

"""simple docstring"""





import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin




@require_phonemizer
class  _lowerCAmelCase  (     lowercase					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Any       		=     WavaVecaPhonemeCTCTokenizer
 __UpperCAmelCase     : int       		=     False


 def  _lowercase			(      self							:      List[str]     ):
    super().setUp()

    __lowercase								=						(
        "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
        "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
        "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
        "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
        "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
        "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
        "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
        "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
        "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
        "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
        "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
        "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
        "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
    ).split(" "     )
    __lowercase								=						dict(zip(UpperCAmelCase__, range(len(UpperCAmelCase__     )     )     )     )

    __lowercase								=						{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

    __lowercase								=						os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]     )
    with open(self.vocab_file, "w", encoding="utf-8"     ) as fp:
       fp.write(json.dumps(UpperCAmelCase__     ) + "\n"     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      Dict=False, UpperCAmelCase__							:      Dict=2_0, UpperCAmelCase__							:      List[Any]=5     ):
    __lowercase								=						[(i, tokenizer.decode([i], clean_up_tokenization_spaces=UpperCAmelCase__     )) for i in range(len(UpperCAmelCase__     )     )]
    __lowercase								=						list(filter(lambda UpperCAmelCase__     : [t[0]] == tokenizer.encode(t[1], do_phonemize=UpperCAmelCase__     ), UpperCAmelCase__     )     )
    if max_length is not None and len(UpperCAmelCase__     ) > max_length:
       __lowercase								=						toks[:max_length]
    if min_length is not None and len(UpperCAmelCase__     ) < min_length and len(UpperCAmelCase__     ) > 0:
       while len(UpperCAmelCase__     ) < min_length:
          __lowercase								=						toks + toks
        # toks_str = [t[1] for t in toks]
    __lowercase								=						[t[0] for t in toks]

    # Ensure consistency
    __lowercase								=						tokenizer.decode(UpperCAmelCase__, clean_up_tokenization_spaces=UpperCAmelCase__     )
    if " " not in output_txt and len(UpperCAmelCase__     ) > 1:
       __lowercase								=						(
           tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=UpperCAmelCase__     )
           + " "
           + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=UpperCAmelCase__     )
       )
    if with_prefix_space:
       __lowercase								=						" " + output_txt
    __lowercase								=						tokenizer.encode(UpperCAmelCase__, add_special_tokens=UpperCAmelCase__     )
    return output_txt, output_ids


 def  _lowercase			(      self							:      Tuple, **UpperCAmelCase__							:      List[str]     ):
    kwargs.update(self.special_tokens_map     )
    return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

    # check adding a single token
    tokenizer.add_tokens("xxx"     )
    __lowercase								=						tokenizer("m xxx ɪ", do_phonemize=UpperCAmelCase__     ).input_ids
    self.assertEqual(UpperCAmelCase__, [1_3, 3_9_2, 1_7]     )  # xxx should be last token

    tokenizer.add_tokens(["aaa", "bbb", "ccc"]     )
    __lowercase								=						tokenizer("m aaa ɪ ccc", do_phonemize=UpperCAmelCase__     ).input_ids
    self.assertEqual(UpperCAmelCase__, [1_3, 3_9_3, 1_7, 3_9_5]     )  # aaa and ccc should be after xxx and 2 after aaa

    __lowercase								=						tokenizer("maɪ c", do_phonemize=UpperCAmelCase__     ).input_ids
    self.assertEqual(UpperCAmelCase__, [3, 2_0_0]     )  # mai should be <unk> (=3)


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )
    self.assertEqual(UpperCAmelCase__, "h ə l oʊ h aʊ ɑːɹ j uː"     )


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )
    self.assertEqual(tokenizer(UpperCAmelCase__     ).input_ids, tokenizer(UpperCAmelCase__, do_phonemize=UpperCAmelCase__     ).input_ids     )


 def  _lowercase			(      self							:      int     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )
    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )

    __lowercase								=						tokenizer.decode(tokenizer(UpperCAmelCase__     ).input_ids     )

    self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )

    __lowercase								=						[
        [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
        [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
    ]
    __lowercase								=						tokenizer.decode(sample_ids[0]     )
    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__     )
    self.assertEqual(UpperCAmelCase__, batch_tokens[0]     )
    self.assertEqual(UpperCAmelCase__, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]     )


 def  _lowercase			(      self							:      int     ):
    __lowercase								=						self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"     )
    tokenizer.add_tokens("|"     )

    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )
    self.assertEqual(UpperCAmelCase__, "h ə l oʊ | h aʊ | ɑːɹ | j uː |"     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"     )
    tokenizer.add_tokens("|"     )

