




"""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'''



			lowerCamelCase__   =    WavaVecaPhonemeCTCTokenizer
			lowerCamelCase__   =    False
			def       A_					(						self						):
							super().setUp()

							_lowerCamelCase  :    List[str]					=					(
							    '<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(' '						)
							_lowerCamelCase  :    List[str]					=					dict(zip(lowercase						,   range(len(lowercase						)						)						)						)

							_lowerCamelCase  :    int					=					{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

							_lowerCamelCase  :    Union[str, Any]					=					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(lowercase						) + '\n'						)
			def       A_					(						self						,   lowercase						,   lowercase=False						,   lowercase=20						,   lowercase=5						):
							_lowerCamelCase  :    List[Any]					=					[(i, tokenizer.decode([i]						,   clean_up_tokenization_spaces=lowercase						)) for i in range(len(lowercase						)						)]
							_lowerCamelCase  :    str					=					list(filter(lambda lowercase						: [t[0]] == tokenizer.encode(t[1]						,   do_phonemize=lowercase						)						,   lowercase						)						)
							if max_length is not None and len(lowercase						) > max_length:
											_lowerCamelCase  :    List[Any]					=					toks[:max_length]
							if min_length is not None and len(lowercase						) < min_length and len(lowercase						) > 0:
											while len(lowercase						) < min_length:
															_lowerCamelCase  :    Any					=					toks + toks
        # toks_str = [t[1] for t in toks]
							_lowerCamelCase  :    Dict					=					[t[0] for t in toks]

							# Ensure consistency
							_lowerCamelCase  :    str					=					tokenizer.decode(lowercase						,   clean_up_tokenization_spaces=lowercase						)
							if " " not in output_txt and len(lowercase						) > 1:
											_lowerCamelCase  :    Dict					=					(
											    tokenizer.decode([toks_ids[0]]						,   clean_up_tokenization_spaces=lowercase						)
											    + ' '
											    + tokenizer.decode(toks_ids[1:]						,   clean_up_tokenization_spaces=lowercase						)
											)
							if with_prefix_space:
											_lowerCamelCase  :    Any					=					' ' + output_txt
							_lowerCamelCase  :    List[str]					=					tokenizer.encode(lowercase						,   add_special_tokens=lowercase						)
							return output_txt, output_ids
			def       A_					(						self						,   **lowercase						):
							kwargs.update(self.special_tokens_map						)
							return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname						,   **lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)

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

							tokenizer.add_tokens(['aaa', 'bbb', 'ccc']						)
							_lowerCamelCase  :    Union[str, Any]					=					tokenizer('m aaa ɪ ccc'						,   do_phonemize=lowercase						).input_ids
							self.assertEqual(lowercase						,   [13, 393, 17, 395]						)  # aaa and ccc should be after xxx and 2 after aaa

							_lowerCamelCase  :    List[str]					=					tokenizer('maɪ c'						,   do_phonemize=lowercase						).input_ids
							self.assertEqual(lowercase						,   [3, 200]						)  # mai should be <unk> (=3)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)

							_lowerCamelCase  :    int					=					'Hello how are you'
							_lowerCamelCase  :    Union[str, Any]					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)
							self.assertEqual(lowercase						,   'h ə l oʊ h aʊ ɑːɹ j uː'						)
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)

							_lowerCamelCase  :    List[str]					=					'Hello how are you'
							_lowerCamelCase  :    Any					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)
							self.assertEqual(tokenizer(lowercase						).input_ids						,   tokenizer(lowercase						,   do_phonemize=lowercase						).input_ids						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)
							_lowerCamelCase  :    Optional[int]					=					'Hello how are you'
							_lowerCamelCase  :    Optional[Any]					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)

							_lowerCamelCase  :    int					=					tokenizer.decode(tokenizer(lowercase						).input_ids						)

							self.assertEqual(lowercase						,   lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)

							_lowerCamelCase  :    Optional[int]					=					[
							    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
							    [24, 22, 5, 24, 22, 5, 77],
							]
							_lowerCamelCase  :    str					=					tokenizer.decode(sample_ids[0]						)
							_lowerCamelCase  :    List[str]					=					tokenizer.batch_decode(lowercase						)
							self.assertEqual(lowercase						,   batch_tokens[0]						)
							self.assertEqual(lowercase						,   ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']						)
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					self.tokenizer_class.from_pretrained(
							    'facebook/wav2vec2-lv-60-espeak-cv-ft'						,   word_delimiter_token='|'						)
							tokenizer.add_tokens('|'						)

							_lowerCamelCase  :    List[str]					=					'Hello how are you'
							_lowerCamelCase  :    str					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)
							self.assertEqual(lowercase						,   'h ə l oʊ | h aʊ | ɑːɹ | j uː |'						)
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					self.tokenizer_class.from_pretrained(
							    'facebook/wav2vec2-lv-60-espeak-cv-ft'						,   word_delimiter_token='|'						)
							tokenizer.add_tokens('|'						)

