





'''simple docstring'''






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__lowerCamelCase						: List[str]		=   'sshleifer/mar_enro_6_3_student'







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Optional[Any]:
    super().setUp()

    _UpperCamelCase       			=cached_path(
        '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''							,	extract_compressed_file=UpperCamelCase__							,	)
    _UpperCamelCase       			=F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''


  @slow
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    MarianMTModel.from_pretrained(UpperCamelCase__  )




  @slow
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			={
        '''$MAX_LEN''': 64,
        '''$BS''': 64,
        '''$GAS''': 1,
        '''$ENRO_DIR''': self.data_dir,
        '''facebook/mbart-large-cc25''': MARIAN_MODEL,
        # "val_check_interval=0.25": "val_check_interval=1.0",
        '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
        '''--num_train_epochs 6''': '''--num_train_epochs 1''',
    }

    # Clean up bash script
    _UpperCamelCase       			=(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''  )[1].strip()
    _UpperCamelCase       			=bash_script.replace('''\\\n'''							,	''''''  ).strip().replace('''"$@"'''							,	''''''  )
    for k, v in env_vars_to_replace.items():
      _UpperCamelCase       			=bash_script.replace(UpperCamelCase__							,	str(UpperCamelCase__  )  )
    _UpperCamelCase       			=self.get_auto_remove_tmp_dir()

    # bash_script = bash_script.replace("--fp16 ", "")
    _UpperCamelCase       			=F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
    # XXX: args.gpus > 1 : handle multi_gpu in the future

    _UpperCamelCase       			=['''finetune.py'''] + bash_script.split() + args
    with patch.object(UpperCamelCase__							,	'''argv'''							,	UpperCamelCase__  ):
      _UpperCamelCase       			=argparse.ArgumentParser()
      _UpperCamelCase       			=pl.Trainer.add_argparse_args(UpperCamelCase__  )
      _UpperCamelCase       			=SummarizationModule.add_model_specific_args(UpperCamelCase__							,	os.getcwd()  )
      _UpperCamelCase       			=parser.parse_args()
      _UpperCamelCase       			=main(UpperCamelCase__  )

    # Check metrics
    _UpperCamelCase       			=load_json(model.metrics_save_path  )
    _UpperCamelCase       			=metrics['''val'''][0]
    _UpperCamelCase       			=metrics['''val'''][-1]
    self.assertEqual(len(metrics['''val''']  )							,	(args.max_epochs / args.val_check_interval)  )
    assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']							,	UpperCamelCase__  )

    self.assertGreater(last_step_stats['''val_avg_gen_time''']							,	0.01  )
    # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
    self.assertLessEqual(last_step_stats['''val_avg_gen_time''']							,	1.0  )

    # test learning requirements:

    # 1. BLEU improves over the course of training by more than 2 pts
    self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']							,	2  )

    # 2. BLEU finishes above 17
    self.assertGreater(last_step_stats['''val_avg_bleu''']							,	17  )

    # 3. test BLEU and val BLEU within ~1.1 pt.
    self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']  )							,	1.1  )

    # check lightning ckpt can be loaded and has a reasonable statedict
    _UpperCamelCase       			=os.listdir(UpperCamelCase__  )
    _UpperCamelCase       			=[x for x in contents if x.endswith('''.ckpt'''  )][0]
    _UpperCamelCase       			=os.path.join(args.output_dir							,	UpperCamelCase__  )
    _UpperCamelCase       			=torch.load(UpperCamelCase__							,	map_location='''cpu'''  )
    _UpperCamelCase       			='''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
      _UpperCamelCase       			={os.path.basename(UpperCamelCase__  ) for p in contents}
      assert "test_generations.txt" in contents
      assert "test_results.txt" in contents
      # assert len(metrics["val"]) ==  desired_n_evals
      assert len(metrics['''test''']  ) == 1







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @timeout_decorator.timeout(600  )
  @slow
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[str]:
    _UpperCamelCase       			=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
    _UpperCamelCase       			={
        '''--fp16_opt_level=O1''': '''''',
        '''$MAX_LEN''': 128,
        '''$BS''': 16,
        '''$GAS''': 1,
        '''$ENRO_DIR''': data_dir,
        '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
        '''val_check_interval=0.25''': '''val_check_interval=1.0''',
    }

    # Clean up bash script
    _UpperCamelCase       			=(
        (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''  )[1].strip()
    )
    _UpperCamelCase       			=bash_script.replace('''\\\n'''							,	''''''  ).strip().replace('''"$@"'''							,	''''''  )
    _UpperCamelCase       			=bash_script.replace('''--fp16 '''							,	''' '''  )

    for k, v in env_vars_to_replace.items():
      _UpperCamelCase       			=bash_script.replace(UpperCamelCase__							,	str(UpperCamelCase__  )  )
    _UpperCamelCase       			=self.get_auto_remove_tmp_dir()
    _UpperCamelCase       			=bash_script.replace('''--fp16'''							,	''''''  )
    _UpperCamelCase       			=6
    _UpperCamelCase       			=(
        ['''distillation.py''']
        + bash_script.split()
        + [
            F'''--output_dir={output_dir}''',
            '''--gpus=1''',
            '''--learning_rate=1e-3''',
            F'''--num_train_epochs={epochs}''',
            '''--warmup_steps=10''',
            '''--val_check_interval=1.0''',
            '''--do_predict''',
        ]
    )
    with patch.object(UpperCamelCase__							,	'''argv'''							,	UpperCamelCase__  ):
      _UpperCamelCase       			=argparse.ArgumentParser()
      _UpperCamelCase       			=pl.Trainer.add_argparse_args(UpperCamelCase__  )
      _UpperCamelCase       			=SummarizationDistiller.add_model_specific_args(UpperCamelCase__							,	os.getcwd()  )
      _UpperCamelCase       			=parser.parse_args()
      # assert args.gpus == gpus THIS BREAKS for multi_gpu