    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )
    self.assertEqual(tokenizer(UpperCAmelCase__     ).input_ids, tokenizer(UpperCAmelCase__, do_phonemize=UpperCAmelCase__     ).input_ids     )


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"     )
    tokenizer.add_tokens("|"     )

    # fmt: off
    __lowercase								=						[
        [1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
        [tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
    ]
    # fmt: on

    # decode with word_del_token filter
    __lowercase								=						tokenizer.decode(sample_ids[0]     )
    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__     )
    self.assertEqual(UpperCAmelCase__, batch_tokens[0]     )
    self.assertEqual(UpperCAmelCase__, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]     )

    # decode with no word_del_token filter
    __lowercase								=						tokenizer.decode(sample_ids[0], filter_word_delimiter_token=UpperCAmelCase__     )
    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__, filter_word_delimiter_token=UpperCAmelCase__     )
    self.assertEqual(UpperCAmelCase__, batch_tokens[0]     )
    self.assertEqual(UpperCAmelCase__, ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"]     )


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"     )
    tokenizer.add_tokens("|"     )

    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )

    __lowercase								=						tokenizer.decode(tokenizer(UpperCAmelCase__     ).input_ids, filter_word_delimiter_token=UpperCAmelCase__     )

    self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"     )
    tokenizer.add_tokens("|"     )

    __lowercase								=						"Hello how are you"
    __lowercase								=						tokenizer.phonemize(UpperCAmelCase__, phonemizer_lang="en-us"     )

    __lowercase								=						tokenizer.decode(tokenizer(UpperCAmelCase__     ).input_ids, filter_word_delimiter_token=UpperCAmelCase__     )

    self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |"     )]     ).strip(), UpperCAmelCase__     )


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token=UpperCAmelCase__     )
    __lowercase								=						"Hello how are you"

    __lowercase								=						tokenizer(UpperCAmelCase__, phonemizer_lang="en-us"     ).input_ids
    __lowercase								=						tokenizer(UpperCAmelCase__, phonemizer_lang="fr-fr"     ).input_ids

    self.assertNotEqual(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						tokenizer.decode(UpperCAmelCase__     )
    __lowercase								=						tokenizer.decode(UpperCAmelCase__     )

    self.assertEqual(UpperCAmelCase__, "h ə l oʊ h aʊ ɑːɹ j uː"     )
    self.assertEqual(UpperCAmelCase__, "ɛ l o h aʊ a ʁ j u"     )


 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )
    __lowercase								=						"Hello how Are you"
    __lowercase								=						"hello how are you"

    __lowercase								=						tokenizer(UpperCAmelCase__     ).input_ids
    __lowercase								=						tokenizer(UpperCAmelCase__     ).input_ids

    self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )


 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"     )
    tokenizer.add_tokens(["!", "?"]     )
    tokenizer.add_special_tokens({"cls_token": "$$$"}     )

    # fmt: off
    __lowercase								=						[
        [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
        [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
    ]
    # fmt: on

    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__     )
    self.assertEqual(UpperCAmelCase__, ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]     )


 @staticmethod
 def  _lowercase			(      UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      str     ):
    __lowercase								=						[d[key] for d in offsets]
    return retrieved_list


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						self.get_tokenizer(word_delimiter_token="|"     )
    tokenizer.add_tokens("|"     )

    # fmt: off
    # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
    __lowercase								=						[1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
    # fmt: on

    __lowercase								=						tokenizer.decode(UpperCAmelCase__, output_char_offsets=UpperCAmelCase__, filter_word_delimiter_token=UpperCAmelCase__     )
    # check Wav2Vec2CTCTokenizerOutput keys for char
    self.assertEqual(len(outputs.keys()     ), 2     )
    self.assertTrue("text" in outputs     )
    self.assertTrue("char_offsets" in outputs     )
    self.assertTrue(isinstance(UpperCAmelCase__, UpperCAmelCase__     )     )

    # check that order of chars is correct and identical for both outputs
    self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"], "char"     )     ), outputs.text     )
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"], "char"     ), ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]     )

    # check that offsets are actually correct for char
    # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
    # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"], "start_offset"     ), [0, 1, 4, 7, 9, 1_1, 1_2, 1_5, 1_6]     )
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"], "end_offset"     ), [1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7]     )


 def  _lowercase			(      self							:      int     ):
    __lowercase								=						self.get_tokenizer(word_delimiter_token="|"     )

    def check_list_tuples_equal(UpperCAmelCase__							:      int, UpperCAmelCase__							:      Optional[int]     ):
       self.assertTrue(isinstance(UpperCAmelCase__, UpperCAmelCase__     )     )
       self.assertTrue(isinstance(outputs_list[0], UpperCAmelCase__     )     )

