



"""simple docstring"""




import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_UpperCamelCase: List[str]										=     {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def      lowercase__   (	_UpperCAmelCase )   ->	Any:






      '''simple docstring'''


      assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
      assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
      if args.mlm:
            assert os.path.isfile(args.token_counts )
            assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
      else:
            assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

      assert args.teacher_type == args.student_type or (
          args.student_type == "distilbert" and args.teacher_type == "bert"
      )
      assert os.path.isfile(args.student_config )
      if args.student_pretrained_weights is not None:
            assert os.path.isfile(args.student_pretrained_weights )

      if args.freeze_token_type_embds:
            assert args.student_type in ["roberta"]

      assert args.alpha_ce >= 0.0
      assert args.alpha_mlm >= 0.0
      assert args.alpha_clm >= 0.0
      assert args.alpha_mse >= 0.0
      assert args.alpha_cos >= 0.0
      assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Optional[Any]:






      '''simple docstring'''


      if args.student_type == "roberta":
            lowercase     :					Tuple 		=      False
      elif args.student_type == "gpt2":
            lowercase     :					List[str] 		=      False


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	List[str]:






      '''simple docstring'''


      if args.student_type == "roberta":
            lowercase     :					List[str] 		=      False


def      lowercase__   (	)   ->	Tuple:






      '''simple docstring'''


      lowercase     :					Dict 		=      argparse.ArgumentParser(description='Training' )
      parser.add_argument('--force'		,					action='store_true'		,					help='Overwrite dump_path if it already exists.' )

      parser.add_argument(
          '--dump_path'		,					type=lowerCAmelCase_		,					required=lowerCAmelCase_		,					help='The output directory (log, checkpoints, parameters, etc.)' )
      parser.add_argument(
          '--data_file'		,					type=lowerCAmelCase_		,					required=lowerCAmelCase_		,					help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'		,					)

      parser.add_argument(
          '--student_type'		,					type=lowerCAmelCase_		,					choices=['distilbert', 'roberta', 'gpt2']		,					required=lowerCAmelCase_		,					help='The student type (DistilBERT, RoBERTa).'		,					)
      parser.add_argument('--student_config'		,					type=lowerCAmelCase_		,					required=lowerCAmelCase_		,					help='Path to the student configuration.' )
      parser.add_argument(
          '--student_pretrained_weights'		,					default=lowerCAmelCase_		,					type=lowerCAmelCase_		,					help='Load student initialization checkpoint.' )

      parser.add_argument(
          '--teacher_type'		,					choices=['bert', 'roberta', 'gpt2']		,					required=lowerCAmelCase_		,					help='Teacher type (BERT, RoBERTa).' )
      parser.add_argument('--teacher_name'		,					type=lowerCAmelCase_		,					required=lowerCAmelCase_		,					help='The teacher model.' )

      parser.add_argument('--temperature'		,					default=2.0		,					type=lowerCAmelCase_		,					help='Temperature for the softmax temperature.' )
      parser.add_argument(
          '--alpha_ce'		,					default=0.5		,					type=lowerCAmelCase_		,					help='Linear weight for the distillation loss. Must be >=0.' )
      parser.add_argument(
          '--alpha_mlm'		,					default=0.0		,					type=lowerCAmelCase_		,					help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'		,					)
      parser.add_argument('--alpha_clm'		,					default=0.5		,					type=lowerCAmelCase_		,					help='Linear weight for the CLM loss. Must be >=0.' )
      parser.add_argument('--alpha_mse'		,					default=0.0		,					type=lowerCAmelCase_		,					help='Linear weight of the MSE loss. Must be >=0.' )
      parser.add_argument(
          '--alpha_cos'		,					default=0.0		,					type=lowerCAmelCase_		,					help='Linear weight of the cosine embedding loss. Must be >=0.' )

