





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


UpperCamelCase_	 ="""./tests/fixtures/elise_format0.mid"""




@unittest.skip('''The note-seq package currently throws an error on import'''							)
class 					_a		( _lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       SpectrogramDiffusionPipeline
				UpperCamelCase								=       PipelineTesterMixin.required_optional_params - {
				    '''callback''',
				    '''latents''',
				    '''callback_steps''',
				    '''output_type''',
				    '''num_images_per_prompt''',
				}
				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       TOKENS_TO_AUDIO_GENERATION_PARAMS
				UpperCamelCase								=       TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






				def 							snake_case      (					self						:       int				)      -> str:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   int      =       SpectrogramNotesEncoder(
										    max_length=2_0_4_8,					vocab_size=1_5_3_6,					d_model=7_6_8,					dropout_rate=0.1,					num_layers=1,					num_heads=1,					d_kv=4,					d_ff=2_0_4_8,					feed_forward_proj='''gated-gelu''',					)

										_UpperCamelCase     :   Optional[Any]      =       SpectrogramContEncoder(
										    input_dims=1_2_8,					targets_context_length=2_5_6,					d_model=7_6_8,					dropout_rate=0.1,					num_layers=1,					num_heads=1,					d_kv=4,					d_ff=2_0_4_8,					feed_forward_proj='''gated-gelu''',					)

										_UpperCamelCase     :   List[str]      =       TaFilmDecoder(
										    input_dims=1_2_8,					targets_length=2_5_6,					max_decoder_noise_time=20_000.0,					d_model=7_6_8,					num_layers=1,					num_heads=1,					d_kv=4,					d_ff=2_0_4_8,					dropout_rate=0.1,					)

										_UpperCamelCase     :   Optional[Any]      =       DDPMScheduler()

										_UpperCamelCase     :   Any      =       {
										    '''notes_encoder''': notes_encoder.eval(),
										    '''continuous_encoder''': continuous_encoder.eval(),
										    '''decoder''': decoder.eval(),
										    '''scheduler''': scheduler,
										    '''melgan''': None,
										}
										return components






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       str=0				)      -> Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__				).startswith('''mps'''				):
																_UpperCamelCase     :   Union[str, Any]      =       torch.manual_seed(lowerCAmelCase__				)
										else:
																_UpperCamelCase     :   List[str]      =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       {
										    '''input_tokens''': [
										        [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
										    ],
										    '''generator''': generator,
										    '''num_inference_steps''': 4,
										    '''output_type''': '''mel''',
										}
										return inputs






				def 							snake_case      (					self						:       List[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase     :   Optional[int]      =       self.get_dummy_components()
										_UpperCamelCase     :   Tuple      =       SpectrogramDiffusionPipeline(**lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       pipe(**lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       output.audios

										_UpperCamelCase     :   Tuple      =       mel[0, -3:, -3:]

										assert mel_slice.shape == (3, 3)
										_UpperCamelCase     :   Tuple      =       np.array(
										    [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0]				)
										assert np.abs(mel_slice.flatten() - expected_slice				).max() < 1e-2






				@skip_mps
				def 							snake_case      (					self						:       Union[str, Any]				)      -> List[Any]:




										'''simple docstring'''



										return super().test_save_load_local()






				@skip_mps
				def 							snake_case      (					self						:       str				)      -> Union[str, Any]:




										'''simple docstring'''



										return super().test_dict_tuple_outputs_equivalent()






				@skip_mps
				def 							snake_case      (					self						:       List[Any]				)      -> int:




										'''simple docstring'''



										return super().test_save_load_optional_components()






				@skip_mps
				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										return super().test_attention_slicing_forward_pass()






				def 							snake_case      (					self						:       Any				)      -> Dict:




										'''simple docstring'''



										pass






				def 							snake_case      (					self						:       List[str]				)      -> List[Any]:




										'''simple docstring'''



										pass






				@skip_mps
				def 							snake_case      (					self						:       int				)      -> List[Any]:




										'''simple docstring'''



										return super().test_progress_bar()




@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       Optional[int]				)      -> List[str]:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 							snake_case      (					self						:       int				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       torch_device

										_UpperCamelCase     :   Union[str, Any]      =       SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''				)
										_UpperCamelCase     :   Dict      =       pipe.melgan
										_UpperCamelCase     :   Tuple      =       None

										_UpperCamelCase     :   Tuple      =       pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										def callback(lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Optional[Any]				):
																# decode mel to audio
																_UpperCamelCase     :   Optional[Any]      =       melgan(input_features=mel_output.astype(np.floataa				)				)[0]
																assert len(audio[0]				) == 8_1_9_2_0 * (step + 1)
																# simulate that audio is played
																return audio