       # transform list to ModelOutput
       __lowercase								=						WavaVecaPhonemeCTCTokenizerOutput(
           {k: [d[k] for d in outputs_list] for k in outputs_list[0]}     )

       self.assertListEqual(outputs_batch["text"], outputs_batch_a["text"]     )

       def recursive_check(UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[Any]     ):
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             [recursive_check(UpperCAmelCase__, UpperCAmelCase__     ) for la, la in zip(UpperCAmelCase__, UpperCAmelCase__     )]
          self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )

       if "char_offsets" in outputs_batch:
          recursive_check(outputs_batch["char_offsets"], outputs_batch_a["char_offsets"]     )

        # fmt: off
    __lowercase								=						[
        [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
        [2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
    ]
    # fmt: on

    # We assume that `decode` works as expected. All we will check now is
    # the output type is correct and the output is identical to `decode`

    # char
    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__, output_char_offsets=UpperCAmelCase__     )
    __lowercase								=						[tokenizer.decode(UpperCAmelCase__, output_char_offsets=UpperCAmelCase__     ) for ids in sample_ids]
    check_list_tuples_equal(UpperCAmelCase__, UpperCAmelCase__     )


 @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"     )
 def  _lowercase			(      self							:      Any     ):
    pass


 @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"     )
 def  _lowercase			(      self							:      Union[str, Any]     ):
    pass


 @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"     )
 def  _lowercase			(      self							:      Union[str, Any]     ):
    pass


 @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"     )
 def  _lowercase			(      self							:      Tuple     ):
    pass


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.get_tokenizers(do_lower_case=UpperCAmelCase__     )
    for tokenizer in tokenizers:
       with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
          __lowercase								=						tokenizer.vocab_size
          __lowercase								=						len(UpperCAmelCase__     )

          self.assertNotEqual(UpperCAmelCase__, 0     )

          # We usually have added tokens from the start in tests because our vocab fixtures are
          # smaller than the original vocabs - let's not assert this
          # self.assertEqual(vocab_size, all_size)

          __lowercase								=						["aaaaa bbbbbb", "cccccccccdddddddd"]
          __lowercase								=						tokenizer.add_tokens(UpperCAmelCase__     )
          __lowercase								=						tokenizer.vocab_size
          __lowercase								=						len(UpperCAmelCase__     )

          self.assertNotEqual(UpperCAmelCase__, 0     )
          self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )
          self.assertEqual(UpperCAmelCase__, len(UpperCAmelCase__     )     )
          self.assertEqual(UpperCAmelCase__, all_size + len(UpperCAmelCase__     )     )

          __lowercase								=						tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=UpperCAmelCase__     )

          self.assertGreaterEqual(len(UpperCAmelCase__     ), 4     )
          self.assertGreater(tokens[0], tokenizer.vocab_size - 1     )
          self.assertGreater(tokens[-3], tokenizer.vocab_size - 1     )

          __lowercase								=						{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
          __lowercase								=						tokenizer.add_special_tokens(UpperCAmelCase__     )
          __lowercase								=						tokenizer.vocab_size
          __lowercase								=						len(UpperCAmelCase__     )

          self.assertNotEqual(UpperCAmelCase__, 0     )
          self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )
          self.assertEqual(UpperCAmelCase__, len(UpperCAmelCase__     )     )
          self.assertEqual(UpperCAmelCase__, all_size_a + len(UpperCAmelCase__     )     )

          __lowercase								=						tokenizer.encode(
              ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=UpperCAmelCase__     )

          self.assertGreaterEqual(len(UpperCAmelCase__     ), 6     )
          self.assertGreater(tokens[0], tokenizer.vocab_size - 1     )
          self.assertGreater(tokens[0], tokens[1]     )
          self.assertGreater(tokens[-3], tokenizer.vocab_size - 1     )
          self.assertGreater(tokens[-3], tokens[-4]     )
          self.assertEqual(tokens[0], tokenizer.eos_token_id     )
          self.assertEqual(tokens[-3], tokenizer.pad_token_id     )


 @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."     )
 def  _lowercase			(      self							:      str     ):
    pass


 @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."     )
 def  _lowercase			(      self							:      List[str]     ):
    pass



 def  _lowercase			(      self							:      Union[str, Any]     ):
    # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
    # is not the case for Wav2Vec2PhonemeCTCTokenizer.
    __lowercase								=						self.get_tokenizers(fast=UpperCAmelCase__, do_lower_case=UpperCAmelCase__     )
    for tokenizer in tokenizers:
       with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
          __lowercase								=						["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
          __lowercase								=						tokenizer.convert_tokens_to_string(UpperCAmelCase__     )

          self.assertIsInstance(output["text"], UpperCAmelCase__     )