      _UpperCamelCase       			=distill_main(UpperCamelCase__  )

    # Check metrics
    _UpperCamelCase       			=load_json(model.metrics_save_path  )
    _UpperCamelCase       			=metrics['''val'''][0]
    _UpperCamelCase       			=metrics['''val'''][-1]
    assert len(metrics['''val''']  ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

    assert last_step_stats["val_avg_gen_time"] >= 0.01

    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
    assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']							,	UpperCamelCase__  )

    # check lightning ckpt can be loaded and has a reasonable statedict
    _UpperCamelCase       			=os.listdir(UpperCamelCase__  )
    _UpperCamelCase       			=[x for x in contents if x.endswith('''.ckpt'''  )][0]
    _UpperCamelCase       			=os.path.join(args.output_dir							,	UpperCamelCase__  )
    _UpperCamelCase       			=torch.load(UpperCamelCase__							,	map_location='''cpu'''  )
    _UpperCamelCase       			='''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
      _UpperCamelCase       			={os.path.basename(UpperCamelCase__  ) for p in contents}
      assert "test_generations.txt" in contents
      assert "test_results.txt" in contents
      # assert len(metrics["val"]) ==  desired_n_evals
      assert len(metrics['''test''']  ) == 1













'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbart import MBartTokenizer
else:
      __lowerCamelCase						: List[Any]		=   None


__lowerCamelCase						: Optional[int]		=   logging.get_logger(__name__)


__lowerCamelCase						: Tuple		=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Union[str, Any]		=   {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase						: List[str]		=   {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__lowerCamelCase						: Optional[int]		=   ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]
  lowerCAmelCase_					=				MBartTokenizer

  lowerCAmelCase_					=				[]
  lowerCAmelCase_					=				[]


  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					List[Any]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					List[str]="<s>"							,	UpperCamelCase__    :					Optional[Any]="</s>"							,	UpperCamelCase__    :					Dict="</s>"							,	UpperCamelCase__    :					str="<s>"							,	UpperCamelCase__    :					Tuple="<unk>"							,	UpperCamelCase__    :					Dict="<pad>"							,	UpperCamelCase__    :					str="<mask>"							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Dict=None							,	**UpperCamelCase__    :					Any							,	)     ->						List[str]:
    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else mask_token

    super().__init__(
        vocab_file=UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	bos_token=UpperCamelCase__							,	eos_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	src_lang=UpperCamelCase__							,	tgt_lang=UpperCamelCase__							,	additional_special_tokens=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=vocab_file
    _UpperCamelCase       			=False if not self.vocab_file else True

    _UpperCamelCase       			=FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]  )

    self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}  )
    _UpperCamelCase       			={
        lang_code: self.convert_tokens_to_ids(UpperCamelCase__  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    _UpperCamelCase       			=src_lang if src_lang is not None else '''en_XX'''
    _UpperCamelCase       			=self.convert_tokens_to_ids(self._src_lang  )
    _UpperCamelCase       			=tgt_lang
    self.set_src_lang_special_tokens(self._src_lang  )


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						str:
    return self._src_lang


  @src_lang.setter
  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=new_src_lang
    self.set_src_lang_special_tokens(self._src_lang  )


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep  ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str]							,	UpperCamelCase__    :					Optional[str]							,	**UpperCamelCase__    :					List[str]  )     ->						str:
    if src_lang is None or tgt_lang is None:
      raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
    _UpperCamelCase       			=src_lang
    _UpperCamelCase       			=self(UpperCamelCase__							,	add_special_tokens=UpperCamelCase__							,	return_tensors=UpperCamelCase__							,	**UpperCamelCase__  )
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=tgt_lang_id
    return inputs


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str = "en_XX"							,	UpperCamelCase__    :					Optional[List[str]] = None							,	UpperCamelCase__    :					str = "ro_RO"							,	**UpperCamelCase__    :					Optional[int]							,	)     ->						BatchEncoding:
    _UpperCamelCase       			=src_lang
    _UpperCamelCase       			=tgt_lang
    return super().prepare_seqaseq_batch(UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[str]:
    return self.set_src_lang_special_tokens(self.src_lang  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang  )


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[self.eos_token_id, self.cur_lang_code]

    _UpperCamelCase       			=self.convert_ids_to_tokens(self.prefix_tokens  )
    _UpperCamelCase       			=self.convert_ids_to_tokens(self.suffix_tokens  )

    _UpperCamelCase       			=processors.TemplateProcessing(
        single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str							,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str							,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,	self.prefix_tokens + self.suffix_tokens  )  )							,	)


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[self.eos_token_id, self.cur_lang_code]

    _UpperCamelCase       			=self.convert_ids_to_tokens(self.prefix_tokens  )
    _UpperCamelCase       			=self.convert_ids_to_tokens(self.suffix_tokens  )

    _UpperCamelCase       			=processors.TemplateProcessing(
        single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str							,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str							,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,	self.prefix_tokens + self.suffix_tokens  )  )							,	)




  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    if not self.can_save_slow_tokenizer:
      raise ValueError(
          '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
          '''tokenizer.'''  )

    if not os.path.isdir(UpperCamelCase__  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''  )
      return
    _UpperCamelCase       			=os.path.join(
        UpperCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

    if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase__  ):
      copyfile(self.vocab_file							,	UpperCamelCase__  )

    return (out_vocab_file,)