      parser.add_argument(
          '--mlm'		,					action='store_true'		,					help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
      parser.add_argument(
          '--mlm_mask_prop'		,					default=0.1_5		,					type=lowerCAmelCase_		,					help='Proportion of tokens for which we need to make a prediction.'		,					)
      parser.add_argument('--word_mask'		,					default=0.8		,					type=lowerCAmelCase_		,					help='Proportion of tokens to mask out.' )
      parser.add_argument('--word_keep'		,					default=0.1		,					type=lowerCAmelCase_		,					help='Proportion of tokens to keep.' )
      parser.add_argument('--word_rand'		,					default=0.1		,					type=lowerCAmelCase_		,					help='Proportion of tokens to randomly replace.' )
      parser.add_argument(
          '--mlm_smoothing'		,					default=0.7		,					type=lowerCAmelCase_		,					help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'		,					)
      parser.add_argument('--token_counts'		,					type=lowerCAmelCase_		,					help='The token counts in the data_file for MLM.' )

      parser.add_argument(
          '--restrict_ce_to_mask'		,					action='store_true'		,					help='If true, compute the distillation loss only the [MLM] prediction distribution.'		,					)
      parser.add_argument(
          '--freeze_pos_embs'		,					action='store_true'		,					help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'		,					)
      parser.add_argument(
          '--freeze_token_type_embds'		,					action='store_true'		,					help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'		,					)

      parser.add_argument('--n_epoch'		,					type=lowerCAmelCase_		,					default=3		,					help='Number of pass on the whole dataset.' )
      parser.add_argument('--batch_size'		,					type=lowerCAmelCase_		,					default=5		,					help='Batch size (for each process).' )
      parser.add_argument(
          '--group_by_size'		,					action='store_false'		,					help='If true, group sequences that have similar length into the same batch. Default is true.'		,					)

      parser.add_argument(
          '--gradient_accumulation_steps'		,					type=lowerCAmelCase_		,					default=50		,					help='Gradient accumulation for larger training batches.'		,					)
      parser.add_argument('--warmup_prop'		,					default=0.0_5		,					type=lowerCAmelCase_		,					help='Linear warmup proportion.' )
      parser.add_argument('--weight_decay'		,					default=0.0		,					type=lowerCAmelCase_		,					help='Weight decay if we apply some.' )
      parser.add_argument('--learning_rate'		,					default=5e-4		,					type=lowerCAmelCase_		,					help='The initial learning rate for Adam.' )
      parser.add_argument('--adam_epsilon'		,					default=1e-6		,					type=lowerCAmelCase_		,					help='Epsilon for Adam optimizer.' )
      parser.add_argument('--max_grad_norm'		,					default=5.0		,					type=lowerCAmelCase_		,					help='Max gradient norm.' )
      parser.add_argument('--initializer_range'		,					default=0.0_2		,					type=lowerCAmelCase_		,					help='Random initialization range.' )

      parser.add_argument(
          '--fp16'		,					action='store_true'		,					help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'		,					)
      parser.add_argument(
          '--fp16_opt_level'		,					type=lowerCAmelCase_		,					default='O1'		,					help=(
              'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
              'See details at https://nvidia.github.io/apex/amp.html'
          )		,					)
      parser.add_argument('--n_gpu'		,					type=lowerCAmelCase_		,					default=1		,					help='Number of GPUs in the node.' )
      parser.add_argument('--local_rank'		,					type=lowerCAmelCase_		,					default=-1		,					help='Distributed training - Local rank' )
      parser.add_argument('--seed'		,					type=lowerCAmelCase_		,					default=56		,					help='Random seed' )

      parser.add_argument('--log_interval'		,					type=lowerCAmelCase_		,					default=5_00		,					help='Tensorboard logging interval.' )
      parser.add_argument('--checkpoint_interval'		,					type=lowerCAmelCase_		,					default=40_00		,					help='Checkpoint interval.' )
      lowercase     :					Dict 		=      parser.parse_args()
      sanity_checks(lowerCAmelCase_ )

      # ARGS #
      init_gpu_params(lowerCAmelCase_ )
      set_seed(lowerCAmelCase_ )
      if args.is_master:
            if os.path.exists(args.dump_path ):
                  if not args.force:
                        raise ValueError(
                            f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
                            ' itUse `--force` if you want to overwrite it' )
                  else:
                        shutil.rmtree(args.dump_path )

            if not os.path.exists(args.dump_path ):
                  os.makedirs(args.dump_path )
            logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' )

