
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   (      __a   , unittest.TestCase			):
				_lowercase     						=WavaVecaPhonemeCTCTokenizer
				_lowercase     						=False




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

							lowerCAmelCase_         =      (
							    "<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_         =      dict(zip(_UpperCamelCase							,							range(len(_UpperCamelCase	)	)	)	)

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

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase=False							,							_UpperCamelCase=20							,							_UpperCamelCase=5	)							-> Tuple[str, list]:
							lowerCAmelCase_         =      [(i, tokenizer.decode([i]							,							clean_up_tokenization_spaces=_UpperCamelCase	)) for i in range(len(_UpperCamelCase	)	)]
							lowerCAmelCase_         =      list(filter(lambda _UpperCamelCase	: [t[0]] == tokenizer.encode(t[1]							,							do_phonemize=_UpperCamelCase	)							,							_UpperCamelCase	)	)
							if max_length is not None and len(_UpperCamelCase	) > max_length:
										lowerCAmelCase_         =      toks[:max_length]
							if min_length is not None and len(_UpperCamelCase	) < min_length and len(_UpperCamelCase	) > 0:
										while len(_UpperCamelCase	) < min_length:
													lowerCAmelCase_         =      toks + toks
        # toks_str = [t[1] for t in toks]
							lowerCAmelCase_         =      [t[0] for t in toks]

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




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Dict:
							kwargs.update(self.special_tokens_map	)
							return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




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

							# check adding a single token
							tokenizer.add_tokens("xxx"	)
							lowerCAmelCase_         =      tokenizer("m xxx ɪ"							,							do_phonemize=_UpperCamelCase	).input_ids
							self.assertEqual(_UpperCamelCase							,							[13, 392, 17]	)  # xxx should be last token

							tokenizer.add_tokens(["aaa", "bbb", "ccc"]	)
							lowerCAmelCase_         =      tokenizer("m aaa ɪ ccc"							,							do_phonemize=_UpperCamelCase	).input_ids
							self.assertEqual(_UpperCamelCase							,							[13, 393, 17, 395]	)  # aaa and ccc should be after xxx and 2 after aaa

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




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

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




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)
							lowerCAmelCase_         =      "Hello how are you"
							lowerCAmelCase_         =      tokenizer.phonemize(_UpperCamelCase							,							phonemizer_lang="en-us"	)

							lowerCAmelCase_         =      tokenizer.decode(tokenizer(_UpperCamelCase	).input_ids	)

							self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)

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




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

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




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

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




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

							# fmt: off
							lowerCAmelCase_         =      [
							    [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_         =      tokenizer.decode(sample_ids[0]	)
							lowerCAmelCase_         =      tokenizer.batch_decode(_UpperCamelCase	)
							self.assertEqual(_UpperCamelCase							,							batch_tokens[0]	)
							self.assertEqual(_UpperCamelCase							,							["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]	)

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




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

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

							lowerCAmelCase_         =      tokenizer.decode(tokenizer(_UpperCamelCase	).input_ids							,							filter_word_delimiter_token=_UpperCamelCase	)

							self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)




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

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

							lowerCAmelCase_         =      tokenizer.decode(tokenizer(_UpperCamelCase	).input_ids							,							filter_word_delimiter_token=_UpperCamelCase	)

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"							,							word_delimiter_token=_UpperCamelCase	)
							lowerCAmelCase_         =      "Hello how are you"

							lowerCAmelCase_         =      tokenizer(_UpperCamelCase							,							phonemizer_lang="en-us"	).input_ids
							lowerCAmelCase_         =      tokenizer(_UpperCamelCase							,							phonemizer_lang="fr-fr"	).input_ids

							self.assertNotEqual(_UpperCamelCase							,							_UpperCamelCase	)

							lowerCAmelCase_         =      tokenizer.decode(_UpperCamelCase	)
							lowerCAmelCase_         =      tokenizer.decode(_UpperCamelCase	)

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




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)
							lowerCAmelCase_         =      "Hello how Are you"
							lowerCAmelCase_         =      "hello how are you"

							lowerCAmelCase_         =      tokenizer(_UpperCamelCase	).input_ids
							lowerCAmelCase_         =      tokenizer(_UpperCamelCase	).input_ids

							self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)




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

							# fmt: off
							lowerCAmelCase_         =      [
							    [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_         =      tokenizer.batch_decode(_UpperCamelCase	)
							self.assertEqual(_UpperCamelCase							,							["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]	)




				@staticmethod
				def 	__a		(   _UpperCamelCase							,							_UpperCamelCase	)							-> Union[str, Any]:
							lowerCAmelCase_         =      [d[key] for d in offsets]
							return retrieved_list




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      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_         =      [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_         =      tokenizer.decode(_UpperCamelCase							,							output_char_offsets=_UpperCamelCase							,							filter_word_delimiter_token=_UpperCamelCase	)
							# check Wav2Vec2CTCTokenizerOutput keys for char
							self.assertEqual(len(outputs.keys()	)							,							2	)
							self.assertTrue("text" in outputs	)
							self.assertTrue("char_offsets" in outputs	)
							self.assertTrue(isinstance(_UpperCamelCase							,							_UpperCamelCase	)	)

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

							# check that offsets are actually correct for char
							# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
							# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
							self.assertListEqual(
							    self.get_from_offsets(outputs["char_offsets"]							,							"start_offset"	)							,							[0, 1, 4, 7, 9, 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	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.get_tokenizer(word_delimiter_token="|"	)

