


'''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


_a   :   Optional[int]												=   get_tests_dir("fixtures/test_sentencepiece.model")


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

_a   :   Union[str, Any]												=   256_047
_a   :   str												=   256_145




@require_sentencepiece
@require_tokenizers
class 			_lowercase (	__lowercase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Dict          =	NllbTokenizer
   _SCREAMING_SNAKE_CASE  :  Tuple          =	NllbTokenizerFast
   _SCREAMING_SNAKE_CASE  :  str          =	True
   _SCREAMING_SNAKE_CASE  :  Dict          =	True
   _SCREAMING_SNAKE_CASE  :  Any          =	{}
   def a  (						self       :      List[str]      ) ->							Union[str, Any]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __snake_case           =						NllbTokenizer(SCREAMING_SNAKE_CASE_       ,   keep_accents=SCREAMING_SNAKE_CASE_      )
        tokenizer.save_pretrained(self.tmpdirname      )
   def a  (						self       :      int      ) ->							int:
        __snake_case           =						NllbTokenizer(SCREAMING_SNAKE_CASE_       ,   keep_accents=SCREAMING_SNAKE_CASE_      )

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

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

        __snake_case           =						tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
        self.assertListEqual(
            SCREAMING_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',
                '.',
            ]       ,   )
        __snake_case           =						tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      )
        self.assertListEqual(
            SCREAMING_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]
            ]       ,   )

        __snake_case           =						tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      )
        self.assertListEqual(
            SCREAMING_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 a  (						self       :      List[str]      ) ->							Tuple:
        __snake_case           =						(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})'      ):
                  __snake_case           =						self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )

                  __snake_case           =						tempfile.mkdtemp()

                  __snake_case           =						tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						tokenizer_p.save_pretrained(SCREAMING_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      )      )
                  __snake_case           =						tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
                  self.assertSequenceEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

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

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

                  shutil.rmtree(SCREAMING_SNAKE_CASE_      )

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

                  __snake_case           =						tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_       ,   legacy_format=SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_      )

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

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

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

                  shutil.rmtree(SCREAMING_SNAKE_CASE_      )

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

                  __snake_case           =						tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_       ,   legacy_format=SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						tokenizer_p.save_pretrained(SCREAMING_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
                  __snake_case           =						tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_      )

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

                  shutil.rmtree(SCREAMING_SNAKE_CASE_      )
   @require_torch
   def a  (						self       :      Dict      ) ->							Optional[Any]:
        if not self.test_seqaseq:
             return

        __snake_case           =						self.get_tokenizers()
        for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'      ):
                  # Longer text that will definitely require truncation.
                  __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.',
                  ]
                  try:
                       __snake_case           =						tokenizer.prepare_seqaseq_batch(
                           src_texts=SCREAMING_SNAKE_CASE_       ,   tgt_texts=SCREAMING_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
                  __snake_case           =						tokenizer.prepare_seqaseq_batch(
                      SCREAMING_SNAKE_CASE_       ,   tgt_texts=SCREAMING_SNAKE_CASE_       ,   max_length=3       ,   return_tensors='pt'      )
                  self.assertEqual(batch.input_ids.shape[1]       ,   3      )
                  self.assertEqual(batch.labels.shape[1]       ,   3      )

                  __snake_case           =						tokenizer.prepare_seqaseq_batch(
                      src_texts=SCREAMING_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'       ,   SCREAMING_SNAKE_CASE_      )
   @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'      )
   def a  (						self       :      Any      ) ->							Union[str, Any]:
        pass





   def a  (						self       :      str      ) ->							Union[str, Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                  __snake_case           =						[AddedToken('<special>'       ,   lstrip=SCREAMING_SNAKE_CASE_      )]

                  __snake_case           =						self.rust_tokenizer_class.from_pretrained(
                      SCREAMING_SNAKE_CASE_       ,   additional_special_tokens=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						tokenizer_r.encode('Hey this is a <special> token'      )

                  __snake_case           =						tokenizer_r.encode('<special>'       ,   add_special_tokens=SCREAMING_SNAKE_CASE_      )[0]

                  self.assertTrue(special_token_id in r_output      )

                  if self.test_slow_tokenizer:
                       __snake_case           =						self.rust_tokenizer_class.from_pretrained(
                           SCREAMING_SNAKE_CASE_       ,   additional_special_tokens=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )
                       __snake_case           =						self.tokenizer_class.from_pretrained(
                           SCREAMING_SNAKE_CASE_       ,   additional_special_tokens=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )

