



"""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       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Any							  =     WavaVecaPhonemeCTCTokenizer
			A__	:					Dict							  =     False


			def   snake_case__	(      self		)      ->	List[str]:
				super().setUp()

				A__											=					(
				    "<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(" "		)
				A__											=					dict(zip(SCREAMING_SNAKE_CASE__						,	range(len(SCREAMING_SNAKE_CASE__		)		)		)		)

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

				A__											=					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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=20						,	SCREAMING_SNAKE_CASE__=5		)      ->	Tuple[str, list]:
				A__											=					[(i, tokenizer.decode([i]						,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__		)) for i in range(len(SCREAMING_SNAKE_CASE__		)		)]
				A__											=					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:
					A__											=					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:
						A__											=					toks + toks
        # toks_str = [t[1] for t in toks]
				A__											=					[t[0] for t in toks]

				# Ensure consistency
				A__											=					tokenizer.decode(SCREAMING_SNAKE_CASE__						,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__		)
				if " " not in output_txt and len(SCREAMING_SNAKE_CASE__		) > 1:
					A__											=					(
					    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:
					A__											=					" " + output_txt
				A__											=					tokenizer.encode(SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		)
				return output_txt, output_ids


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Any:
				kwargs.update(self.special_tokens_map		)
				return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"		)

				# check adding a single token
				tokenizer.add_tokens("xxx"		)
				A__											=					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"]		)
				A__											=					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

				A__											=					tokenizer("maɪ c"						,	do_phonemize=SCREAMING_SNAKE_CASE__		).input_ids
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[3, 200]		)  # mai should be <unk> (=3)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"		)

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


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

				A__											=					"Hello how are you"
				A__											=					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   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"		)
				A__											=					"Hello how are you"
				A__											=					tokenizer.phonemize(SCREAMING_SNAKE_CASE__						,	phonemizer_lang="en-us"		)

				A__											=					tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__		).input_ids		)

				self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


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

				A__											=					[
				    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
				    [24, 22, 5, 24, 22, 5, 77],
				]
				A__											=					tokenizer.decode(sample_ids[0]		)
				A__											=					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   snake_case__	(      self		)      ->	str:
				A__											=					self.tokenizer_class.from_pretrained(
				    "facebook/wav2vec2-lv-60-espeak-cv-ft"						,	word_delimiter_token="|"		)
				tokenizer.add_tokens("|"		)

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


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.tokenizer_class.from_pretrained(
				    "facebook/wav2vec2-lv-60-espeak-cv-ft"						,	word_delimiter_token="|"		)
				tokenizer.add_tokens("|"		)

				A__											=					"Hello how are you"
				A__											=					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   snake_case__	(      self		)      ->	str:
				A__											=					self.tokenizer_class.from_pretrained(
				    "facebook/wav2vec2-lv-60-espeak-cv-ft"						,	word_delimiter_token="|"		)
				tokenizer.add_tokens("|"		)

				# fmt: off
				A__											=					[
				    [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
				A__											=					tokenizer.decode(sample_ids[0]		)
				A__											=					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
				A__											=					tokenizer.decode(sample_ids[0]						,	filter_word_delimiter_token=SCREAMING_SNAKE_CASE__		)
				A__											=					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   snake_case__	(      self		)      ->	Dict:
				A__											=					self.tokenizer_class.from_pretrained(
				    "facebook/wav2vec2-lv-60-espeak-cv-ft"						,	word_delimiter_token="|"		)
				tokenizer.add_tokens("|"		)

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

				A__											=					tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__		).input_ids						,	filter_word_delimiter_token=SCREAMING_SNAKE_CASE__		)

				self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.tokenizer_class.from_pretrained(
				    "facebook/wav2vec2-lv-60-espeak-cv-ft"						,	word_delimiter_token="|"		)
				tokenizer.add_tokens("|"		)

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

				A__											=					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   snake_case__	(      self		)      ->	Any:
				A__											=					self.tokenizer_class.from_pretrained(
				    "facebook/wav2vec2-lv-60-espeak-cv-ft"						,	word_delimiter_token=SCREAMING_SNAKE_CASE__		)
				A__											=					"Hello how are you"

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	phonemizer_lang="en-us"		).input_ids
				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	phonemizer_lang="fr-fr"		).input_ids

				self.assertNotEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer.decode(SCREAMING_SNAKE_CASE__		)
				A__											=					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   snake_case__	(      self		)      ->	int:
				A__											=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"		)
				A__											=					"Hello how Are you"
				A__											=					"hello how are you"

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__		).input_ids
				A__											=					tokenizer(SCREAMING_SNAKE_CASE__		).input_ids

				self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"		)
				tokenizer.add_tokens(["!", "?"]		)
				tokenizer.add_special_tokens({"cls_token": "$$$"}		)

				# fmt: off
				A__											=					[
				    [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

				A__											=					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   snake_case__	(      SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					[d[key] for d in offsets]
				return retrieved_list


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					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"
				A__											=					[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

				A__											=					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   snake_case__	(      self		)      ->	List[Any]:
				A__											=					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
					A__											=					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
				A__											=					[
				    [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
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__						,	output_char_offsets=SCREAMING_SNAKE_CASE__		)
				A__											=					[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   snake_case__	(      self		)      ->	Union[str, Any]:
				pass


			@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"		)
			def   snake_case__	(      self		)      ->	Optional[Any]:
				pass


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


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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__		)
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
						A__											=					tokenizer.vocab_size
						A__											=					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)

						A__											=					["aaaaa bbbbbb", "cccccccccdddddddd"]
						A__											=					tokenizer.add_tokens(SCREAMING_SNAKE_CASE__		)
						A__											=					tokenizer.vocab_size
						A__											=					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__		)		)

						A__											=					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		)

						A__											=					{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
						A__											=					tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE__		)
						A__											=					tokenizer.vocab_size
						A__											=					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__		)		)

						A__											=					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   snake_case__	(      self		)      ->	str:
				pass


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







			def   snake_case__	(      self		)      ->	Optional[int]:
				# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
				# is not the case for Wav2Vec2PhonemeCTCTokenizer.
				A__											=					self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__						,	do_lower_case=SCREAMING_SNAKE_CASE__		)
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
						A__											=					["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
						A__											=					tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__		)

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









"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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

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

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

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

else:
			import sys

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




