




'''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						(	snake_case_							):
      __magic_name__     :					Dict             =				['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']






      def __init__(   self      :     List[Any]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           assert Path(hparams.data_dir					).exists()
           a_    :       Any    	=     Path(hparams.output_dir					)
           self.output_dir.mkdir(exist_ok=lowercase__					)

           a_    :       Dict    	=     self.output_dir.joinpath("""student"""					)

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

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

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

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

           a_    :       List[str]    	=     nn.KLDivLoss(reduction="""batchmean"""					)
           a_    :       Tuple    	=     2.0
           a_    :       Any    	=     hparams.alpha_mlm
           a_    :       str    	=     hparams.alpha_ce
           a_    :       Dict    	=     hparams.alpha_hid
           gc.collect()
           torch.cuda.empty_cache()






      def        lowercase_	(   self      :     Any	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Optional[int]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       str    	=     mask[:, :, None].expand_as(lowercase__					)
           a_    :       List[Any]    	=     s_logits.size(-1					)
           a_    :       List[Any]    	=     torch.masked_select(lowercase__	,	lowercase__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
           a_    :       List[Any]    	=     torch.masked_select(lowercase__	,	lowercase__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
           a_    :       int    	=     s_logits_slct.view(-1	,	lowercase__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
           a_    :       List[str]    	=     t_logits_slct.view(-1	,	lowercase__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
           assert t_logits_slct.size() == s_logits_slct.size()
           a_    :       Optional[Any]    	=     (
               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        lowercase_	(   lowercase__      :     Dict	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     Dict	,	lowercase__      :     dict					):


           '''simple docstring'''

           a_    :       int    	=     self.tokenizer.pad_token_id
           a_    :       Any    	=     batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
           if isinstance(self.model	,	lowercase__					):
                a_    :       List[str]    	=     self.model._shift_right(lowercase__					)
           else:
                a_    :       Any    	=     shift_tokens_right(lowercase__	,	lowercase__					)

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

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

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

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

           a_    :       List[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        lowercase_	(   lowercase__      :     Union[str, Any]	,	lowercase__      :     Any	,	lowercase__      :     Tuple	,	lowercase__      :     Any	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict							):




     """simple docstring"""


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







class     SCREAMING_SNAKE_CASE						(	snake_case_							):
      __magic_name__     :					str             =				'''translation'''
      __magic_name__     :					Union[str, Any]             =				['''bleu''']
      __magic_name__     :					str             =				'''bleu'''






      def __init__(   self      :     List[str]	,	lowercase__      :     Tuple	,	**lowercase__      :     List[str]					):


           '''simple docstring'''

           super().__init__(lowercase__	,	**lowercase__					)
           assert hparams.src_lang is not None
           assert hparams.tgt_lang is not None
           a_    :       List[str]    	=     hparams.src_lang
           a_    :       Dict    	=     hparams.tgt_lang
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,	lowercase__					):
                a_    :       int    	=     self.tokenizer.lang_code_to_id[hparams.tgt_lang]






      def        lowercase_	(   self      :     Union[str, Any]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     int					):


           '''simple docstring'''

           return calculate_bleu(lowercase__	,	lowercase__					)






      @staticmethod
      def        lowercase_	(   lowercase__      :     List[Any]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict							):




     """simple docstring"""


     if args.no_teacher:
          a_    :       Optional[int]    	=     TranslationModule if """translation""" in args.task else SummarizationModule
     else:  # DISTILL WITH TEACHER
          a_    :       List[str]    	=     TranslationDistiller if """translation""" in args.task else SummarizationDistiller
     a_    :       str    	=     module_cls.__name__
     print(F"using module {args.setup_cls}"							)
     a_    :       str    	=     module_cls(UpperCamelCase__							)
     return model



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     Path(args.output_dir							).mkdir(exist_ok=UpperCamelCase__							)
     check_output_dir(UpperCamelCase__   ,						expected_items=3							)

     a_    :       str    	=     create_module(UpperCamelCase__							)
     return ft_main(UpperCamelCase__   ,						model=UpperCamelCase__							)


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

       distill_main(args)






'''simple docstring'''


import shutil

import requests



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				bool = False   ,						UpperCamelCase__						:				str = "."							):




     """simple docstring"""


     a_    :       Any    	=     """https://api.nasa.gov/planetary/apod"""
     return requests.get(UpperCamelCase__   ,						params={"""api_key""": api_key}							).json()



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				str = "."							):




     """simple docstring"""


     a_    :       Tuple    	=     get_apod_data(UpperCamelCase__							)
     a_    :       Tuple    	=     apod_data["""url"""]
     a_    :       Any    	=     img_url.split("""/"""							)[-1]
     a_    :       Tuple    	=     requests.get(UpperCamelCase__   ,						stream=UpperCamelCase__							)

     with open(F"{path}/{img_name}"   ,						"""wb+"""							) as img_file:
          shutil.copyfileobj(response.raw   ,						UpperCamelCase__							)
     del response
     return apod_data



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       str    	=     """https://images-api.nasa.gov/search"""
     return requests.get(UpperCamelCase__   ,						params={"""q""": query}							).json()


if __name__ == "__main__":
 print(save_apod('YOUR API KEY'))
 lowerCAmelCase_			:     int							    =     get_archive_data('apollo 2011')['collection']['items']
 print(apollo_aaaa_items[0]['data'][0]['description'])
