'''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 ( _snake_case	):
 UpperCAmelCase				=				["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]


 def __init__(			self       :  Optional[int] ,					__lowerCamelCase       :  str   ):
    assert Path(hparams.data_dir   ).exists()
    UpperCAmelCase__  :List[str]            =			Path(hparams.output_dir   )
    self.output_dir.mkdir(exist_ok=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.output_dir.joinpath('''student'''   )

    UpperCAmelCase__  :Union[str, Any]            =			str(__lowerCamelCase   )  # Tell lightning we are training the student
    UpperCAmelCase__  :Optional[int]            =			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:
       UpperCAmelCase__  :int            =			AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
       use_task_specific_params(__lowerCamelCase ,					hparams.task   )
       UpperCAmelCase__  :Optional[Any]            =			None, None
    else:
       UpperCAmelCase__  :List[str]            =			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:
       UpperCAmelCase__  :int            =			hparams.length_penalty
    UpperCAmelCase__  :Optional[Any]            =			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":
       UpperCAmelCase__  :Dict            =			len(student.get_encoder().block   )
       UpperCAmelCase__  :int            =			len(student.get_decoder().block   )
       UpperCAmelCase__  :str            =			len(teacher.get_encoder().block   )
       UpperCAmelCase__  :int            =			len(teacher.get_decoder().block   )
    else:
       UpperCAmelCase__  :Optional[int]            =			student.config.encoder_layers
       UpperCAmelCase__  :List[str]            =			student.config.decoder_layers
       UpperCAmelCase__  :Optional[Any]            =			teacher.config.encoder_layers
       UpperCAmelCase__  :List[str]            =			teacher.config.decoder_layers

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

    UpperCAmelCase__  :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:
          UpperCAmelCase__  :List[Any]            =			get_layers_to_supervise(
              n_student=len(self.e_layer_ids   ) ,					n_teacher=__lowerCamelCase   )
          UpperCAmelCase__  :Optional[int]            =			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
          UpperCAmelCase__  :Union[str, Any]            =			self.e_layer_ids
          UpperCAmelCase__  :Tuple            =			self.d_layer_ids
    else:
       UpperCAmelCase__  :str            =			None
       UpperCAmelCase__  :str            =			None

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


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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  dict   ):
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer.pad_token_id
    UpperCAmelCase__  :List[Any]            =			batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
    if isinstance(self.model ,					__lowerCamelCase   ):
       UpperCAmelCase__  :Dict            =			self.model._shift_right(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :int            =			shift_tokens_right(__lowerCamelCase ,					__lowerCamelCase   )

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

    UpperCAmelCase__  :Optional[Any]            =			student_outputs[
        '''encoder_last_hidden_state'''
    ]  # use this unless self.different_base_models
    UpperCAmelCase__  :Tuple            =			zero_tensor(), zero_tensor()
    if self.different_encoder:  # compute encoder hidden state loss
       UpperCAmelCase__  :List[str]            =			self.teacher.get_encoder()(
           __lowerCamelCase ,					attention_mask=__lowerCamelCase ,					output_hidden_states=self.do_calc_hidden_loss ,					)
       if self.different_base_models:
          UpperCAmelCase__  :Optional[int]            =			all_teacher_encoder_outputs['''last_hidden_state''']
       elif self.do_calc_hidden_loss:
          UpperCAmelCase__  :Tuple            =			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 ,					)

    UpperCAmelCase__  :int            =			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 ,					)
    UpperCAmelCase__  :Optional[int]            =			decoder_input_ids.ne(__lowerCamelCase   )
    UpperCAmelCase__  :int            =			self.calc_ce_loss(__lowerCamelCase ,					__lowerCamelCase ,					teacher_outputs['''logits''']   )
    if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       UpperCAmelCase__  :str            =			self.calc_hidden_loss(
           __lowerCamelCase ,					student_outputs['''decoder_hidden_states'''] ,					teacher_outputs['''decoder_hidden_states'''] ,					self.d_matches ,					normalize_hidden=self.hparams.normalize_hidden ,					)

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


def 					a__    (				UpperCamelCase_	:			Any	):
   # 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=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 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				"translation"
 UpperCAmelCase				=				["bleu"]
 UpperCAmelCase				=				"bleu"


