
"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase_					:						Union[str, Any]				= 25_6047
UpperCamelCase_					:						List[str]				= 25_6145



@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




   """simple docstring"""







   snake_case	      =				NllbTokenizer
   snake_case	      =				NllbTokenizerFast
   snake_case	      =				True
   snake_case	      =				True
   snake_case	      =				{}




   def      lowerCamelCase__     (				self						:					int	)      -> Any:







        """simple docstring"""
        super().setUp()

        # We have a SentencePiece fixture for testing
        A_				 =						NllbTokenizer(_snake_case       ,   keep_accents=_snake_case	)
        tokenizer.save_pretrained(self.tmpdirname	)




   def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







        """simple docstring"""
        A_				 =						NllbTokenizer(_snake_case       ,   keep_accents=_snake_case	)

        A_				 =						tokenizer.tokenize("This is a test"	)
        self.assertListEqual(_snake_case       ,   ["▁This", "▁is", "▁a", "▁t", "est"]	)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_snake_case	)       ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,   )

        A_				 =						tokenizer.tokenize("I was born in 92000, and this is falsé."	)
        self.assertListEqual(
            _snake_case       ,   [
                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",
                ".",
            ]       ,   )
        A_				 =						tokenizer.convert_tokens_to_ids(_snake_case	)
        self.assertListEqual(
            _snake_case       ,   [
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]       ,   )

        A_				 =						tokenizer.convert_ids_to_tokens(_snake_case	)
        self.assertListEqual(
            _snake_case       ,   [
                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      lowerCamelCase__     (				self						:					Any	)      -> Dict:







        """simple docstring"""
        A_				 =						(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'	):
                  A_				 =						self.rust_tokenizer_class.from_pretrained(_snake_case       ,   **_snake_case	)
                  A_				 =						self.tokenizer_class.from_pretrained(_snake_case       ,   **_snake_case	)

                  A_				 =						tempfile.mkdtemp()

                  A_				 =						tokenizer_r.save_pretrained(_snake_case	)
                  A_				 =						tokenizer_p.save_pretrained(_snake_case	)

                  # 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	)	)
                  A_				 =						tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f	)
                  self.assertSequenceEqual(_snake_case       ,   _snake_case	)

                  # Checks everything loads correctly in the same way
                  A_				 =						tokenizer_r.from_pretrained(_snake_case	)
                  A_				 =						tokenizer_p.from_pretrained(_snake_case	)

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

                  shutil.rmtree(_snake_case	)

                  # Save tokenizer rust, legacy_format=True
                  A_				 =						tempfile.mkdtemp()

                  A_				 =						tokenizer_r.save_pretrained(_snake_case       ,   legacy_format=_snake_case	)
                  A_				 =						tokenizer_p.save_pretrained(_snake_case	)

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

                  # Checks everything loads correctly in the same way
                  A_				 =						tokenizer_r.from_pretrained(_snake_case	)
                  A_				 =						tokenizer_p.from_pretrained(_snake_case	)

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

                  shutil.rmtree(_snake_case	)

                  # Save tokenizer rust, legacy_format=False
                  A_				 =						tempfile.mkdtemp()

                  A_				 =						tokenizer_r.save_pretrained(_snake_case       ,   legacy_format=_snake_case	)
                  A_				 =						tokenizer_p.save_pretrained(_snake_case	)

                  # 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
                  A_				 =						tokenizer_r.from_pretrained(_snake_case	)
                  A_				 =						tokenizer_p.from_pretrained(_snake_case	)

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

                  shutil.rmtree(_snake_case	)




   @require_torch
   def      lowerCamelCase__     (				self						:					List[str]	)      -> Union[str, Any]:







        """simple docstring"""
        if not self.test_seqaseq:
             return

        A_				 =						self.get_tokenizers()
        for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'	):
                  # Longer text that will definitely require truncation.
                  A_				 =						[
                      " 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.",
                  ]
                  A_				 =						[
                      "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.",
                  ]
                  try:
                       A_				 =						tokenizer.prepare_seqaseq_batch(
                           src_texts=_snake_case       ,   tgt_texts=_snake_case       ,   max_length=3       ,   max_target_length=10       ,   return_tensors="pt"       ,   src_lang="eng_Latn"       ,   tgt_lang="ron_Latn"       ,   )
                  except NotImplementedError:
                       return
                  self.assertEqual(batch.input_ids.shape[1]       ,   3	)
                  self.assertEqual(batch.labels.shape[1]       ,   10	)
                  # max_target_length will default to max_length if not specified
                  A_				 =						tokenizer.prepare_seqaseq_batch(
                      _snake_case       ,   tgt_texts=_snake_case       ,   max_length=3       ,   return_tensors="pt"	)
                  self.assertEqual(batch.input_ids.shape[1]       ,   3	)
                  self.assertEqual(batch.labels.shape[1]       ,   3	)

