_layers"       ,   default=12       ,   type=snake_case__       ,   required=snake_case__    )
						parser.add_argument("--student_encoder_layers"       ,   default=12       ,   type=snake_case__       ,   required=snake_case__    )
						parser.add_argument("--no_teacher"       ,   action="store_true"       ,   default=snake_case__    )
						parser.add_argument("--length_penalty"       ,   type=snake_case__       ,   default=-1    )
						parser.add_argument("--supervise_forward"       ,   action="store_true"       ,   default=snake_case__    )
						parser.add_argument("--normalize_hidden"       ,   action="store_true"       ,   default=snake_case__    )






class        __UpperCAmelCase						(     _a     ):
				A__				:       int  							=	'''translation'''
				A__				:       Tuple  							=	['''bleu''']
				A__				:       int  							=	'''bleu'''

				def __init__(				self     ,			_lowerCamelCase     ,			**_lowerCamelCase				):
										super().__init__(_lowerCamelCase     ,			**_lowerCamelCase				)
										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     ,			_lowerCamelCase				):
																lowerCamelCase__ =self.tokenizer.lang_code_to_id[hparams.tgt_lang]

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										return calculate_bleu(_lowerCamelCase     ,			_lowerCamelCase				)






				@staticmethod
				def 		_a     (				_lowerCamelCase     ,			_lowerCamelCase				):
										TranslationModule.add_model_specific_args(_lowerCamelCase     ,			_lowerCamelCase				)
										add_distill_args(_lowerCamelCase				)
										return parser





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Dict:






						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(snake_case__    )
						return model





def       lowerCamelCase_			( __lowerCAmelCase    )							-> List[str]:
						Path(args.output_dir    ).mkdir(exist_ok=snake_case__    )
						check_output_dir(snake_case__       ,   expected_items=3    )

						lowerCamelCase__ =create_module(snake_case__    )
						return ft_main(snake_case__       ,   model=snake_case__    )


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"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['OpenAIGPTTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

	a							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