            # SAVE PARAMS #
            logger.info(f'''Param: {args}''' )
            with open(os.path.join(args.dump_path		,					'parameters.json' )		,					'w' ) as f:
                  json.dump(vars(lowerCAmelCase_ )		,					lowerCAmelCase_		,					indent=4 )
            git_log(args.dump_path )

      lowercase     :					Dict 		=      MODEL_CLASSES[args.student_type]
      lowercase     :					Union[str, Any] 		=      MODEL_CLASSES[args.teacher_type]

      # TOKENIZER #
      lowercase     :					Any 		=      teacher_tokenizer_class.from_pretrained(args.teacher_name )
      lowercase     :					Dict 		=      {}
      for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
            lowercase     :					Tuple 		=      tokenizer.all_special_tokens.index(lowerCAmelCase_ )
            lowercase     :					Tuple 		=      tokenizer.all_special_ids[idx]
      logger.info(f'''Special tokens {special_tok_ids}''' )
      lowercase     :					List[Any] 		=      special_tok_ids
      lowercase     :					Tuple 		=      tokenizer.max_model_input_sizes[args.teacher_name]

      # DATA LOADER #
      logger.info(f'''Loading data from {args.data_file}''' )
      with open(args.data_file		,					'rb' ) as fp:
            lowercase     :					Optional[Any] 		=      pickle.load(lowerCAmelCase_ )

      if args.mlm:
            logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' )
            with open(args.token_counts		,					'rb' ) as fp:
                  lowercase     :					List[str] 		=      pickle.load(lowerCAmelCase_ )

            lowercase     :					int 		=      np.maximum(lowerCAmelCase_		,					1 ) ** -args.mlm_smoothing
            for idx in special_tok_ids.values():
                  lowercase     :					Any 		=      0.0  # do not predict special tokens
            lowercase     :					List[Any] 		=      torch.from_numpy(lowerCAmelCase_ )
      else:
            lowercase     :					Optional[int] 		=      None

      lowercase     :					Union[str, Any] 		=      LmSeqsDataset(params=lowerCAmelCase_		,					data=lowerCAmelCase_ )
      logger.info('Data loader created.' )

      # STUDENT #
      logger.info(f'''Loading student config from {args.student_config}''' )
      lowercase     :					List[Any] 		=      student_config_class.from_pretrained(args.student_config )
      lowercase     :					str 		=      True

      if args.student_pretrained_weights is not None:
            logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' )
            lowercase     :					Dict 		=      student_model_class.from_pretrained(args.student_pretrained_weights		,					config=lowerCAmelCase_ )
      else:
            lowercase     :					List[Any] 		=      student_model_class(lowerCAmelCase_ )

      if args.n_gpu > 0:
            student.to(f'''cuda:{args.local_rank}''' )
      logger.info('Student loaded.' )

      # TEACHER #
      lowercase     :					List[str] 		=      teacher_model_class.from_pretrained(args.teacher_name		,					output_hidden_states=lowerCAmelCase_ )
      if args.n_gpu > 0:
            teacher.to(f'''cuda:{args.local_rank}''' )
      logger.info(f'''Teacher loaded from {args.teacher_name}.''' )

      # FREEZING #
      if args.freeze_pos_embs:
            freeze_pos_embeddings(lowerCAmelCase_		,					lowerCAmelCase_ )
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(lowerCAmelCase_		,					lowerCAmelCase_ )

      # SANITY CHECKS #
      assert student.config.vocab_size == teacher.config.vocab_size
      assert student.config.hidden_size == teacher.config.hidden_size
      assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
      if args.mlm:
            assert token_probs.size(0 ) == stu_architecture_config.vocab_size

      # DISTILLER #
      torch.cuda.empty_cache()
      lowercase     :					int 		=      Distiller(
          params=lowerCAmelCase_		,					dataset=lowerCAmelCase_		,					token_probs=lowerCAmelCase_		,					student=lowerCAmelCase_		,					teacher=lowerCAmelCase_ )
      distiller.train()
      logger.info('Let\'s go get some drinks.' )


if __name__ == "__main__":
     main()