							_lowerCamelCase  :    Union[str, Any]					=					'Hello how are you'
							_lowerCamelCase  :    Any					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)
							self.assertEqual(tokenizer(lowercase						).input_ids						,   tokenizer(lowercase						,   do_phonemize=lowercase						).input_ids						)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					self.tokenizer_class.from_pretrained(
							    'facebook/wav2vec2-lv-60-espeak-cv-ft'						,   word_delimiter_token='|'						)
							tokenizer.add_tokens('|'						)

							# fmt: off
							_lowerCamelCase  :    str					=					[
							    [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
							_lowerCamelCase  :    int					=					tokenizer.decode(sample_ids[0]						)
							_lowerCamelCase  :    Optional[int]					=					tokenizer.batch_decode(lowercase						)
							self.assertEqual(lowercase						,   batch_tokens[0]						)
							self.assertEqual(lowercase						,   ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']						)

							# decode with no word_del_token filter
							_lowerCamelCase  :    Any					=					tokenizer.decode(sample_ids[0]						,   filter_word_delimiter_token=lowercase						)
							_lowerCamelCase  :    List[str]					=					tokenizer.batch_decode(lowercase						,   filter_word_delimiter_token=lowercase						)
							self.assertEqual(lowercase						,   batch_tokens[0]						)
							self.assertEqual(lowercase						,   ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ']						)
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					self.tokenizer_class.from_pretrained(
							    'facebook/wav2vec2-lv-60-espeak-cv-ft'						,   word_delimiter_token='|'						)
							tokenizer.add_tokens('|'						)

							_lowerCamelCase  :    Tuple					=					'Hello how are you'
							_lowerCamelCase  :    Optional[Any]					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)

							_lowerCamelCase  :    int					=					tokenizer.decode(tokenizer(lowercase						).input_ids						,   filter_word_delimiter_token=lowercase						)

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

							_lowerCamelCase  :    Optional[Any]					=					'Hello how are you'
							_lowerCamelCase  :    List[Any]					=					tokenizer.phonemize(lowercase						,   phonemizer_lang='en-us'						)

							_lowerCamelCase  :    Optional[int]					=					tokenizer.decode(tokenizer(lowercase						).input_ids						,   filter_word_delimiter_token=lowercase						)

							self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |'						)]						).strip()						,   lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					self.tokenizer_class.from_pretrained(
							    'facebook/wav2vec2-lv-60-espeak-cv-ft'						,   word_delimiter_token=lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					'Hello how are you'

							_lowerCamelCase  :    Optional[int]					=					tokenizer(lowercase						,   phonemizer_lang='en-us'						).input_ids
							_lowerCamelCase  :    Optional[int]					=					tokenizer(lowercase						,   phonemizer_lang='fr-fr'						).input_ids

							self.assertNotEqual(lowercase						,   lowercase						)

							_lowerCamelCase  :    Tuple					=					tokenizer.decode(lowercase						)
							_lowerCamelCase  :    Optional[int]					=					tokenizer.decode(lowercase						)

							self.assertEqual(lowercase						,   'h ə l oʊ h aʊ ɑːɹ j uː'						)
							self.assertEqual(lowercase						,   'ɛ l o h aʊ a ʁ j u'						)
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)
							_lowerCamelCase  :    List[Any]					=					'Hello how Are you'
							_lowerCamelCase  :    Tuple					=					'hello how are you'

							_lowerCamelCase  :    Dict					=					tokenizer(lowercase						).input_ids
							_lowerCamelCase  :    Optional[int]					=					tokenizer(lowercase						).input_ids

							self.assertEqual(lowercase						,   lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'						)
							tokenizer.add_tokens(['!', '?']						)
							tokenizer.add_special_tokens({'cls_token': '$$$'}						)

							# fmt: off
							_lowerCamelCase  :    Dict					=					[
							    [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

							_lowerCamelCase  :    Optional[Any]					=					tokenizer.batch_decode(lowercase						)
							self.assertEqual(lowercase						,   ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']						)
			@staticmethod
			def       A_					(						lowercase						,   lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					[d[key] for d in offsets]
							return retrieved_list
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					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"
							_lowerCamelCase  :    str					=					[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

							_lowerCamelCase  :    str					=					tokenizer.decode(lowercase						,   output_char_offsets=lowercase						,   filter_word_delimiter_token=lowercase						)
							# 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(lowercase						,   lowercase						)						)

							# 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       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					self.get_tokenizer(word_delimiter_token='|'						)

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

											# transform list to ModelOutput
											_lowerCamelCase  :    List[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(lowercase						,   lowercase						):
															if isinstance(lowercase						,   lowercase						):
																			[recursive_check(lowercase						,   lowercase						) for la, la in zip(lowercase						,   lowercase						)]
															self.assertEqual(lowercase						,   lowercase						)