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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Optional[Any]   ):
    return calculate_bleu(__lowerCamelCase ,					__lowerCamelCase   )





 @staticmethod
 def 					__SCREAMING_SNAKE_CASE				(			__lowerCamelCase       :  Any ,					__lowerCamelCase       :  Optional[Any]   ):
    TranslationModule.add_model_specific_args(__lowerCamelCase ,					__lowerCamelCase   )
    add_distill_args(__lowerCamelCase   )
    return parser


def 					a__    (				UpperCamelCase_	:			List[str]	):
   if args.no_teacher:
      UpperCAmelCase__  :Dict            =			TranslationModule if '''translation''' in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      UpperCAmelCase__  :Tuple            =			TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
   UpperCAmelCase__  :str            =			module_cls.__name__
   print(F'''using module {args.setup_cls}'''	)
   UpperCAmelCase__  :List[str]            =			module_cls(UpperCamelCase_	)
   return model


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   Path(args.output_dir	).mkdir(exist_ok=UpperCamelCase_	)
   check_output_dir(UpperCamelCase_,      expected_items=3	)

   UpperCAmelCase__  :Dict            =			create_module(UpperCamelCase_	)
   return ft_main(UpperCamelCase_,      model=UpperCamelCase_	)


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

    distill_main(args)







'''simple docstring'''





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCamelCase       				=	'''stas/tiny-wmt19-en-ru'''




class 							UpperCAmelCase ( _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				FSMTTokenizer
 UpperCAmelCase				=				False


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase__  :Any            =			[
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    UpperCAmelCase__  :Dict            =			dict(zip(__lowerCamelCase ,					range(len(__lowerCamelCase   )   )   )   )
    UpperCAmelCase__  :Dict            =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    UpperCAmelCase__  :Dict            =			['''en''', '''ru''']
    UpperCAmelCase__  :Tuple            =			{
        '''langs''': self.langs,
        '''src_vocab_size''': 1_0,
        '''tgt_vocab_size''': 2_0,
    }

    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''src_vocab_file''']   )
    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
    UpperCAmelCase__  :Dict            =			os.path.join(self.tmpdirname ,					'''tokenizer_config.json'''   )
    UpperCAmelCase__  :Union[str, Any]            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']   )
    with open(self.src_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.tgt_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.merges_file ,					'''w'''   ) as fp:
       fp.write('''\n'''.join(__lowerCamelCase   )   )
    with open(__lowerCamelCase ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :int            =			FSMTTokenizer.from_pretrained(__lowerCamelCase   )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,					['''en''', '''ru''']   )
    self.assertEqual(tokenizer.src_vocab_size ,					2_1   )
    self.assertEqual(tokenizer.tgt_vocab_size ,					2_1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Tuple            =			FSMTTokenizer(self.langs ,					self.src_vocab_file ,					self.tgt_vocab_file ,					self.merges_file   )

    UpperCAmelCase__  :Any            =			'''lower'''
    UpperCAmelCase__  :List[Any]            =			['''low''', '''er</w>''']
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.tokenize(__lowerCamelCase   )
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

    UpperCAmelCase__  :Dict            =			tokens + ['''<unk>''']
    UpperCAmelCase__  :Union[str, Any]            =			[1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   ) ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :str            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.encode('''sequence builders''' ,					add_special_tokens=__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__lowerCamelCase   )

    UpperCAmelCase__  :Any            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,					__lowerCamelCase   )

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :List[str]            =			self.tokenizer_en_ru
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Any            =			[
        [
            '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
            [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
        ],
        ['''This is it. No more. I\'m done!''', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
       UpperCAmelCase__  :List[Any]            =			tokenizer_enc.encode(__lowerCamelCase ,					return_tensors=__lowerCamelCase   )
       self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

       # and decode backward, using the reversed languages model
       UpperCAmelCase__  :str            =			tokenizer_dec.decode(__lowerCamelCase ,					skip_special_tokens=__lowerCamelCase   )
       self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' ,					do_lower_case=__lowerCamelCase   )
    UpperCAmelCase__  :str            =			tokenizer.tokenize('''USA is United States of America'''   )
    UpperCAmelCase__  :str            =			['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    pass





 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    pass