                  A_				 =						tokenizer.prepare_seqaseq_batch(
                      src_texts=_snake_case       ,   max_length=3       ,   max_target_length=10       ,   return_tensors="pt"	)
                  self.assertEqual(batch_encoder_only.input_ids.shape[1]       ,   3	)
                  self.assertEqual(batch_encoder_only.attention_mask.shape[1]       ,   3	)
                  self.assertNotIn("decoder_input_ids"       ,   _snake_case	)




   @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."	)
   def      lowerCamelCase__     (				self						:					Any	)      -> List[Any]:







        """simple docstring"""
        pass






   def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







        """simple docstring"""
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'	):
                  A_				 =						[AddedToken("<special>"       ,   lstrip=_snake_case	)]

                  A_				 =						self.rust_tokenizer_class.from_pretrained(
                      _snake_case       ,   additional_special_tokens=_snake_case       ,   **_snake_case	)
                  A_				 =						tokenizer_r.encode("Hey this is a <special> token"	)

                  A_				 =						tokenizer_r.encode("<special>"       ,   add_special_tokens=_snake_case	)[0]

                  self.assertTrue(special_token_id in r_output	)

                  if self.test_slow_tokenizer:
                       A_				 =						self.rust_tokenizer_class.from_pretrained(
                           _snake_case       ,   additional_special_tokens=_snake_case       ,   **_snake_case       ,   )
                       A_				 =						self.tokenizer_class.from_pretrained(
                           _snake_case       ,   additional_special_tokens=_snake_case       ,   **_snake_case	)

                       A_				 =						tokenizer_p.encode("Hey this is a <special> token"	)

                       A_				 =						tokenizer_cr.encode("Hey this is a <special> token"	)

                       self.assertEqual(_snake_case       ,   _snake_case	)
                       self.assertEqual(_snake_case       ,   _snake_case	)
                       self.assertTrue(special_token_id in p_output	)
                       self.assertTrue(special_token_id in cr_output	)





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




   """simple docstring"""







   snake_case	      =				"facebook/nllb-200-distilled-600M"
   snake_case	      =				[
       " 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.",
   ]
   snake_case	      =				[
       "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.",
   ]
   snake_case	      =				[
       25_60_47,
       1_62_97,
       13_44_08,
       81_65,
       24_80_66,
       1_47_34,
       9_50,
       11_35,
       10_57_21,
       35_73,
       83,
       2_73_52,
       1_08,
       4_94_86,
       2,
   ]




   @classmethod
   def      lowerCamelCase__     (				cls						:					Optional[int]	)      -> int:







        """simple docstring"""
        A_				 =						NllbTokenizer.from_pretrained(
            cls.checkpoint_name       ,   src_lang="eng_Latn"       ,   tgt_lang="ron_Latn"	)
        A_				 =						1
        return cls




   def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[int]:







        """simple docstring"""
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]       ,   256_001	)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]       ,   256_002	)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]       ,   256_057	)




   def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







        """simple docstring"""
        A_				 =						self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
        self.assertListEqual(self.expected_src_tokens       ,   _snake_case	)




   def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







        """simple docstring"""
        self.assertIn(_snake_case       ,   self.tokenizer.all_special_ids	)
        # fmt: off
        A_				 =						[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
        # fmt: on

        A_				 =						self.tokenizer.decode(_snake_case       ,   skip_special_tokens=_snake_case	)
        A_				 =						self.tokenizer.decode(generated_ids[1:]       ,   skip_special_tokens=_snake_case	)
        self.assertEqual(_snake_case       ,   _snake_case	)
        self.assertNotIn(self.tokenizer.eos_token       ,   _snake_case	)




   def      lowerCamelCase__     (				self						:					int	)      -> Any:







        """simple docstring"""
        A_				 =						["this is gunna be a long sentence " * 20]
        assert isinstance(src_text[0]       ,   _snake_case	)
        A_				 =						10
        A_				 =						self.tokenizer(_snake_case       ,   max_length=_snake_case       ,   truncation=_snake_case	).input_ids[0]
        self.assertEqual(ids[-1]       ,   2	)
        self.assertEqual(ids[0]       ,   _snake_case	)
        self.assertEqual(len(_snake_case	)       ,   _snake_case	)




   def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







        """simple docstring"""
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]	)       ,   [256_203, 3]	)




   def      lowerCamelCase__     (				self						:					List[Any]	)      -> Tuple:







        """simple docstring"""
        A_				 =						tempfile.mkdtemp()
        A_				 =						self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(_snake_case	)
        A_				 =						NllbTokenizer.from_pretrained(_snake_case	)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,   _snake_case	)