										_UpperCamelCase     :   Dict      =       MidiProcessor()
										_UpperCamelCase     :   Tuple      =       processor(lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[int]      =       input_tokens[:3]
										_UpperCamelCase     :   List[Any]      =       torch.manual_seed(0				)
										pipe(lowerCAmelCase__,					num_inference_steps=5,					generator=lowerCAmelCase__,					callback=lowerCAmelCase__,					output_type='''mel'''				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       torch_device

										_UpperCamelCase     :   str      =       SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''				)
										_UpperCamelCase     :   Any      =       pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       MidiProcessor()

										_UpperCamelCase     :   Tuple      =       processor(lowerCAmelCase__				)
										# just run two denoising loops
										_UpperCamelCase     :   Tuple      =       input_tokens[:2]

										_UpperCamelCase     :   List[str]      =       torch.manual_seed(0				)
										_UpperCamelCase     :   Any      =       pipe(lowerCAmelCase__,					num_inference_steps=2,					generator=lowerCAmelCase__				)

										_UpperCamelCase     :   Dict      =       output.audios[0]

										assert abs(np.abs(lowerCAmelCase__				).sum() - 3_612.841				) < 1e-1






				def 							snake_case      (					self						:       Tuple				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       torch_device

										_UpperCamelCase     :   Tuple      =       SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''				)
										_UpperCamelCase     :   Union[str, Any]      =       pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       MidiProcessor()

										_UpperCamelCase     :   Tuple      =       processor(lowerCAmelCase__				)

										# just run 4 denoising loops
										_UpperCamelCase     :   Tuple      =       input_tokens[:4]

										_UpperCamelCase     :   List[Any]      =       torch.manual_seed(0				)
										_UpperCamelCase     :   Dict      =       pipe(lowerCAmelCase__,					num_inference_steps=1_0_0,					generator=lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[int]      =       output.audios[0]
										assert abs(np.abs(lowerCAmelCase__				).sum() - 9_389.1_111				) < 5e-2




'''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 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


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

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

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

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

         lowercase_			,				lowercase_     			=   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:
                     lowercase_     			=   get_layers_to_supervise(
                         n_student=len(self.e_layer_ids) ,		n_teacher=__lowerCAmelCase)
                     lowercase_     			=   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
                     lowercase_     			=   self.e_layer_ids
                     lowercase_     			=   self.d_layer_ids
         else:
               lowercase_     			=   None
               lowercase_     			=   None

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


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


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

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

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

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

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

         lowercase_     			=   (
             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							( __lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Optional[Any])   ->       int:
         lowercase_     			=   "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}'
         lowercase_     			=   attention_mask.to(hidden_states[0])
         lowercase_     			=   mask.sum() * hidden_states[0].size(-1)
         lowercase_     			=   torch.stack([hidden_states[i] for i in range(len(__lowerCAmelCase))])
         lowercase_     			=   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:
               lowercase_     			=   nn.functional.layer_norm(__lowerCAmelCase ,		student_states.shape[1:])
               lowercase_     			=   nn.functional.layer_norm(__lowerCAmelCase ,		teacher_states.shape[1:])
         lowercase_     			=   nn.functional.mse_loss(__lowerCAmelCase ,		__lowerCAmelCase ,		reduction="none")
         lowercase_     			=   (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
         return masked_mse




def __a			(						__lowerCamelCase      :    Tuple					)						->				str:
      '''simple docstring'''


      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 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            ='translation'
   lowerCamelCase_            =['bleu']
   lowerCamelCase_            ='bleu'


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						str ,		**__lowerCAmelCase					:						List[Any])   ->       Dict:
         super().__init__(__lowerCAmelCase ,		**__lowerCAmelCase)
         assert hparams.src_lang is not None
         assert hparams.tgt_lang is not None
         lowercase_     			=   hparams.src_lang
         lowercase_     			=   hparams.tgt_lang
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,		__lowerCAmelCase):
               lowercase_     			=   self.tokenizer.lang_code_to_id[hparams.tgt_lang]


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Tuple)   ->       dict:
         return calculate_bleu(__lowerCAmelCase ,		__lowerCAmelCase)


   @staticmethod
   def __UpperCAmelCase							( __lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Tuple)   ->       str:
         TranslationModule.add_model_specific_args(__lowerCAmelCase ,		__lowerCAmelCase)
         add_distill_args(__lowerCAmelCase)
         return parser




def __a			(						__lowerCamelCase      :    List[str]					)						->				Union[str, Any]:
      '''simple docstring'''


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


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				int:
      '''simple docstring'''


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

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


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

       distill_main(args)






