


"""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():
   __snake_case		=       get_tests_dir('''fixtures/test_sentencepiece.model''')


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

__snake_case		=       128022
__snake_case		=       128028


@require_sentencepiece
class     __lowerCamelCase  (							a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Any    =		MaMaaaTokenizer
     A_       :	int    =		False
     A_       :	List[Any]    =		False
     A_       :	int    =		True


     def 							_UpperCAmelCase      (      self							)       ->							int:
          super().setUp()

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

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


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							Union[str, Any]:
          return MaMaaaTokenizer.from_pretrained(self.tmpdirname    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          return (
              "This is a test",
              "This is a test",
          )


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				'''</s>'''
          _a    	=				0

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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.get_tokenizer()
          _a    	=				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(__UpperCAmelCase							)    ,				tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)


     @unittest.skip('''Skip this test while all models are still to be uploaded.'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.get_tokenizer()

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

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

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

          _a    	=				tokenizer.convert_tokens_to_string(__UpperCAmelCase							)
          self.assertEqual(__UpperCAmelCase    ,				'''This is a test'''							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          # fmt: off
          _a    	=				{'''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=__UpperCAmelCase    ,				model_name='''facebook/m2m100_418M'''    ,				revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''    ,				)







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

     '''simple docstring'''
     A_       :	Any    =		'facebook/m2m100_418M'
     A_       :	Optional[Any]    =		[
         'In my opinion, there are two levels of response from the French government.',
         'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
     ]
     A_       :	Optional[int]    =		[
         '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
     A_       :	Tuple    =		[EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]


     @classmethod
     def 							_UpperCAmelCase      (      cls							)       ->							Tuple:
          _a    	=				MaMaaaTokenizer.from_pretrained(
              cls.checkpoint_name    ,				src_lang='''en'''    ,				tgt_lang='''fr'''							)
          _a    	=				1
          return cls


     def 							_UpperCAmelCase      (      self							)       ->							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 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.tokenizer.get_vocab()
          self.assertEqual(len(__UpperCAmelCase							)    ,				self.tokenizer.vocab_size							)
          self.assertEqual(vocab['''<unk>''']    ,				3							)
          self.assertIn(self.tokenizer.get_lang_token('''en'''							)    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				'''en'''
          _a    	=				self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
          self.assertListEqual(self.expected_src_tokens    ,				__UpperCAmelCase							)


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


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				tempfile.mkdtemp()
          _a    	=				self.tokenizer.lang_token_to_id
          self.tokenizer.save_pretrained(__UpperCAmelCase							)
          _a    	=				MaMaaaTokenizer.from_pretrained(__UpperCAmelCase							)
          self.assertDictEqual(new_tok.lang_token_to_id    ,				__UpperCAmelCase							)


     @require_torch
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				'''en'''
          _a    	=				'''fr'''

          _a    	=				self.tokenizer(self.src_text    ,				text_target=self.tgt_text    ,				padding=__UpperCAmelCase    ,				return_tensors='''pt'''							)

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

          for k in batch:
               _a    	=				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 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				'''mr'''
          self.assertListEqual(self.tokenizer.prefix_tokens    ,				[self.tokenizer.get_lang_id('''mr'''							)]							)
          self.assertListEqual(self.tokenizer.suffix_tokens    ,				[self.tokenizer.eos_token_id]							)

          _a    	=				'''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 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				'''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							)]							)

          _a    	=				'''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 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.tokenizer._build_translation_inputs('''A test'''    ,				return_tensors='''pt'''    ,				src_lang='''en'''    ,				tgt_lang='''ar'''							)

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



"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		['image_processor', 'tokenizer']
     A_       :	Union[str, Any]    =		'ChineseCLIPImageProcessor'
     A_       :	Union[str, Any]    =		('BertTokenizer', 'BertTokenizerFast')


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:
          _a    	=				None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''    ,				__UpperCAmelCase    ,				)
               _a    	=				kwargs.pop('''feature_extractor'''							)

          _a    	=				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''							)
          if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''							)

          super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				self.image_processor


     def __call__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:

          if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''							)

          if text is not None:
               _a    	=				self.tokenizer(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if images is not None:
               _a    	=				self.image_processor(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if text is not None and images is not None:
               _a    	=				image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**__UpperCAmelCase							)    ,				tensor_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Optional[int]:
          return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.tokenizer.model_input_names
          _a    	=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)





     @property
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,				__UpperCAmelCase    ,				)
          return self.image_processor_class