





'''simple docstring'''


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
    from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
    a__			:					Any       						=						get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a__			:					Tuple       						=						128_022
a__			:					Tuple       						=						128_028






@require_sentencepiece
class     lowerCAmelCase__ (				UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=MaMaaaTokenizer
       _lowerCamelCase								=False
       _lowerCamelCase								=False
       _lowerCamelCase								=True

       def 			__snake_case      ( self					:				Any						):
              super().setUp()

              UpperCAmelCase  =						['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
              UpperCAmelCase  =						dict(zip(a__       ,     range(len(a__						)						)						)						)
              UpperCAmelCase  =						Path(self.tmpdirname						)
              save_json(a__       ,     save_dir / VOCAB_FILES_NAMES['''vocab_file''']						)
              if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                     copyfile(a__       ,     save_dir / VOCAB_FILES_NAMES['''spm_file''']						)

              UpperCAmelCase  =						MaMaaaTokenizer.from_pretrained(self.tmpdirname						)
              tokenizer.save_pretrained(self.tmpdirname						)

       def 			__snake_case      ( self					:				Optional[Any]       ,     **a__					:				Optional[Any]						):
              return MaMaaaTokenizer.from_pretrained(self.tmpdirname       ,     **a__						)

       def 			__snake_case      ( self					:				int       ,     a__					:				Optional[int]						):
              return (
                  "This is a test",
                  "This is a test",
              )

       def 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						'''</s>'''
              UpperCAmelCase  =						0

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__						)       ,     a__						)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__						)       ,     a__						)

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.get_tokenizer()
              UpperCAmelCase  =						list(tokenizer.get_vocab().keys()						)

              self.assertEqual(vocab_keys[0]       ,     '''</s>'''						)
              self.assertEqual(vocab_keys[1]       ,     '''<unk>'''						)
              self.assertEqual(vocab_keys[-1]       ,     '''<s>'''						)
              self.assertEqual(len(a__						)       ,     tokenizer.vocab_size + len(tokenizer.get_added_vocab()						)						)

       @unittest.skip('''Skip this test while all models are still to be uploaded.'''						)
       def 			__snake_case      ( self					:				List[Any]						):
              pass

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.get_tokenizer()

              UpperCAmelCase  =						tokenizer.tokenize('''This is a test'''						)
              self.assertListEqual(a__       ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(a__						)       ,     [2, 3, 4, 5, 6]       ,     )

              UpperCAmelCase  =						tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]						)
              self.assertListEqual(a__       ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

              UpperCAmelCase  =						tokenizer.convert_tokens_to_string(a__						)
              self.assertEqual(a__       ,     '''This is a test'''						)



       @slow
       def 			__snake_case      ( self					:				Union[str, Any]						):
              # fmt: off
              UpperCAmelCase  =						{'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
              # fmt: on

              self.tokenizer_integration_test_util(
                  expected_encoding=a__       ,     model_name='''facebook/m2m100_418M'''       ,     revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''       ,     )






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

       '''simple docstring'''
       _lowerCamelCase								="facebook/m2m100_418M"
       _lowerCamelCase								=[
           "In my opinion, there are two levels of response from the French government.",
           "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
       ]
       _lowerCamelCase								=[
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
       ]

       # fmt: off
       _lowerCamelCase								=[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]

       @classmethod
       def 			__snake_case      ( cls					:				Union[str, Any]						):
              UpperCAmelCase  =						MaMaaaTokenizer.from_pretrained(
                  cls.checkpoint_name       ,     src_lang='''en'''       ,     tgt_lang='''fr'''						)
              UpperCAmelCase  =						1
              return cls

       def 			__snake_case      ( self					:				List[str]						):
              self.assertEqual(self.tokenizer.get_lang_id('''ar'''						)       ,     128006						)
              self.assertEqual(self.tokenizer.get_lang_id('''en'''						)       ,     128022						)
              self.assertEqual(self.tokenizer.get_lang_id('''ro'''						)       ,     128076						)
              self.assertEqual(self.tokenizer.get_lang_id('''mr'''						)       ,     128063						)

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.tokenizer.get_vocab()
              self.assertEqual(len(a__						)       ,     self.tokenizer.vocab_size						)
              self.assertEqual(vocab['''<unk>''']       ,     3						)
              self.assertIn(self.tokenizer.get_lang_token('''en'''						)       ,     a__						)

       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						'''en'''
              UpperCAmelCase  =						self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
              self.assertListEqual(self.expected_src_tokens       ,     a__						)

       def 			__snake_case      ( self					:				List[str]						):
              self.assertIn(a__       ,     self.tokenizer.all_special_ids						)
              # fmt: off
              UpperCAmelCase  =						[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
              # fmt: on
              UpperCAmelCase  =						self.tokenizer.decode(a__       ,     skip_special_tokens=a__						)
              UpperCAmelCase  =						self.tokenizer.decode(generated_ids[1:]       ,     skip_special_tokens=a__						)
              self.assertEqual(a__       ,     a__						)
              self.assertNotIn(self.tokenizer.eos_token       ,     a__						)

       def 			__snake_case      ( self					:				Any						):
              UpperCAmelCase  =						tempfile.mkdtemp()
              UpperCAmelCase  =						self.tokenizer.lang_token_to_id
              self.tokenizer.save_pretrained(a__						)
              UpperCAmelCase  =						MaMaaaTokenizer.from_pretrained(a__						)
              self.assertDictEqual(new_tok.lang_token_to_id       ,     a__						)

       @require_torch
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						'''en'''
              UpperCAmelCase  =						'''fr'''