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

        # fmt: off
							_lowerCamelCase  :    Dict					=					[
							    [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
							_lowerCamelCase  :    Dict					=					tokenizer.batch_decode(lowercase						,   output_char_offsets=lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					[tokenizer.decode(lowercase						,   output_char_offsets=lowercase						) for ids in sample_ids]
							check_list_tuples_equal(lowercase						,   lowercase						)
			@unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'						)
			def       A_					(						self						):
							pass
			@unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'						)
			def       A_					(						self						):
							pass
			@unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency'						)
			def       A_					(						self						):
							pass
			@unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.get_tokenizers(do_lower_case=lowercase						)
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
															_lowerCamelCase  :    int					=					tokenizer.vocab_size
															_lowerCamelCase  :    Tuple					=					len(lowercase						)

															self.assertNotEqual(lowercase						,   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)

															_lowerCamelCase  :    List[Any]					=					['aaaaa bbbbbb', 'cccccccccdddddddd']
															_lowerCamelCase  :    Dict					=					tokenizer.add_tokens(lowercase						)
															_lowerCamelCase  :    Union[str, Any]					=					tokenizer.vocab_size
															_lowerCamelCase  :    int					=					len(lowercase						)

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

															_lowerCamelCase  :    Any					=					tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'						,   add_special_tokens=lowercase						)

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

															_lowerCamelCase  :    int					=					{'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
															_lowerCamelCase  :    str					=					tokenizer.add_special_tokens(lowercase						)
															_lowerCamelCase  :    Dict					=					tokenizer.vocab_size
															_lowerCamelCase  :    str					=					len(lowercase						)

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

															_lowerCamelCase  :    List[str]					=					tokenizer.encode(
															    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'						,   add_special_tokens=lowercase						)

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






			def       A_					(						self						):
							# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
							# is not the case for Wav2Vec2PhonemeCTCTokenizer.
							_lowerCamelCase  :    Tuple					=					self.get_tokenizers(fast=lowercase						,   do_lower_case=lowercase						)
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
															_lowerCamelCase  :    Optional[int]					=					['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
															_lowerCamelCase  :    List[Any]					=					tokenizer.convert_tokens_to_string(lowercase						)

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





"""simple docstring"""






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """umt5"""
			lowerCamelCase__   =    ["""past_key_values"""]
			def __init__(						self						,   lowercase=250112						,   lowercase=512						,   lowercase=64						,   lowercase=1024						,   lowercase=8						,   lowercase=None						,   lowercase=6						,   lowercase=32						,   lowercase=128						,   lowercase=0.1						,   lowercase=1E-6						,   lowercase=1.0						,   lowercase="gated-gelu"						,   lowercase=True						,   lowercase=True						,   lowercase="T5Tokenizer"						,   lowercase=True						,   lowercase=0						,   lowercase=1						,   lowercase=0						,   **lowercase						,   ):
							super().__init__(
							    is_encoder_decoder=lowercase						,   tokenizer_class=lowercase						,   tie_word_embeddings=lowercase						,   pad_token_id=lowercase						,   eos_token_id=lowercase						,   decoder_start_token_id=lowercase						,   **lowercase						,   )
							_lowerCamelCase  :    Union[str, Any]					=					vocab_size
							_lowerCamelCase  :    Optional[Any]					=					d_model
							_lowerCamelCase  :    Optional[Any]					=					d_kv
							_lowerCamelCase  :    Optional[int]					=					d_ff
							_lowerCamelCase  :    List[str]					=					num_layers
							_lowerCamelCase  :    Union[str, Any]					=					(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							_lowerCamelCase  :    List[str]					=					num_heads
							_lowerCamelCase  :    int					=					relative_attention_num_buckets
							_lowerCamelCase  :    Union[str, Any]					=					relative_attention_max_distance
							_lowerCamelCase  :    int					=					dropout_rate
							_lowerCamelCase  :    List[Any]					=					layer_norm_epsilon
							_lowerCamelCase  :    Union[str, Any]					=					initializer_factor
							_lowerCamelCase  :    List[Any]					=					feed_forward_proj
							_lowerCamelCase  :    Dict					=					use_cache

							_lowerCamelCase  :    Dict					=					self.feed_forward_proj.split('-'						)
							_lowerCamelCase  :    List[str]					=					act_info[-1]
							_lowerCamelCase  :    int					=					act_info[0] == 'gated'

							if len(lowercase						) > 1 and act_info[0] != "gated" or len(lowercase						) > 2:
											raise ValueError(
											    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''						)

							if feed_forward_proj == "gated-gelu":
											_lowerCamelCase  :    Optional[int]					=					'gelu_new'
			@property
			def       A_					(						self						):
							return self.d_model
			@property
			def       A_					(						self						):
							return self.num_heads






			@property
			def       A_					(						self						):
							return self.num_layers






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
											_lowerCamelCase  :    Optional[int]					=					'past_encoder_sequence + sequence'
											_lowerCamelCase  :    int					=					{0: 'batch'}
											_lowerCamelCase  :    List[str]					=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
											_lowerCamelCase  :    Dict					=					{0: 'batch', 1: 'decoder_sequence'}
											_lowerCamelCase  :    str					=					{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
											self.fill_with_past_key_values_(lowercase						,   direction='inputs'						)

							return common_inputs
			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def       A_					(						self						):
							return 13






			@property
			def       A_					(						self						):
							return 5E-4