

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   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					WavaVecaPhonemeCTCTokenizer
			_UpperCAmelCase       :							Tuple      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									super().setUp()

									SCREAMING_SNAKE_CASE_:			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_:			Dict  		=	dict(zip(lowerCAmelCase__       ,						range(len(lowerCAmelCase__))))

									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(lowerCAmelCase__) + "\n")



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

									# Ensure consistency
									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.decode(lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)
									if " " not in output_txt and len(lowerCAmelCase__) > 1:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	(
															    tokenizer.decode([toks_ids[0]]       ,						clean_up_tokenization_spaces=lowerCAmelCase__)
															    + " "
															    + tokenizer.decode(toks_ids[1:]       ,						clean_up_tokenization_spaces=lowerCAmelCase__)
															)
									if with_prefix_space:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	" " + output_txt
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.encode(lowerCAmelCase__       ,						add_special_tokens=lowerCAmelCase__)
									return output_txt, output_ids



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						**lowerCAmelCase__  :					List[Any]):
									kwargs.update(self.special_tokens_map)
									return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCAmelCase__)



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

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

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

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"Hello how are you"
									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.phonemize(lowerCAmelCase__       ,						phonemizer_lang="en-us")
									self.assertEqual(lowerCAmelCase__       ,						"h ə l oʊ h aʊ ɑːɹ j uː")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

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



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

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

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

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



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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"Hello how are you"
									SCREAMING_SNAKE_CASE_:			Any  		=	tokenizer.phonemize(lowerCAmelCase__       ,						phonemizer_lang="en-us")
									self.assertEqual(lowerCAmelCase__       ,						"h ə l oʊ | h aʊ | ɑːɹ | j uː |")



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

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"Hello how are you"
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.phonemize(lowerCAmelCase__       ,						phonemizer_lang="en-us")
									self.assertEqual(tokenizer(lowerCAmelCase__).input_ids       ,						tokenizer(lowerCAmelCase__       ,						do_phonemize=lowerCAmelCase__).input_ids)



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

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

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



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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"Hello how are you"
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.phonemize(lowerCAmelCase__       ,						phonemizer_lang="en-us")

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.decode(tokenizer(lowerCAmelCase__).input_ids       ,						filter_word_delimiter_token=lowerCAmelCase__)

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



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

									SCREAMING_SNAKE_CASE_:			Dict  		=	"Hello how are you"
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.phonemize(lowerCAmelCase__       ,						phonemizer_lang="en-us")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(tokenizer(lowerCAmelCase__).input_ids       ,						filter_word_delimiter_token=lowerCAmelCase__)

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



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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer(lowerCAmelCase__       ,						phonemizer_lang="en-us").input_ids
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer(lowerCAmelCase__       ,						phonemizer_lang="fr-fr").input_ids

									self.assertNotEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer.decode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"Hello how Are you"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"hello how are you"

									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer(lowerCAmelCase__).input_ids
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer(lowerCAmelCase__).input_ids

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			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_:			Union[str, Any]  		=	[
									    [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_:			int  		=	tokenizer.batch_decode(lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__       ,						["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"])



			@staticmethod
			def 	_SCREAMING_SNAKE_CASE      (			lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[d[key] for d in offsets]
									return retrieved_list



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	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_:			Optional[int]  		=	[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_:			Tuple  		=	tokenizer.decode(lowerCAmelCase__       ,						output_char_offsets=lowerCAmelCase__       ,						filter_word_delimiter_token=lowerCAmelCase__)
									# 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(lowerCAmelCase__       ,						lowerCAmelCase__))

									# 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 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.get_tokenizer(word_delimiter_token="|")

									def check_list_tuples_equal(lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					List[Any]):
															self.assertTrue(isinstance(lowerCAmelCase__       ,						lowerCAmelCase__))
															self.assertTrue(isinstance(outputs_list[0]       ,						lowerCAmelCase__))

															# 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(lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[int]):
																					if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
																											[recursive_check(lowerCAmelCase__       ,						lowerCAmelCase__) for la, la in zip(lowerCAmelCase__       ,						lowerCAmelCase__)]
																					self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

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

        # fmt: off
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[
									    [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_:			List[str]  		=	tokenizer.batch_decode(lowerCAmelCase__       ,						output_char_offsets=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	[tokenizer.decode(lowerCAmelCase__       ,						output_char_offsets=lowerCAmelCase__) for ids in sample_ids]
									check_list_tuples_equal(lowerCAmelCase__       ,						lowerCAmelCase__)



			@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes")
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									pass



			@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes")
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									pass



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



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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			str  		=	self.get_tokenizers(do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer.vocab_size
																					SCREAMING_SNAKE_CASE_:			Dict  		=	len(lowerCAmelCase__)

																					self.assertNotEqual(lowerCAmelCase__       ,						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_:			str  		=	["aaaaa bbbbbb", "cccccccccdddddddd"]
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.add_tokens(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.vocab_size
																					SCREAMING_SNAKE_CASE_:			Dict  		=	len(lowerCAmelCase__)

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

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"       ,						add_special_tokens=lowerCAmelCase__)

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

																					SCREAMING_SNAKE_CASE_:			int  		=	{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
																					SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer.add_special_tokens(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer.vocab_size
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	len(lowerCAmelCase__)

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

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.encode(
																					    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"       ,						add_special_tokens=lowerCAmelCase__)

																					self.assertGreaterEqual(len(lowerCAmelCase__)       ,						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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									pass



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




			def 	_SCREAMING_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.
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_tokenizers(fast=lowerCAmelCase__       ,						do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_:			int  		=	["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
																					SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.convert_tokens_to_string(lowerCAmelCase__)

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









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					StableDiffusionInpaintPipeline
			_UpperCAmelCase       :							Tuple      							=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCAmelCase       :							List[str]      							=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase       :							Tuple      							=					frozenset(
			    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase       :							Optional[int]      							=					frozenset([]     )



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,						layers_per_block=2       ,						sample_size=32       ,						in_channels=9       ,						out_channels=4       ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,						cross_attention_dim=32       ,						attention_head_dim=(2, 4)       ,						use_linear_projection=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoencoderKL(
									    block_out_channels=[32, 64]       ,						in_channels=3       ,						out_channels=3       ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,						latent_channels=4       ,						sample_size=128       ,						)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextConfig(
									    bos_token_id=0       ,						eos_token_id=2       ,						hidden_size=32       ,						intermediate_size=37       ,						layer_norm_eps=1E-05       ,						num_attention_heads=4       ,						num_hidden_layers=5       ,						pad_token_id=1       ,						vocab_size=1000       ,						hidden_act="gelu"       ,						projection_dim=512       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextModel(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[int]=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									SCREAMING_SNAKE_CASE_:			Dict  		=	floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	image.cpu().permute(0       ,						2       ,						3       ,						1)[0]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((64, 64))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": init_image,
									    "mask_image": mask_image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			int  		=	StableDiffusionInpaintPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Dict  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			str  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 9E-3



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Any  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

									SCREAMING_SNAKE_CASE_:			str  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Dict  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						safety_checker=lowerCAmelCase__       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Dict  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 5E-1




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE_:			Tuple  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PNDMScheduler.from_pretrained(lowerCAmelCase__       ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_:			Any  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=2       ,						output_type="np"       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9