              UpperCAmelCase  =						self.tokenizer(self.src_text       ,     text_target=self.tgt_text       ,     padding=a__       ,     return_tensors='''pt'''						)

              UpperCAmelCase  =						shift_tokens_right(
                  batch['''labels''']       ,     self.tokenizer.pad_token_id       ,     self.tokenizer.eos_token_id						)

              for k in batch:
                     UpperCAmelCase  =						batch[k].tolist()
              # batch = {k: v.tolist() for k,v in batch.items()}
              # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
              # batch.decoder_inputs_ids[0][0] ==
              assert batch.input_ids[1][0] == EN_CODE
              assert batch.input_ids[1][-1] == 2
              assert batch.labels[1][0] == FR_CODE
              assert batch.labels[1][-1] == 2
              assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

       @require_torch
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						'''mr'''
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''mr'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)

              UpperCAmelCase  =						'''zh'''
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''zh'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)

       @require_torch
       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase  =						'''mr'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''mr'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang						)]						)

              UpperCAmelCase  =						'''zh'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''zh'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang						)]						)



       @require_torch
       def 			__snake_case      ( self					:				Optional[int]						):
              UpperCAmelCase  =						self.tokenizer._build_translation_inputs('''A test'''       ,     return_tensors='''pt'''       ,     src_lang='''en'''       ,     tgt_lang='''ar'''						)

              self.assertEqual(
                  nested_simplify(a__						)       ,     {
                      # en_XX, A, test, EOS
                      '''input_ids''': [[128022, 58, 4183, 2]],
                      '''attention_mask''': [[1, 1, 1, 1]],
                      # ar_AR
                      '''forced_bos_token_id''': 128006,
                  }       ,     )






import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met






class 	__lowercase					(	__snake_case	):





      def __init__(		self    :       Dict       ,							*__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]=None       ,							__lowerCamelCase    :       str=None       ,							**__lowerCamelCase    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(*__lowerCamelCase       ,							**__lowerCamelCase							)
         UpperCAmelCase      					=  eval_examples
         UpperCAmelCase      					=  post_process_function





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       int=None       ,							__lowerCamelCase    :       int=None       ,							__lowerCamelCase    :       Tuple=None       ,							__lowerCamelCase    :       str = "eval"							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.eval_dataset if eval_dataset is None else eval_dataset
         UpperCAmelCase      					=  self.get_eval_dataloader(__lowerCamelCase							)
         UpperCAmelCase      					=  self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         UpperCAmelCase      					=  self.compute_metrics
         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         UpperCAmelCase      					=  time.time()
         try:
            UpperCAmelCase      					=  eval_loop(
                __lowerCamelCase       ,							description="""Evaluation"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=__lowerCamelCase       ,							metric_key_prefix=__lowerCamelCase       ,							)
         finally:
            UpperCAmelCase      					=  compute_metrics
         UpperCAmelCase      					=  self.args.eval_batch_size * self.args.world_size
         if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
            start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
         output.metrics.update(
             speed_metrics(
                 __lowerCamelCase       ,							__lowerCamelCase       ,							num_samples=output.num_samples       ,							num_steps=math.ceil(output.num_samples / total_batch_size							)       ,							)							)
         if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            UpperCAmelCase      					=  self.post_process_function(__lowerCamelCase       ,							__lowerCamelCase       ,							output.predictions							)
            UpperCAmelCase      					=  self.compute_metrics(__lowerCamelCase							)

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()							):
               if not key.startswith(F"""{metric_key_prefix}_"""							):
                  UpperCAmelCase      					=  metrics.pop(__lowerCamelCase							)
            metrics.update(output.metrics							)
         else:
            UpperCAmelCase      					=  output.metrics

         if self.args.should_log:
            # Only the main node log the results by default
            self.log(__lowerCamelCase							)

         if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report()							)

         UpperCAmelCase      					=  self.callback_handler.on_evaluate(self.args       ,							self.state       ,							self.control       ,							__lowerCamelCase							)
         return metrics





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Dict=None       ,							__lowerCamelCase    :       str = "test"							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_test_dataloader(__lowerCamelCase							)

         # Temporarily disable metric computation, we will do it in the loop here.
         UpperCAmelCase      					=  self.compute_metrics
         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         UpperCAmelCase      					=  time.time()
         try:
            UpperCAmelCase      					=  eval_loop(
                __lowerCamelCase       ,							description="""Prediction"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=__lowerCamelCase       ,							metric_key_prefix=__lowerCamelCase       ,							)
         finally:
            UpperCAmelCase      					=  compute_metrics
         UpperCAmelCase      					=  self.args.eval_batch_size * self.args.world_size
         if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
            start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
         output.metrics.update(
             speed_metrics(
                 __lowerCamelCase       ,							__lowerCamelCase       ,							num_samples=output.num_samples       ,							num_steps=math.ceil(output.num_samples / total_batch_size							)       ,							)							)

         if self.post_process_function is None or self.compute_metrics is None:
            return output

         UpperCAmelCase      					=  self.post_process_function(__lowerCamelCase       ,							__lowerCamelCase       ,							output.predictions       ,							"""predict"""							)
         UpperCAmelCase      					=  self.compute_metrics(__lowerCamelCase							)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()							):
            if not key.startswith(F"""{metric_key_prefix}_"""							):
               UpperCAmelCase      					=  metrics.pop(__lowerCamelCase							)
         metrics.update(output.metrics							)
         return PredictionOutput(predictions=predictions.predictions       ,							label_ids=predictions.label_ids       ,							metrics=__lowerCamelCase							)