"""simple docstring"""





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_a							=							'bert-base-cased'
_a							=							'google/pegasus-xsum'
_a							=							[' Sam ate lunch today.', 'Sams lunch ingredients.']
_a							=							['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_a							=							'patrickvonplaten/t5-tiny-random'
_a							=							'sshleifer/bart-tiny-random'
_a							=							'sshleifer/tiny-mbart'
_a							=							'sshleifer/tiny-marian-en-de'




def   _A  (  UpperCamelCase_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.source"""),      UpperCamelCase_)
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.target"""),      UpperCamelCase_)
   return tmp_dir




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __lowercase		,__lowercase								=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert isinstance(UpperCAmelCase__, UpperCAmelCase__     )
       assert batch["attention_mask"].shape == batch["input_ids"].shape
       # show that articles were trimmed.
       assert batch["input_ids"].shape[1] == max_src_len
       # show that targets are the same len
       assert batch["labels"].shape[1] == max_tgt_len
       if tok_name != MBART_TINY:
          continue
       # check language codes in correct place
       __lowercase								=						shift_tokens_right(batch["labels"], tokenizer.pad_token_id     )
       assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
       assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
       assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
       assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

       break  # No need to test every batch


 @parameterized.expand([BART_TINY, BERT_BASE_CASED]     )
 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert batch["attention_mask"].shape == batch["input_ids"].shape
       # show that articles were trimmed.
       assert batch["input_ids"].shape[1] == max_len_source
       assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
       # show that targets were truncated
       assert batch["labels"].shape[1] == trunc_target  # Truncated
       assert max_len_target > trunc_target  # Truncated
       break  # No need to test every batch


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"     )

    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    __lowercase								=						tmp_dir.joinpath("train.source"     ).open().readlines()
    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    pack_data_dir(UpperCAmelCase__, UpperCAmelCase__, 1_2_8, UpperCAmelCase__     )
    __lowercase								=						{x.name for x in tmp_dir.iterdir()}
    __lowercase								=						{x.name for x in save_dir.iterdir()}
    __lowercase								=						save_dir.joinpath("train.source"     ).open().readlines()
    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
    assert len(UpperCAmelCase__     ) < len(UpperCAmelCase__     )
    assert len(UpperCAmelCase__     ) == 1
    assert len(packed_examples[0]     ) == sum(len(UpperCAmelCase__     ) for x in orig_examples     )
    assert orig_paths == new_paths


 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq"     )
 def  _lowercase			(      self							:      List[str]     ):
    if not FAIRSEQ_AVAILABLE:
       return
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=6_4     )
    __lowercase								=						6_4
    __lowercase								=						ds.make_dynamic_sampler(UpperCAmelCase__, required_batch_size_multiple=UpperCAmelCase__     )
    __lowercase								=						[len(UpperCAmelCase__     ) for x in batch_sampler]
    assert len(set(UpperCAmelCase__     )     ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(UpperCAmelCase__     ) == len(UpperCAmelCase__     )  # no dropped or added examples
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_sampler=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						[]
    __lowercase								=						[]
    for batch in data_loader:
       __lowercase								=						batch["input_ids"].shape
       __lowercase								=						src_shape[0]
       assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
       __lowercase								=						np.product(batch["input_ids"].shape     )
       num_src_per_batch.append(UpperCAmelCase__     )
       if num_src_tokens > (max_tokens * 1.1):
          failures.append(UpperCAmelCase__     )
    assert num_src_per_batch[0] == max(UpperCAmelCase__     )
    if failures:
       raise AssertionError(F"""too many tokens in {len(UpperCAmelCase__     )} batches"""     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

    def count_pad_tokens(UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any]="input_ids"     ):
       return [batch[k].eq(UpperCAmelCase__     ).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(UpperCAmelCase__, k="labels"     )     ) < sum(count_pad_tokens(UpperCAmelCase__, k="labels"     )     )
    assert sum(count_pad_tokens(UpperCAmelCase__     )     ) < sum(count_pad_tokens(UpperCAmelCase__     )     )
    assert len(UpperCAmelCase__     ) == len(UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, n_obs=UpperCAmelCase__, )
    return ds, max_tokens, tokenizer


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=1, add_extra_examples=UpperCAmelCase__     )     )
    assert idsa.intersection(UpperCAmelCase__     ) == set()



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     ), type_path="train", max_source_length=4, max_target_length=8, src_lang="EN", tgt_lang="FR", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     ), type_path="train", max_source_length=4, max_target_length=8, )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
       assert len(UpperCAmelCase__     ) == 1 if tok_name == BART_TINY else len(UpperCAmelCase__     ) == 0