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase	:     List[str]   =get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
						from transformers.models.mbart.modeling_mbart import shift_tokens_right

_UpperCAmelCase	:     Optional[int]   =25_0004
_UpperCAmelCase	:     Tuple   =25_0020







@require_sentencepiece
@require_tokenizers
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Tuple    =							MBartTokenizer
						SCREAMING_SNAKE_CASE__					:     Dict    =							MBartTokenizerFast
						SCREAMING_SNAKE_CASE__					:     Tuple    =							True
						SCREAMING_SNAKE_CASE__					:     List[str]    =							True




						def   lowercase_       ( self							)    ->			Dict:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowerCAmelCase_       :					str			 = MBartTokenizer(__lowercase      ,  keep_accents=__lowercase							)
										tokenizer.save_pretrained(self.tmpdirname							)




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Optional[int]			 = MBartTokenizer(__lowercase      ,  keep_accents=__lowercase							)

										lowerCAmelCase_       :					Dict			 = tokenizer.tokenize('''This is a test'''							)
										self.assertListEqual(__lowercase      ,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowercase							)      ,  [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]      ,  )

										lowerCAmelCase_       :					Optional[Any]			 = tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
										self.assertListEqual(
										    __lowercase      ,  [
										        SPIECE_UNDERLINE + '''I''',
										        SPIECE_UNDERLINE + '''was''',
										        SPIECE_UNDERLINE + '''b''',
										        '''or''',
										        '''n''',
										        SPIECE_UNDERLINE + '''in''',
										        SPIECE_UNDERLINE + '''''',
										        '''9''',
										        '''2''',
										        '''0''',
										        '''0''',
										        '''0''',
										        ''',''',
										        SPIECE_UNDERLINE + '''and''',
										        SPIECE_UNDERLINE + '''this''',
										        SPIECE_UNDERLINE + '''is''',
										        SPIECE_UNDERLINE + '''f''',
										        '''al''',
										        '''s''',
										        '''é''',
										        '''.''',
										    ]      ,  )
										lowerCAmelCase_       :					Dict			 = tokenizer.convert_tokens_to_ids(__lowercase							)
										self.assertListEqual(
										    __lowercase      ,  [
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]      ,  )

										lowerCAmelCase_       :					Union[str, Any]			 = tokenizer.convert_ids_to_tokens(__lowercase							)
										self.assertListEqual(
										    __lowercase      ,  [
										        SPIECE_UNDERLINE + '''I''',
										        SPIECE_UNDERLINE + '''was''',
										        SPIECE_UNDERLINE + '''b''',
										        '''or''',
										        '''n''',
										        SPIECE_UNDERLINE + '''in''',
										        SPIECE_UNDERLINE + '''''',
										        '''<unk>''',
										        '''2''',
										        '''0''',
										        '''0''',
										        '''0''',
										        ''',''',
										        SPIECE_UNDERLINE + '''and''',
										        SPIECE_UNDERLINE + '''this''',
										        SPIECE_UNDERLINE + '''is''',
										        SPIECE_UNDERLINE + '''f''',
										        '''al''',
										        '''s''',
										        '''<unk>''',
										        '''.''',
										    ]      ,  )




						def   lowercase_       ( self							)    ->			Dict:
										if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

										lowerCAmelCase_       :					int			 = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																		lowerCAmelCase_       :					Optional[int]			 = self.rust_tokenizer_class.from_pretrained(__lowercase      ,  **__lowercase							)
																		lowerCAmelCase_       :					int			 = self.tokenizer_class.from_pretrained(__lowercase      ,  **__lowercase							)

																		lowerCAmelCase_       :					Tuple			 = tempfile.mkdtemp()

																		lowerCAmelCase_       :					Union[str, Any]			 = tokenizer_r.save_pretrained(__lowercase							)
																		lowerCAmelCase_       :					Dict			 = tokenizer_p.save_pretrained(__lowercase							)

