





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


__lowerCamelCase :  int										=  get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__lowerCamelCase :  List[Any]										=  25_0004
__lowerCamelCase :  List[Any]										=  25_0020



@require_sentencepiece
@require_tokenizers
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:Dict							=     MBartTokenizer
      _UpperCAmelCase			:Tuple							=     MBartTokenizerFast
      _UpperCAmelCase			:str							=     True
      _UpperCAmelCase			:List[Any]							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase			:      List[str] 			=     MBartTokenizer(A_ ,							keep_accents=A_     )
       tokenizer.save_pretrained(self.tmpdirname     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     MBartTokenizer(A_ ,							keep_accents=A_     )

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

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

       UpperCamelCase			:      Tuple 			=     tokenizer.tokenize("I was born in 92000, and this is falsé."     )
       self.assertListEqual(
           A_ ,							[
               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",
               ".",
           ] ,							)
       UpperCamelCase			:      List[Any] 			=     tokenizer.convert_tokens_to_ids(A_     )
       self.assertListEqual(
           A_ ,							[
               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]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ] ,							)

       UpperCamelCase			:      Tuple 			=     tokenizer.convert_ids_to_tokens(A_     )
       self.assertListEqual(
           A_ ,							[
               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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       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

       UpperCamelCase			:      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})"""     ):
         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Any 			=     self.tokenizer_class.from_pretrained(A_ ,							**A_     )

         UpperCamelCase			:      Any 			=     tempfile.mkdtemp()

         UpperCamelCase			:      Any 			=     tokenizer_r.save_pretrained(A_     )
         UpperCamelCase			:      Any 			=     tokenizer_p.save_pretrained(A_     )

         # 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     )     )
         UpperCamelCase			:      Optional[Any] 			=     tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
         self.assertSequenceEqual(A_ ,							A_     )

         # Checks everything loads correctly in the same way
         UpperCamelCase			:      Union[str, Any] 			=     tokenizer_r.from_pretrained(A_     )
         UpperCamelCase			:      Any 			=     tokenizer_p.from_pretrained(A_     )

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

         shutil.rmtree(A_     )

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

         UpperCamelCase			:      int 			=     tokenizer_r.save_pretrained(A_ ,							legacy_format=A_     )
         UpperCamelCase			:      Tuple 			=     tokenizer_p.save_pretrained(A_     )

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

         # Checks everything loads correctly in the same way
         UpperCamelCase			:      int 			=     tokenizer_r.from_pretrained(A_     )
         UpperCamelCase			:      Dict 			=     tokenizer_p.from_pretrained(A_     )

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

         shutil.rmtree(A_     )

         # Save tokenizer rust, legacy_format=False
         UpperCamelCase			:      List[str] 			=     tempfile.mkdtemp()

         UpperCamelCase			:      Optional[Any] 			=     tokenizer_r.save_pretrained(A_ ,							legacy_format=A_     )
         UpperCamelCase			:      Any 			=     tokenizer_p.save_pretrained(A_     )

         # 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
         UpperCamelCase			:      str 			=     tokenizer_r.from_pretrained(A_     )
         UpperCamelCase			:      Optional[int] 			=     tokenizer_p.from_pretrained(A_     )

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

         shutil.rmtree(A_     )



@require_torch
@require_sentencepiece
@require_tokenizers
class       A__     ( unittest.TestCase    ):
      _UpperCAmelCase			:Union[str, Any]							=     'facebook/mbart-large-en-ro'
      _UpperCAmelCase			:List[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.',
      ]
      _UpperCAmelCase			:Dict							=     [
          '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.',
      ]
      _UpperCAmelCase			:Optional[int]							=     [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]





      @classmethod
      def  __UpperCamelCase(				cls     ):
       '''simple docstring'''




       UpperCamelCase			:      MBartTokenizer 			=     MBartTokenizer.from_pretrained(
           cls.checkpoint_name ,							src_lang="en_XX" ,							tgt_lang="ro_RO"     )
       UpperCamelCase			:      Any 			=     1
       return cls





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,							25_0001     )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,							25_0004     )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,							25_0020     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.assertIn(A_ ,							self.tokenizer.all_special_ids     )
       UpperCamelCase			:      List[Any] 			=     [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
       UpperCamelCase			:      Optional[Any] 			=     self.tokenizer.decode(A_ ,							skip_special_tokens=A_     )
       UpperCamelCase			:      str 			=     self.tokenizer.decode(generated_ids[1:] ,							skip_special_tokens=A_     )
       self.assertEqual(A_ ,							A_     )
       self.assertNotIn(self.tokenizer.eos_token ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     ["this is gunna be a long sentence " * 20]
       assert isinstance(src_text[0] ,							A_     )
       UpperCamelCase			:      Tuple 			=     10
       UpperCamelCase			:      Optional[int] 			=     self.tokenizer(A_ ,							max_length=A_ ,							truncation=A_     ).input_ids[0]
       self.assertEqual(ids[-2] ,							2     )
       self.assertEqual(ids[-1] ,							A_     )
       self.assertEqual(len(A_     ) ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     ) ,							[25_0026, 25_0001]     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     tempfile.mkdtemp()
       UpperCamelCase			:      Dict 			=     self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(A_     )
       UpperCamelCase			:      int 			=     MBartTokenizer.from_pretrained(A_     )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,							A_     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.tokenizer(self.src_text ,							text_target=self.tgt_text ,							padding=A_ ,							return_tensors="pt"     )
       UpperCamelCase			:      Dict 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.tokenizer(
           self.src_text ,							text_target=self.tgt_text ,							padding=A_ ,							truncation=A_ ,							max_length=len(self.expected_src_tokens     ) ,							return_tensors="pt" ,							)

       UpperCamelCase			:      Dict 			=     shift_tokens_right(batch["labels"] ,							self.tokenizer.pad_token_id     )

       self.assertIsInstance(A_ ,							A_     )

       self.assertEqual((2, 14) ,							batch.input_ids.shape     )
       self.assertEqual((2, 14) ,							batch.attention_mask.shape     )
       UpperCamelCase			:      List[Any] 			=     batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens ,							A_     )
       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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.tokenizer(self.src_text ,							padding=A_ ,							truncation=A_ ,							max_length=3 ,							return_tensors="pt"     )
       UpperCamelCase			:      List[Any] 			=     self.tokenizer(
           text_target=self.tgt_text ,							padding=A_ ,							truncation=A_ ,							max_length=10 ,							return_tensors="pt"     )
       UpperCamelCase			:      List[str] 			=     targets["input_ids"]
       UpperCamelCase			:      str 			=     shift_tokens_right(A_ ,							self.tokenizer.pad_token_id     )

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





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.tokenizer._build_translation_inputs(
           "A test" ,							return_tensors="pt" ,							src_lang="en_XX" ,							tgt_lang="ar_AR"     )

       self.assertEqual(
           nested_simplify(A_     ) ,							{
               # A, test, EOS, en_XX
               "input_ids": [[62, 3034, 2, 25_0004]],
               "attention_mask": [[1, 1, 1, 1]],
               # ar_AR
               "forced_bos_token_id": 25_0001,
           } ,							)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase :  Union[str, Any]										=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  List[str]										=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

    __lowerCamelCase :  str										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
