





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   ):
  __lowerCamelCase						=     WavaVecaPhonemeCTCTokenizer
  __lowerCamelCase						=     False







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_       ,				range(len(SCREAMING_SNAKE_CASE_   )   )   )   )

     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(SCREAMING_SNAKE_CASE_   ) + "\n"   )







  def     UpperCAmelCase	(     self    :Optional[Any]       ,				_lowercase    :int       ,				_lowercase    :List[str]=False       ,				_lowercase    :Union[str, Any]=20       ,				_lowercase    :Union[str, Any]=5   ):






     '''simple docstring'''





     lowercase__					  =    [(i, tokenizer.decode([i]       ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   )) for i in range(len(SCREAMING_SNAKE_CASE_   )   )]
     lowercase__					  =    list(filter(lambda _lowercase   : [t[0]] == tokenizer.encode(t[1]       ,				do_phonemize=SCREAMING_SNAKE_CASE_   )       ,				SCREAMING_SNAKE_CASE_   )   )
     if max_length is not None and len(SCREAMING_SNAKE_CASE_   ) > max_length:
        lowercase__					  =    toks[:max_length]
     if min_length is not None and len(SCREAMING_SNAKE_CASE_   ) < min_length and len(SCREAMING_SNAKE_CASE_   ) > 0:
        while len(SCREAMING_SNAKE_CASE_   ) < 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(SCREAMING_SNAKE_CASE_       ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   )
     if " " not in output_txt and len(SCREAMING_SNAKE_CASE_   ) > 1:
        lowercase__					  =    (
            tokenizer.decode([toks_ids[0]]       ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   )
            + " "
            + tokenizer.decode(toks_ids[1:]       ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   )
        )
     if with_prefix_space:
        lowercase__					  =    " " + output_txt
     lowercase__					  =    tokenizer.encode(SCREAMING_SNAKE_CASE_       ,				add_special_tokens=SCREAMING_SNAKE_CASE_   )
     return output_txt, output_ids







  def     UpperCAmelCase	(     self    :Tuple       ,				**_lowercase    :int   ):






     '''simple docstring'''





     kwargs.update(self.special_tokens_map   )
     return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname       ,				**SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :List[Any]   ):






     '''simple docstring'''





     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=SCREAMING_SNAKE_CASE_   ).input_ids
     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				[13, 3_92, 17]   )  # xxx should be last token

     tokenizer.add_tokens(["aaa", "bbb", "ccc"]   )
     lowercase__					  =    tokenizer("m aaa ɪ ccc"       ,				do_phonemize=SCREAMING_SNAKE_CASE_   ).input_ids
     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				[13, 3_93, 17, 3_95]   )  # aaa and ccc should be after xxx and 2 after aaa

     lowercase__					  =    tokenizer("maɪ c"       ,				do_phonemize=SCREAMING_SNAKE_CASE_   ).input_ids
     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				[3, 2_00]   )  # mai should be <unk> (=3)







  def     UpperCAmelCase	(     self    :Union[str, Any]   ):






     '''simple docstring'''





     lowercase__					  =    self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

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







  def     UpperCAmelCase	(     self    :str   ):






     '''simple docstring'''





     lowercase__					  =    self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

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







  def     UpperCAmelCase	(     self    :str   ):






     '''simple docstring'''





     lowercase__					  =    self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
     lowercase__					  =    "Hello how are you"
     lowercase__					  =    tokenizer.phonemize(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="en-us"   )

     lowercase__					  =    tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE_   ).input_ids   )

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :Optional[Any]   ):






     '''simple docstring'''





     lowercase__					  =    self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

     lowercase__					  =    [
         [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
         [24, 22, 5, 24, 22, 5, 77],
     ]
     lowercase__					  =    tokenizer.decode(sample_ids[0]   )
     lowercase__					  =    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_   )
     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				batch_tokens[0]   )
     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]   )







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="en-us"   )
     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				"h ə l oʊ | h aʊ | ɑːɹ | j uː |"   )







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="en-us"   )
     self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE_   ).input_ids       ,				tokenizer(SCREAMING_SNAKE_CASE_       ,				do_phonemize=SCREAMING_SNAKE_CASE_   ).input_ids   )