                       __snake_case           =						tokenizer_p.encode('Hey this is a <special> token'      )

                       __snake_case           =						tokenizer_cr.encode('Hey this is a <special> token'      )

                       self.assertEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
                       self.assertEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_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 			_lowercase (	unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Union[str, Any]          =	"facebook/nllb-200-distilled-600M"
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	[
       " 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  :  List[str]          =	[
       2_5_6_0_4_7,
       1_6_2_9_7,
       1_3_4_4_0_8,
       8_1_6_5,
       2_4_8_0_6_6,
       1_4_7_3_4,
       9_5_0,
       1_1_3_5,
       1_0_5_7_2_1,
       3_5_7_3,
       8_3,
       2_7_3_5_2,
       1_0_8,
       4_9_4_8_6,
       2,
   ]
   @classmethod
   def a  (						cls       :      Union[str, Any]      ) ->							Union[str, Any]:
        __snake_case           =						NllbTokenizer.from_pretrained(
            cls.checkpoint_name       ,   src_lang='eng_Latn'       ,   tgt_lang='ron_Latn'      )
        __snake_case           =						1
        return cls
   def a  (						self       :      int      ) ->							str:
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']       ,   25_6001      )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']       ,   25_6002      )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']       ,   25_6057      )
   def a  (						self       :      Optional[int]      ) ->							int:
        __snake_case           =						self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
        self.assertListEqual(self.expected_src_tokens       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							Union[str, Any]:
        self.assertIn(SCREAMING_SNAKE_CASE_       ,   self.tokenizer.all_special_ids      )
        # fmt: off
        __snake_case           =						[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
        # fmt: on

        __snake_case           =						self.tokenizer.decode(SCREAMING_SNAKE_CASE_       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						self.tokenizer.decode(generated_ids[1:]       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_      )
        self.assertEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        self.assertNotIn(self.tokenizer.eos_token       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[Any]      ) ->							Union[str, Any]:
        __snake_case           =						['this is gunna be a long sentence ' * 20]
        assert isinstance(src_text[0]       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						10
        __snake_case           =						self.tokenizer(SCREAMING_SNAKE_CASE_       ,   max_length=SCREAMING_SNAKE_CASE_       ,   truncation=SCREAMING_SNAKE_CASE_      ).input_ids[0]
        self.assertEqual(ids[-1]       ,   2      )
        self.assertEqual(ids[0]       ,   SCREAMING_SNAKE_CASE_      )
        self.assertEqual(len(SCREAMING_SNAKE_CASE_      )       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Any      ) ->							str:
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )       ,   [25_6203, 3]      )
   def a  (						self       :      str      ) ->							Tuple:
        __snake_case           =						tempfile.mkdtemp()
        __snake_case           =						self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						NllbTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,   SCREAMING_SNAKE_CASE_      )
   @require_torch
   def a  (						self       :      str      ) ->							List[Any]:
        __snake_case           =						self.tokenizer(
            self.src_text       ,   text_target=self.tgt_text       ,   padding=SCREAMING_SNAKE_CASE_       ,   truncation=SCREAMING_SNAKE_CASE_       ,   max_length=len(self.expected_src_tokens      )       ,   return_tensors='pt'       ,   )
        __snake_case           =						shift_tokens_right(
            batch['labels']       ,   self.tokenizer.pad_token_id       ,   self.tokenizer.lang_code_to_id['ron_Latn']      )

        self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        self.assertEqual((2, 15)       ,   batch.input_ids.shape      )
        self.assertEqual((2, 15)       ,   batch.attention_mask.shape      )
        __snake_case           =						batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens       ,   SCREAMING_SNAKE_CASE_      )
        self.assertEqual(SCREAMING_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 a  (						self       :      int      ) ->							List[Any]:
        __snake_case           =						self.tokenizer(self.src_text       ,   padding=SCREAMING_SNAKE_CASE_       ,   truncation=SCREAMING_SNAKE_CASE_       ,   max_length=3       ,   return_tensors='pt'      )
        __snake_case           =						self.tokenizer(
            text_target=self.tgt_text       ,   padding=SCREAMING_SNAKE_CASE_       ,   truncation=SCREAMING_SNAKE_CASE_       ,   max_length=10       ,   return_tensors='pt'      )
        __snake_case           =						targets['input_ids']
        __snake_case           =						shift_tokens_right(
            SCREAMING_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 a  (						self       :      Union[str, Any]      ) ->							Any:
        __snake_case           =						self.tokenizer._build_translation_inputs(
            'A test'       ,   return_tensors='pt'       ,   src_lang='eng_Latn'       ,   tgt_lang='fra_Latn'      )

        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE_      )       ,   {
                # A, test, EOS, en_XX
                'input_ids': [[25_6047, 70, 7356, 2]],
                'attention_mask': [[1, 1, 1, 1]],
                # ar_AR
                'forced_bos_token_id': 25_6057,
            }       ,   )





   @require_torch
   def a  (						self       :      List[str]      ) ->							Union[str, Any]:
        __snake_case           =						True
        __snake_case           =						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       ,   [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]      )

        __snake_case           =						False
        __snake_case           =						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       ,   [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]      )








'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






