
'''simple docstring'''
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_											=						get_tests_dir("fixtures/test_sentencepiece.model")

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

a_											=						25_0004
a_											=						25_0020






@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						MBartaaTokenizer
 UpperCAmelCase_		    =						MBartaaTokenizerFast
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						True





 def        snake_case__		(    self):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_ :    Dict     			=						MBartaaTokenizer(lowercase_ ,       src_lang="en_XX" ,       tgt_lang="ro_RO" ,       keep_accents=lowercase_)
     tokenizer.save_pretrained(self.tmpdirname)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"<s>"
     snake_case_ :    Any     			=						0

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





 def        snake_case__		(    self):
     snake_case_ :    int     			=						list(self.get_tokenizer().get_vocab().keys())

     self.assertEqual(vocab_keys[0] ,       "<s>")
     self.assertEqual(vocab_keys[1] ,       "<pad>")
     self.assertEqual(vocab_keys[-1] ,       "<mask>")
     self.assertEqual(len(lowercase_) ,       10_54)





 def        snake_case__		(    self):
     self.assertEqual(self.get_tokenizer().vocab_size ,       10_54)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						MBartaaTokenizer(lowercase_ ,       src_lang="en_XX" ,       tgt_lang="ro_RO" ,       keep_accents=lowercase_)

     snake_case_ :    List[str]     			=						tokenizer.tokenize("This is a test")
     self.assertListEqual(lowercase_ ,       ["▁This", "▁is", "▁a", "▁t", "est"])

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_) ,       [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,       )

     snake_case_ :    List[str]     			=						tokenizer.tokenize("I was born in 92000, and this is falsé.")
     self.assertListEqual(
         lowercase_ ,       [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_ :    Tuple     			=						tokenizer.convert_tokens_to_ids(lowercase_)
     self.assertListEqual(
         lowercase_ ,       [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ] ,       )

     snake_case_ :    List[str]     			=						tokenizer.convert_ids_to_tokens(lowercase_)
     self.assertListEqual(
         lowercase_ ,       [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>", "."] ,       )





 @slow
 def        snake_case__		(    self):
     # fmt: off
     snake_case_ :    int     			=						{"input_ids": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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]], "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, 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], [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]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_ ,       model_name="facebook/mbart-large-50" ,       revision="d3913889c59cd5c9e456b269c376325eabad57e2" ,       )





 def        snake_case__		(    self):
     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

     snake_case_ :    Union[str, Any]     			=						(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
             snake_case_ :    Dict     			=						self.rust_tokenizer_class.from_pretrained(lowercase_ ,       **lowercase_)
             snake_case_ :    Tuple     			=						self.tokenizer_class.from_pretrained(lowercase_ ,       **lowercase_)

             snake_case_ :    List[Any]     			=						tempfile.mkdtemp()

             snake_case_ :    Optional[int]     			=						tokenizer_r.save_pretrained(lowercase_)
             snake_case_ :    Optional[Any]     			=						tokenizer_p.save_pretrained(lowercase_)

             # 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_ :    Any     			=						tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
             self.assertSequenceEqual(lowercase_ ,       lowercase_)

             # Checks everything loads correctly in the same way
             snake_case_ :    List[Any]     			=						tokenizer_r.from_pretrained(lowercase_)
             snake_case_ :    str     			=						tokenizer_p.from_pretrained(lowercase_)

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

             shutil.rmtree(lowercase_)

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

             snake_case_ :    List[Any]     			=						tokenizer_r.save_pretrained(lowercase_ ,       legacy_format=lowercase_)
             snake_case_ :    List[Any]     			=						tokenizer_p.save_pretrained(lowercase_)

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

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

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

             shutil.rmtree(lowercase_)

             # Save tokenizer rust, legacy_format=False
             snake_case_ :    Union[str, Any]     			=						tempfile.mkdtemp()

             snake_case_ :    Optional[Any]     			=						tokenizer_r.save_pretrained(lowercase_ ,       legacy_format=lowercase_)
             snake_case_ :    List[str]     			=						tokenizer_p.save_pretrained(lowercase_)