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





     lowercase__					  =    self.tokenizer_class.from_pretrained(
         "facebook/wav2vec2-lv-60-espeak-cv-ft"       ,				word_delimiter_token="|"   )
     tokenizer.add_tokens("|"   )

     # fmt: off
     lowercase__					  =    [
         [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
         [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
     ]
     # fmt: on

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







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="en-us"   )

     lowercase__					  =    tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE_   ).input_ids       ,				filter_word_delimiter_token=SCREAMING_SNAKE_CASE_   )

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="en-us"   )

     lowercase__					  =    tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE_   ).input_ids       ,				filter_word_delimiter_token=SCREAMING_SNAKE_CASE_   )

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







  def     UpperCAmelCase	(     self    :Optional[int]   ):






     '''simple docstring'''





     lowercase__					  =    self.tokenizer_class.from_pretrained(
         "facebook/wav2vec2-lv-60-espeak-cv-ft"       ,				word_delimiter_token=SCREAMING_SNAKE_CASE_   )
     lowercase__					  =    "Hello how are you"

     lowercase__					  =    tokenizer(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="en-us"   ).input_ids
     lowercase__					  =    tokenizer(SCREAMING_SNAKE_CASE_       ,				phonemizer_lang="fr-fr"   ).input_ids

     self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )

     lowercase__					  =    tokenizer.decode(SCREAMING_SNAKE_CASE_   )
     lowercase__					  =    tokenizer.decode(SCREAMING_SNAKE_CASE_   )

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







  def     UpperCAmelCase	(     self    :List[Any]   ):






     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_   ).input_ids
     lowercase__					  =    tokenizer(SCREAMING_SNAKE_CASE_   ).input_ids

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :Optional[int]   ):






     '''simple docstring'''





     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__					  =    [
         [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
         [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
     ]
     # fmt: on

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







  @staticmethod
  def     UpperCAmelCase	(     _lowercase    :int       ,				_lowercase    :Any   ):






     '''simple docstring'''





     lowercase__					  =    [d[key] for d in offsets]
     return retrieved_list







  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     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__					  =    [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
     # fmt: on

     lowercase__					  =    tokenizer.decode(SCREAMING_SNAKE_CASE_       ,				output_char_offsets=SCREAMING_SNAKE_CASE_       ,				filter_word_delimiter_token=SCREAMING_SNAKE_CASE_   )
     # 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(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )   )

     # 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, 11, 12, 15, 16]   )
     self.assertListEqual(
         self.get_from_offsets(outputs["char_offsets"]       ,				"end_offset"   )       ,				[1, 4, 6, 9, 10, 12, 15, 16, 17]   )







  def     UpperCAmelCase	(     self    :List[str]   ):






     '''simple docstring'''





     lowercase__					  =    self.get_tokenizer(word_delimiter_token="|"   )

     def check_list_tuples_equal(_lowercase    :int       ,				_lowercase    :List[str]   ):
        self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )   )
        self.assertTrue(isinstance(outputs_list[0]       ,				SCREAMING_SNAKE_CASE_   )   )

        # 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(_lowercase    :int       ,				_lowercase    :Tuple   ):
           if isinstance(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   ):
              [recursive_check(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   ) for la, la in zip(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )]
           self.assertEqual(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )

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

        # fmt: off
     lowercase__					  =    [
         [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
         [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
     ]
     # 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(SCREAMING_SNAKE_CASE_       ,				output_char_offsets=SCREAMING_SNAKE_CASE_   )
     lowercase__					  =    [tokenizer.decode(SCREAMING_SNAKE_CASE_       ,				output_char_offsets=SCREAMING_SNAKE_CASE_   ) for ids in sample_ids]
     check_list_tuples_equal(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_   )







  @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"   )
  def     UpperCAmelCase	(     self    :Tuple   ):






     '''simple docstring'''





     pass







  @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"   )
  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     pass







  @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"   )
  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     pass







  @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"   )
  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     pass







  def     UpperCAmelCase	(     self    :Optional[Any]   ):






     '''simple docstring'''





     lowercase__					  =    self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_   )
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''   ):
           lowercase__					  =    tokenizer.vocab_size
           lowercase__					  =    len(SCREAMING_SNAKE_CASE_   )

           self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,				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(SCREAMING_SNAKE_CASE_   )
           lowercase__					  =    tokenizer.vocab_size
           lowercase__					  =    len(SCREAMING_SNAKE_CASE_   )

