

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  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      int       =  WavaVecaPhonemeCTCTokenizer
  SCREAMING_SNAKE_CASE    :      Tuple       =  False

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().setUp()

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     (
            '<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(' '					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     dict(zip(_SCREAMING_SNAKE_CASE ,					range(len(_SCREAMING_SNAKE_CASE					)					)					)					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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 ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=20 ,					_SCREAMING_SNAKE_CASE=5					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     [(i, tokenizer.decode([i] ,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE					)) for i in range(len(_SCREAMING_SNAKE_CASE					)					)]
        SCREAMING_SNAKE_CASE_				:  str           =     list(filter(lambda _SCREAMING_SNAKE_CASE					: [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:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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:
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     toks + toks
        # toks_str = [t[1] for t in toks]
        SCREAMING_SNAKE_CASE_				:  int           =     [t[0] for t in toks]

        # Ensure consistency
        SCREAMING_SNAKE_CASE_				:  int           =     tokenizer.decode(_SCREAMING_SNAKE_CASE ,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE					)
        if " " not in output_txt and len(_SCREAMING_SNAKE_CASE					) > 1:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     (
                  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:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     ' ' + output_txt
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
        return output_txt, output_ids

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

        # check adding a single token
        tokenizer.add_tokens('xxx'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer('m xxx ɪ' ,					do_phonemize=_SCREAMING_SNAKE_CASE					).input_ids
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					[13, 392, 17]					)  # xxx should be last token

        tokenizer.add_tokens(['aaa', 'bbb', 'ccc']					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer('m aaa ɪ ccc' ,					do_phonemize=_SCREAMING_SNAKE_CASE					).input_ids
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					[13, 393, 17, 395]					)  # aaa and ccc should be after xxx and 2 after aaa

        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer('maɪ c' ,					do_phonemize=_SCREAMING_SNAKE_CASE					).input_ids
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					[3, 200]					)  # mai should be <unk> (=3)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Any           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Any           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Any           =     tokenizer.phonemize(_SCREAMING_SNAKE_CASE ,					phonemizer_lang='en-us'					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.decode(tokenizer(_SCREAMING_SNAKE_CASE					).input_ids					)

        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [
            [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
            [24, 22, 5, 24, 22, 5, 77],
        ]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer.decode(sample_ids[0]					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     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					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Dict           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.tokenizer_class.from_pretrained(
            'facebook/wav2vec2-lv-60-espeak-cv-ft' ,					word_delimiter_token='|'					)
        tokenizer.add_tokens('|'					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Any           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.tokenizer_class.from_pretrained(
            'facebook/wav2vec2-lv-60-espeak-cv-ft' ,					word_delimiter_token='|'					)
        tokenizer.add_tokens('|'					)

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [
            [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
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.decode(sample_ids[0]					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer.decode(sample_ids[0] ,					filter_word_delimiter_token=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer_class.from_pretrained(
            'facebook/wav2vec2-lv-60-espeak-cv-ft' ,					word_delimiter_token='|'					)
        tokenizer.add_tokens('|'					)

        SCREAMING_SNAKE_CASE_				:  Any           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.phonemize(_SCREAMING_SNAKE_CASE ,					phonemizer_lang='en-us'					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.tokenizer_class.from_pretrained(
            'facebook/wav2vec2-lv-60-espeak-cv-ft' ,					word_delimiter_token='|'					)
        tokenizer.add_tokens('|'					)

        SCREAMING_SNAKE_CASE_				:  Any           =     'Hello how are you'
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer.phonemize(_SCREAMING_SNAKE_CASE ,					phonemizer_lang='en-us'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.tokenizer_class.from_pretrained(
            'facebook/wav2vec2-lv-60-espeak-cv-ft' ,					word_delimiter_token=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     'Hello how are you'

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer(_SCREAMING_SNAKE_CASE ,					phonemizer_lang='en-us'					).input_ids
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer(_SCREAMING_SNAKE_CASE ,					phonemizer_lang='fr-fr'					).input_ids

        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'Hello how Are you'
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'hello how are you'

        SCREAMING_SNAKE_CASE_				:  Any           =     tokenizer(_SCREAMING_SNAKE_CASE					).input_ids
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer(_SCREAMING_SNAKE_CASE					).input_ids

        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'					)
        tokenizer.add_tokens(['!', '?']					)
        tokenizer.add_special_tokens({'cls_token': '$$$'}					)

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [
            [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
            [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
        ]
        # fmt: on

        SCREAMING_SNAKE_CASE_				:  str           =     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    (				_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [d[key] for d in offsets]
        return retrieved_list

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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"
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [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

        SCREAMING_SNAKE_CASE_				:  List[str]           =     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					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_tokenizer(word_delimiter_token='|'					)

        def check_list_tuples_equal(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
              self.assertTrue(isinstance(outputs_list[0] ,					_SCREAMING_SNAKE_CASE					)					)

              # transform list to ModelOutput
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    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
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [
            [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
        SCREAMING_SNAKE_CASE_				:  int           =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,					output_char_offsets=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [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					):





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE					)
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"					):
                    SCREAMING_SNAKE_CASE_				:  int           =     tokenizer.vocab_size
                    SCREAMING_SNAKE_CASE_				:  Dict           =     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)

                    SCREAMING_SNAKE_CASE_				:  Tuple           =     ['aaaaa bbbbbb', 'cccccccccdddddddd']
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.add_tokens(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer.vocab_size
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     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					)					)

                    SCREAMING_SNAKE_CASE_				:  List[str]           =     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					)

                    SCREAMING_SNAKE_CASE_				:  Dict           =     {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.add_special_tokens(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer.vocab_size
                    SCREAMING_SNAKE_CASE_				:  Any           =     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					)					)

                    SCREAMING_SNAKE_CASE_				:  Any           =     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					):





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE ,					do_lower_case=_SCREAMING_SNAKE_CASE					)
        for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"					):
                    SCREAMING_SNAKE_CASE_				:  Dict           =     ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE					)

                    self.assertIsInstance(output['text'] ,					_SCREAMING_SNAKE_CASE					)









import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     UNetaDModel(
            sample_size=(32, 64) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     UNetaDConditionModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,					cross_attention_dim=10 ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoencoderKL(
            sample_size=(128, 64) ,					in_channels=1 ,					out_channels=1 ,					latent_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     UNetaDModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return vqvae, unet



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Any           =     Mel(
            x_res=self.dummy_unet.config.sample_size[1] ,					y_res=self.dummy_unet.config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_				:  str           =     AudioDiffusionPipeline(vqvae=_SCREAMING_SNAKE_CASE ,					unet=self.dummy_unet ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4 ,					return_dict=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     output[0][0]

        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
        assert (
            image.height == self.dummy_unet.config.sample_size[0]
            and image.width == self.dummy_unet.config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image_from_tuple.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     Mel(
            x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  str           =     DDIMScheduler()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_vqvae_and_unet
        SCREAMING_SNAKE_CASE_				:  int           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=dummy_vqvae_and_unet[1] ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.random.uniform(-1 ,					1 ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     pipe(raw_audio=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					start_step=5 ,					steps=10					)
        SCREAMING_SNAKE_CASE_				:  int           =     output.images[0]

        assert (
            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  int           =     np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_unet_condition
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=_SCREAMING_SNAKE_CASE ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.rand((1, 1, 10)					)
        SCREAMING_SNAKE_CASE_				:  Any           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					encoding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

@slow
@require_torch_gpu
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch_device

        SCREAMING_SNAKE_CASE_				:  str           =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     output.images[0]

        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0