             # 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_ :    Tuple     			=						tokenizer_r.from_pretrained(lowercase_)
             snake_case_ :    List[str]     			=						tokenizer_p.from_pretrained(lowercase_)

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

             shutil.rmtree(lowercase_)






@require_torch
@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase_				(	unittest.TestCase					):
 UpperCAmelCase_		    =						"""facebook/mbart-large-50-one-to-many-mmt"""
 UpperCAmelCase_		    =						[
     """ 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_		    =						[
     """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_		    =						[EN_CODE, 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]





 @classmethod
 def        snake_case__		(    cls):
     snake_case_ :    MBartaaTokenizer     			=						MBartaaTokenizer.from_pretrained(
         cls.checkpoint_name ,       src_lang="en_XX" ,       tgt_lang="ro_RO")
     snake_case_ :    Dict     			=						1
     return cls





 def        snake_case__		(    self):
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,       25_00_01)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,       25_00_04)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,       25_00_20)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"] ,       25_00_38)





 def        snake_case__		(    self):
     snake_case_ :    int     			=						self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
     self.assertListEqual(self.expected_src_tokens ,       lowercase_)





 def        snake_case__		(    self):
     self.assertIn(lowercase_ ,       self.tokenizer.all_special_ids)
     snake_case_ :    List[str]     			=						[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
     snake_case_ :    List[Any]     			=						self.tokenizer.decode(lowercase_ ,       skip_special_tokens=lowercase_)
     snake_case_ :    Optional[Any]     			=						self.tokenizer.decode(generated_ids[1:] ,       skip_special_tokens=lowercase_)
     self.assertEqual(lowercase_ ,       lowercase_)
     self.assertNotIn(self.tokenizer.eos_token ,       lowercase_)





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





 def        snake_case__		(    self):
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]) ,       [25_00_53, 25_00_01])





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						tempfile.mkdtemp()
     snake_case_ :    List[str]     			=						self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(lowercase_)
     snake_case_ :    int     			=						MBartaaTokenizer.from_pretrained(lowercase_)
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,       lowercase_)





 @require_torch
 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.tokenizer(self.src_text ,       text_target=self.tgt_text ,       padding=lowercase_ ,       return_tensors="pt")
     snake_case_ :    Optional[int]     			=						shift_tokens_right(batch["labels"] ,       self.tokenizer.pad_token_id)

     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == RO_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]





 @require_torch
 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self.tokenizer(
         self.src_text ,       text_target=self.tgt_text ,       padding=lowercase_ ,       truncation=lowercase_ ,       max_length=len(self.expected_src_tokens) ,       return_tensors="pt" ,       )
     snake_case_ :    Tuple     			=						shift_tokens_right(batch["labels"] ,       self.tokenizer.pad_token_id)

     self.assertIsInstance(lowercase_ ,       lowercase_)

     self.assertEqual((2, 14) ,       batch.input_ids.shape)
     self.assertEqual((2, 14) ,       batch.attention_mask.shape)
     snake_case_ :    Optional[int]     			=						batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens ,       lowercase_)
     self.assertEqual(2 ,       batch.decoder_input_ids[0, 0])  # decoder_start_token_id
     # 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        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer(self.src_text ,       padding=lowercase_ ,       truncation=lowercase_ ,       max_length=3 ,       return_tensors="pt")
     snake_case_ :    List[Any]     			=						self.tokenizer(
         text_target=self.tgt_text ,       padding=lowercase_ ,       truncation=lowercase_ ,       max_length=10 ,       return_tensors="pt")
     snake_case_ :    str     			=						targets["input_ids"]
     snake_case_ :    List[Any]     			=						shift_tokens_right(lowercase_ ,       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        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer._build_translation_inputs(
         "A test" ,       return_tensors="pt" ,       src_lang="en_XX" ,       tgt_lang="ar_AR")

     self.assertEqual(
         nested_simplify(lowercase_) ,       {
             # en_XX, A, test, EOS
             "input_ids": [[25_00_04, 62, 30_34, 2]],
             "attention_mask": [[1, 1, 1, 1]],
             # ar_AR
             "forced_bos_token_id": 25_00_01,
         } ,       )






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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