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

           lowercase__					  =    tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"       ,				add_special_tokens=SCREAMING_SNAKE_CASE_   )

           self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE_   )       ,				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(SCREAMING_SNAKE_CASE_   )
           lowercase__					  =    tokenizer.vocab_size
           lowercase__					  =    len(SCREAMING_SNAKE_CASE_   )

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

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

           self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE_   )       ,				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     UpperCAmelCase	(     self    :Tuple   ):






     '''simple docstring'''





     pass







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






     '''simple docstring'''





     pass







  def     UpperCAmelCase	(     self    :Tuple   ):






     '''simple docstring'''





     lowercase__					  =    self.get_tokenizers(fast=SCREAMING_SNAKE_CASE_       ,				do_lower_case=SCREAMING_SNAKE_CASE_   )
     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(SCREAMING_SNAKE_CASE_   )

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








"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCAmelCase__				=					r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'





@add_start_docstrings(__lowercase )
class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''rag'''
							UpperCAmelCase__      =   True

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=" / "							,							SCREAMING_SNAKE_CASE_=" // "							,							SCREAMING_SNAKE_CASE_=5							,							SCREAMING_SNAKE_CASE_=3_00							,							SCREAMING_SNAKE_CASE_=7_68							,							SCREAMING_SNAKE_CASE_=8							,							SCREAMING_SNAKE_CASE_="wiki_dpr"							,							SCREAMING_SNAKE_CASE_="train"							,							SCREAMING_SNAKE_CASE_="compressed"							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0.0							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(
									    bos_token_id=SCREAMING_SNAKE_CASE_							,							pad_token_id=SCREAMING_SNAKE_CASE_							,							eos_token_id=SCREAMING_SNAKE_CASE_							,							decoder_start_token_id=SCREAMING_SNAKE_CASE_							,							forced_eos_token_id=SCREAMING_SNAKE_CASE_							,							is_encoder_decoder=SCREAMING_SNAKE_CASE_							,							prefix=SCREAMING_SNAKE_CASE_							,							vocab_size=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_							,							)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''question_encoder'''     )
									SCREAMING_SNAKE_CASE_    			=			question_encoder_config.pop('''model_type'''     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''generator'''     )
									SCREAMING_SNAKE_CASE_    			=			decoder_config.pop('''model_type'''     )

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			reduce_loss
									SCREAMING_SNAKE_CASE_    			=			label_smoothing
									SCREAMING_SNAKE_CASE_    			=			exclude_bos_score
									SCREAMING_SNAKE_CASE_    			=			do_marginalize

									SCREAMING_SNAKE_CASE_    			=			title_sep
									SCREAMING_SNAKE_CASE_    			=			doc_sep
									SCREAMING_SNAKE_CASE_    			=			n_docs
									SCREAMING_SNAKE_CASE_    			=			max_combined_length

									SCREAMING_SNAKE_CASE_    			=			dataset
									SCREAMING_SNAKE_CASE_    			=			dataset_split
									SCREAMING_SNAKE_CASE_    			=			index_name

									SCREAMING_SNAKE_CASE_    			=			retrieval_vector_size
									SCREAMING_SNAKE_CASE_    			=			retrieval_batch_size
									SCREAMING_SNAKE_CASE_    			=			passages_path
									SCREAMING_SNAKE_CASE_    			=			index_path
									SCREAMING_SNAKE_CASE_    			=			use_dummy_dataset

									SCREAMING_SNAKE_CASE_    			=			output_retrieved

									SCREAMING_SNAKE_CASE_    			=			do_deduplication

									SCREAMING_SNAKE_CASE_    			=			use_cache

									if self.forced_eos_token_id is None:
											SCREAMING_SNAKE_CASE_    			=			getattr(self.generator							,							'''forced_eos_token_id'''							,							SCREAMING_SNAKE_CASE_     )

							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return cls(question_encoder=question_encoder_config.to_dict()							,							generator=generator_config.to_dict()							,							**SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			copy.deepcopy(self.__dict__     )
									SCREAMING_SNAKE_CASE_    			=			self.question_encoder.to_dict()
									SCREAMING_SNAKE_CASE_    			=			self.generator.to_dict()
									SCREAMING_SNAKE_CASE_    			=			self.__class__.model_type
									return output