																		# Checks it save with the same files + the tokenizer.json file for the fast one
																		self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files							)							)
																		lowerCAmelCase_       :					str			 = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f							)
																		self.assertSequenceEqual(__lowercase      ,  __lowercase							)

																		# Checks everything loads correctly in the same way
																		lowerCAmelCase_       :					Tuple			 = tokenizer_r.from_pretrained(__lowercase							)
																		lowerCAmelCase_       :					Dict			 = tokenizer_p.from_pretrained(__lowercase							)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__lowercase      ,  __lowercase							)							)
																						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																		shutil.rmtree(__lowercase							)

																		# Save tokenizer rust, legacy_format=True
																		lowerCAmelCase_       :					Optional[Any]			 = tempfile.mkdtemp()

																		lowerCAmelCase_       :					int			 = tokenizer_r.save_pretrained(__lowercase      ,  legacy_format=__lowercase							)
																		lowerCAmelCase_       :					Tuple			 = tokenizer_p.save_pretrained(__lowercase							)

																		# Checks it save with the same files
																		self.assertSequenceEqual(__lowercase      ,  __lowercase							)

																		# Checks everything loads correctly in the same way
																		lowerCAmelCase_       :					Optional[int]			 = tokenizer_r.from_pretrained(__lowercase							)
																		lowerCAmelCase_       :					List[Any]			 = tokenizer_p.from_pretrained(__lowercase							)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__lowercase      ,  __lowercase							)							)

																		shutil.rmtree(__lowercase							)

																		# Save tokenizer rust, legacy_format=False
																		lowerCAmelCase_       :					Optional[Any]			 = tempfile.mkdtemp()

																		lowerCAmelCase_       :					List[str]			 = tokenizer_r.save_pretrained(__lowercase      ,  legacy_format=__lowercase							)
																		lowerCAmelCase_       :					Optional[int]			 = tokenizer_p.save_pretrained(__lowercase							)

																		# Checks it saved the tokenizer.json file
																		self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files							)							)

																		# Checks everything loads correctly in the same way
																		lowerCAmelCase_       :					Dict			 = tokenizer_r.from_pretrained(__lowercase							)
																		lowerCAmelCase_       :					List[Any]			 = tokenizer_p.from_pretrained(__lowercase							)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__lowercase      ,  __lowercase							)							)

																		shutil.rmtree(__lowercase							)







@require_torch
@require_sentencepiece
@require_tokenizers
class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     int    =							"""facebook/mbart-large-en-ro"""
						SCREAMING_SNAKE_CASE__					:     int    =							[
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						SCREAMING_SNAKE_CASE__					:     Optional[int]    =							[
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						SCREAMING_SNAKE_CASE__					:     str    =							[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]




						@classmethod
						def   lowercase_       ( cls							)    ->			Optional[int]:
										lowerCAmelCase_       :					MBartTokenizer			 = MBartTokenizer.from_pretrained(
										    cls.checkpoint_name      ,  src_lang='''en_XX'''      ,  tgt_lang='''ro_RO'''							)
										lowerCAmelCase_       :					Optional[Any]			 = 1
										return cls




						def   lowercase_       ( self							)    ->			Optional[Any]:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']      ,  2_5_0_0_0_1							)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']      ,  2_5_0_0_0_4							)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']      ,  2_5_0_0_2_0							)




						def   lowercase_       ( self							)    ->			Tuple:
										lowerCAmelCase_       :					Optional[int]			 = self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
										self.assertListEqual(self.expected_src_tokens      ,  __lowercase							)




						def   lowercase_       ( self							)    ->			Any:
										self.assertIn(__lowercase      ,  self.tokenizer.all_special_ids							)
										lowerCAmelCase_       :					Union[str, Any]			 = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
										lowerCAmelCase_       :					Tuple			 = self.tokenizer.decode(__lowercase      ,  skip_special_tokens=__lowercase							)
										lowerCAmelCase_       :					List[Any]			 = self.tokenizer.decode(generated_ids[1:]      ,  skip_special_tokens=__lowercase							)
										self.assertEqual(__lowercase      ,  __lowercase							)
										self.assertNotIn(self.tokenizer.eos_token      ,  __lowercase							)