   @require_torch
   def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







        """simple docstring"""
        A_				 =						self.tokenizer(
            self.src_text       ,   text_target=self.tgt_text       ,   padding=_snake_case       ,   truncation=_snake_case       ,   max_length=len(self.expected_src_tokens	)       ,   return_tensors="pt"       ,   )
        A_				 =						shift_tokens_right(
            batch["labels"]       ,   self.tokenizer.pad_token_id       ,   self.tokenizer.lang_code_to_id["ron_Latn"]	)

        self.assertIsInstance(_snake_case       ,   _snake_case	)

        self.assertEqual((2, 15)       ,   batch.input_ids.shape	)
        self.assertEqual((2, 15)       ,   batch.attention_mask.shape	)
        A_				 =						batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens       ,   _snake_case	)
        self.assertEqual(_snake_case       ,   batch.decoder_input_ids[0, 0]	)  # EOS
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens       ,   [EN_CODE]	)
        self.assertEqual(self.tokenizer.suffix_tokens       ,   [self.tokenizer.eos_token_id]	)




   def      lowerCamelCase__     (				self						:					Any	)      -> Any:







        """simple docstring"""
        A_				 =						self.tokenizer(self.src_text       ,   padding=_snake_case       ,   truncation=_snake_case       ,   max_length=3       ,   return_tensors="pt"	)
        A_				 =						self.tokenizer(
            text_target=self.tgt_text       ,   padding=_snake_case       ,   truncation=_snake_case       ,   max_length=10       ,   return_tensors="pt"	)
        A_				 =						targets["input_ids"]
        A_				 =						shift_tokens_right(
            _snake_case       ,   self.tokenizer.pad_token_id       ,   decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]       ,   )

        self.assertEqual(batch.input_ids.shape[1]       ,   3	)
        self.assertEqual(batch.decoder_input_ids.shape[1]       ,   10	)




   @require_torch
   def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







        """simple docstring"""
        A_				 =						self.tokenizer._build_translation_inputs(
            "A test"       ,   return_tensors="pt"       ,   src_lang="eng_Latn"       ,   tgt_lang="fra_Latn"	)

        self.assertEqual(
            nested_simplify(_snake_case	)       ,   {
                # A, test, EOS, en_XX
                "input_ids": [[256_047, 70, 7_356, 2]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 256_057,
            }       ,   )






   @require_torch
   def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







        """simple docstring"""
        A_				 =						True
        A_				 =						self.tokenizer(
            "UN Chief says there is no military solution in Syria"       ,   src_lang="eng_Latn"       ,   tgt_lang="fra_Latn"	)
        self.assertEqual(
            inputs.input_ids       ,   [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]	)

        A_				 =						False
        A_				 =						self.tokenizer(
            "UN Chief says there is no military solution in Syria"       ,   src_lang="eng_Latn"       ,   tgt_lang="fra_Latn"	)
        self.assertEqual(
            inputs.input_ids       ,   [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]	)






"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


UpperCamelCase_					:						Any				= logging.get_logger(__name__)

UpperCamelCase_					:						Any				= {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_					:						List[str]				= {
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_					:						Union[str, Any]				= {
    '''yjernite/retribert-base-uncased''': 512,
}


UpperCamelCase_					:						str				= {
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				PRETRAINED_INIT_CONFIGURATION
			snake_case	      =				RetriBertTokenizer
			snake_case	      =				["input_ids", "attention_mask"]




			def __init__(				self						:					Tuple       ,   _snake_case						:					int=None       ,   _snake_case						:					Any=None       ,   _snake_case						:					str=True       ,   _snake_case						:					List[Any]="[UNK]"       ,   _snake_case						:					Optional[Any]="[SEP]"       ,   _snake_case						:					str="[PAD]"       ,   _snake_case						:					List[str]="[CLS]"       ,   _snake_case						:					List[str]="[MASK]"       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Optional[int]=None       ,   **_snake_case						:					Tuple       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								super().__init__(
								    _snake_case       ,   tokenizer_file=_snake_case       ,   do_lower_case=_snake_case       ,   unk_token=_snake_case       ,   sep_token=_snake_case       ,   pad_token=_snake_case       ,   cls_token=_snake_case       ,   mask_token=_snake_case       ,   tokenize_chinese_chars=_snake_case       ,   strip_accents=_snake_case       ,   **_snake_case       ,   )

								A_				 =						json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    normalizer_state.get("lowercase"       ,   _snake_case	) != do_lower_case
								    or normalizer_state.get("strip_accents"       ,   _snake_case	) != strip_accents
								    or normalizer_state.get("handle_chinese_chars"       ,   _snake_case	) != tokenize_chinese_chars
								):
													A_				 =						getattr(_snake_case       ,   normalizer_state.pop("type"	)	)
													A_				 =						do_lower_case
													A_				 =						strip_accents
													A_				 =						tokenize_chinese_chars
													A_				 =						normalizer_class(**_snake_case	)

								A_				 =						do_lower_case




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Union[str, Any]=None	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

								return output




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								A_				 =						[self.sep_token_id]
								A_				 =						[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	) * [0] + len(token_ids_a + sep	) * [1]






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[str] = None	)      -> Tuple[str]:







								"""simple docstring"""
								A_				 =						self._tokenizer.model.save(_snake_case       ,   name=_snake_case	)
								return tuple(_snake_case	)