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

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

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

										def recursive_check(_UpperCamelCase							,							_UpperCamelCase	):
													if isinstance(_UpperCamelCase							,							_UpperCamelCase	):
																[recursive_check(_UpperCamelCase							,							_UpperCamelCase	) for la, la in zip(_UpperCamelCase							,							_UpperCamelCase	)]
													self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)

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

        # fmt: off
							lowerCAmelCase_         =      [
							    [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_         =      tokenizer.batch_decode(_UpperCamelCase							,							output_char_offsets=_UpperCamelCase	)
							lowerCAmelCase_         =      [tokenizer.decode(_UpperCamelCase							,							output_char_offsets=_UpperCamelCase	) for ids in sample_ids]
							check_list_tuples_equal(_UpperCamelCase							,							_UpperCamelCase	)




				@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"	)
				def 	__a		(   self	)							-> str:
							pass




				@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"	)
				def 	__a		(   self	)							-> Tuple:
							pass




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




				@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"	)
				def 	__a		(   self	)							-> List[str]:
							pass




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.get_tokenizers(do_lower_case=_UpperCamelCase	)
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""	):
													lowerCAmelCase_         =      tokenizer.vocab_size
													lowerCAmelCase_         =      len(_UpperCamelCase	)

													self.assertNotEqual(_UpperCamelCase							,							0	)

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

													lowerCAmelCase_         =      ["aaaaa bbbbbb", "cccccccccdddddddd"]
													lowerCAmelCase_         =      tokenizer.add_tokens(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer.vocab_size
													lowerCAmelCase_         =      len(_UpperCamelCase	)

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

													lowerCAmelCase_         =      tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"							,							add_special_tokens=_UpperCamelCase	)

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

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

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

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

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




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




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




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

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''bloom'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''num_hidden_layers''': '''n_layer''',
				    '''num_attention_heads''': '''n_head''',
				}




				def __init__(   self							,							_UpperCamelCase=250_880							,							_UpperCamelCase=64							,							_UpperCamelCase=2							,							_UpperCamelCase=8							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=1							,							_UpperCamelCase=2							,							_UpperCamelCase=False							,							_UpperCamelCase=0.0							,							_UpperCamelCase=0.0							,							_UpperCamelCase=1							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> str:
							lowerCAmelCase_         =      vocab_size
							# Backward compatibility with n_embed kwarg
							lowerCAmelCase_         =      kwargs.pop("n_embed"							,							_UpperCamelCase	)
							lowerCAmelCase_         =      hidden_size if n_embed is None else n_embed
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      pretraining_tp
							lowerCAmelCase_         =      apply_residual_connection_post_layernorm
							lowerCAmelCase_         =      hidden_dropout
							lowerCAmelCase_         =      attention_dropout

							lowerCAmelCase_         =      bos_token_id
							lowerCAmelCase_         =      eos_token_id
							lowerCAmelCase_         =      slow_but_exact

							super().__init__(bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=version.parse('''1.12'''			)




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase = "default"							,							_UpperCamelCase = None							,							_UpperCamelCase = False							,							)							-> int:
							super().__init__(_UpperCamelCase							,							task=_UpperCamelCase							,							patching_specs=_UpperCamelCase							,							use_past=_UpperCamelCase	)
							if not getattr(self._config							,							"pad_token_id"							,							_UpperCamelCase	):
										# TODO: how to do that better?
										lowerCAmelCase_         =      0




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							lowerCAmelCase_         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
							if self.use_past:
										# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
										self.fill_with_past_key_values_(_UpperCamelCase							,							direction="inputs"							,							inverted_values_shape=_UpperCamelCase	)
										lowerCAmelCase_         =      {0: "batch", 1: "past_sequence + sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}

							return common_inputs




				@property
				def 	__a		(   self	)							-> int:
							return self._config.n_layer




				@property
				def 	__a		(   self	)							-> int:
							return self._config.n_head




				@property
				def 	__a		(   self	)							-> float:
							return 1e-3




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = -1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      super(_UpperCamelCase							,							self	).generate_dummy_inputs(
							    _UpperCamelCase							,							batch_size=_UpperCamelCase							,							seq_length=_UpperCamelCase							,							is_pair=_UpperCamelCase							,							framework=_UpperCamelCase	)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase_         =      OrderedDict({"input_ids": common_inputs["input_ids"]}	)

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
										else:
													import torch

													lowerCAmelCase_      ,    lowerCAmelCase_         =      common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													lowerCAmelCase_         =      seqlen + 2
													lowerCAmelCase_         =      self._config.hidden_size // self.num_attention_heads
													lowerCAmelCase_         =      (
													    batch * self.num_attention_heads,
													    head_dim,
													    past_key_values_length,
													)
													lowerCAmelCase_         =      (
													    batch * self.num_attention_heads,
													    past_key_values_length,
													    head_dim,
													)
													lowerCAmelCase_         =      [
													    (torch.zeros(_UpperCamelCase	), torch.zeros(_UpperCamelCase	)) for _ in range(self.num_layers	)
													]

							lowerCAmelCase_         =      common_inputs["attention_mask"]
							if self.use_past:
										lowerCAmelCase_         =      ordered_inputs["attention_mask"].dtype
										lowerCAmelCase_         =      torch.cat(
										    [ordered_inputs["attention_mask"], torch.ones(_UpperCamelCase							,							_UpperCamelCase							,							dtype=_UpperCamelCase	)]							,							dim=1	)

							return ordered_inputs




				@property
				def 	__a		(   self	)							-> int:
							return 13