						def   lowercase_       ( self							)    ->			Any:
										lowerCAmelCase_       :					Union[str, Any]			 = ['''this is gunna be a long sentence ''' * 2_0]
										assert isinstance(src_text[0]      ,  __lowercase							)
										lowerCAmelCase_       :					str			 = 1_0
										lowerCAmelCase_       :					Tuple			 = self.tokenizer(__lowercase      ,  max_length=__lowercase      ,  truncation=__lowercase							).input_ids[0]
										self.assertEqual(ids[-2]      ,  2							)
										self.assertEqual(ids[-1]      ,  __lowercase							)
										self.assertEqual(len(__lowercase							)      ,  __lowercase							)




						def   lowercase_       ( self							)    ->			int:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']							)      ,  [2_5_0_0_2_6, 2_5_0_0_0_1]							)




						def   lowercase_       ( self							)    ->			Dict:
										lowerCAmelCase_       :					Any			 = tempfile.mkdtemp()
										lowerCAmelCase_       :					int			 = self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__lowercase							)
										lowerCAmelCase_       :					Optional[Any]			 = MBartTokenizer.from_pretrained(__lowercase							)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,  __lowercase							)




						@require_torch
						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					Tuple			 = self.tokenizer(self.src_text      ,  text_target=self.tgt_text      ,  padding=__lowercase      ,  return_tensors='''pt'''							)
										lowerCAmelCase_       :					Tuple			 = shift_tokens_right(batch['''labels''']      ,  self.tokenizer.pad_token_id							)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




						@require_torch
						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					str			 = self.tokenizer(
										    self.src_text      ,  text_target=self.tgt_text      ,  padding=__lowercase      ,  truncation=__lowercase      ,  max_length=len(self.expected_src_tokens							)      ,  return_tensors='''pt'''      ,  )

										lowerCAmelCase_       :					int			 = shift_tokens_right(batch['''labels''']      ,  self.tokenizer.pad_token_id							)

										self.assertIsInstance(__lowercase      ,  __lowercase							)

										self.assertEqual((2, 1_4)      ,  batch.input_ids.shape							)
										self.assertEqual((2, 1_4)      ,  batch.attention_mask.shape							)
										lowerCAmelCase_       :					str			 = batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens      ,  __lowercase							)
										self.assertEqual(2      ,  batch.decoder_input_ids[0, -1]							)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens      ,  []							)
										self.assertEqual(self.tokenizer.suffix_tokens      ,  [self.tokenizer.eos_token_id, EN_CODE]							)




						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					Optional[Any]			 = self.tokenizer(self.src_text      ,  padding=__lowercase      ,  truncation=__lowercase      ,  max_length=3      ,  return_tensors='''pt'''							)
										lowerCAmelCase_       :					Any			 = self.tokenizer(
										    text_target=self.tgt_text      ,  padding=__lowercase      ,  truncation=__lowercase      ,  max_length=1_0      ,  return_tensors='''pt'''							)
										lowerCAmelCase_       :					int			 = targets['''input_ids''']
										lowerCAmelCase_       :					Optional[Any]			 = shift_tokens_right(__lowercase      ,  self.tokenizer.pad_token_id							)

										self.assertEqual(batch.input_ids.shape[1]      ,  3							)
										self.assertEqual(batch.decoder_input_ids.shape[1]      ,  1_0							)




						@require_torch
						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Any			 = self.tokenizer._build_translation_inputs(
										    '''A test'''      ,  return_tensors='''pt'''      ,  src_lang='''en_XX'''      ,  tgt_lang='''ar_AR'''							)

										self.assertEqual(
										    nested_simplify(__lowercase							)      ,  {
										        # A, test, EOS, en_XX
										        '''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
										        '''attention_mask''': [[1, 1, 1, 1]],
										        # ar_AR
										        '''forced_bos_token_id''': 2_5_0_0_0_1,
										    }      ,  )