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):
						UpperCamelCase_  =	WavaVecaPhonemeCTCTokenizer
						UpperCamelCase_  =	False


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(
												    '''<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '''
												    '''ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '''
												    '''ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '''
												    '''oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '''
												    '''pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '''
												    '''yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '''
												    '''əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '''
												    '''ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '''
												    '''ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '''
												    '''uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '''
												    '''ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '''
												    '''ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '''
												    '''ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'''
												).split(''' '''		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							dict(zip(UpperCamelCase__					,	range(len(UpperCamelCase__		)		)		)		)

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

												SCREAMING_SNAKE_CASE							:					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(UpperCamelCase__		) + '''\n'''		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       Dict=20					,	UpperCamelCase__			:       Optional[int]=5		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[(i, tokenizer.decode([i]					,	clean_up_tokenization_spaces=UpperCamelCase__		)) for i in range(len(UpperCamelCase__		)		)]
												SCREAMING_SNAKE_CASE							:					str           =							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:
																		SCREAMING_SNAKE_CASE							:					int           =							toks[:max_length]
												if min_length is not None and len(UpperCamelCase__		) < min_length and len(UpperCamelCase__		) > 0:
																		while len(UpperCamelCase__		) < min_length:
																								SCREAMING_SNAKE_CASE							:					int           =							toks + toks
        # toks_str = [t[1] for t in toks]
												SCREAMING_SNAKE_CASE							:					Dict           =							[t[0] for t in toks]

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


						def  __A ( self			:       Tuple					,	**UpperCamelCase__			:       Dict		):





												'''simple docstring'''





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


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





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

												# check adding a single token
												tokenizer.add_tokens('''xxx'''		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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''']		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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

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


						def  __A ( self			:       Dict		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					int           =							'''Hello how are you'''
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.phonemize(UpperCamelCase__					,	phonemizer_lang='''en-us'''		)
												self.assertEqual(UpperCamelCase__					,	'''h ə l oʊ h aʊ ɑːɹ j uː'''		)


						def  __A ( self			:       str		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					List[Any]           =							'''Hello how are you'''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer.phonemize(UpperCamelCase__					,	phonemizer_lang='''en-us'''		)
												self.assertEqual(tokenizer(UpperCamelCase__		).input_ids					,	tokenizer(UpperCamelCase__					,	do_phonemize=UpperCamelCase__		).input_ids		)


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.decode(tokenizer(UpperCamelCase__		).input_ids		)

												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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

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





												'''simple docstring'''





												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							:					Union[str, Any]           =							'''Hello how are you'''
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.phonemize(UpperCamelCase__					,	phonemizer_lang='''en-us'''		)
												self.assertEqual(UpperCamelCase__					,	'''h ə l oʊ | h aʊ | ɑːɹ | j uː |'''		)


						def  __A ( self			:       int		):





												'''simple docstring'''





												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							:					Dict           =							'''Hello how are you'''
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.phonemize(UpperCamelCase__					,	phonemizer_lang='''en-us'''		)
												self.assertEqual(tokenizer(UpperCamelCase__		).input_ids					,	tokenizer(UpperCamelCase__					,	do_phonemize=UpperCamelCase__		).input_ids		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





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

												# fmt: off
												SCREAMING_SNAKE_CASE							:					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
												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.decode(sample_ids[0]		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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
												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.decode(sample_ids[0]					,	filter_word_delimiter_token=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							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			:       Dict		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					int           =							'''Hello how are you'''
												SCREAMING_SNAKE_CASE							:					str           =							tokenizer.phonemize(UpperCamelCase__					,	phonemizer_lang='''en-us'''		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.decode(tokenizer(UpperCamelCase__		).input_ids					,	filter_word_delimiter_token=UpperCamelCase__		)

												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       str		):





												'''simple docstring'''





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

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

												SCREAMING_SNAKE_CASE							:					List[Any]           =							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]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.tokenizer_class.from_pretrained(
												    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''					,	word_delimiter_token=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''Hello how are you'''

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer(UpperCamelCase__					,	phonemizer_lang='''en-us'''		).input_ids
												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer(UpperCamelCase__					,	phonemizer_lang='''fr-fr'''		).input_ids

												self.assertNotEqual(UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer.decode(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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[Any]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer(UpperCamelCase__		).input_ids
												SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer(UpperCamelCase__		).input_ids

												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							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							:					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

												SCREAMING_SNAKE_CASE							:					str           =							tokenizer.batch_decode(UpperCamelCase__		)
												self.assertEqual(UpperCamelCase__					,	['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$''']		)


						@staticmethod
						def  __A ( UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[d[key] for d in offsets]
												return retrieved_list


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							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							:					Any           =							[11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
												# fmt: on

												SCREAMING_SNAKE_CASE							:					Tuple           =							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			:       List[str]		):





												'''simple docstring'''





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

												def check_list_tuples_equal(UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       int		):
																		self.assertTrue(isinstance(UpperCamelCase__					,	UpperCamelCase__		)		)
																		self.assertTrue(isinstance(outputs_list[0]					,	UpperCamelCase__		)		)

																		# transform list to ModelOutput
																		SCREAMING_SNAKE_CASE							:					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(UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Dict		):
																								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
												SCREAMING_SNAKE_CASE							:					str           =							[
												    [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[Any]           =							tokenizer.batch_decode(UpperCamelCase__					,	output_char_offsets=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[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			:       Optional[Any]		):





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												pass


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.vocab_size
																								SCREAMING_SNAKE_CASE							:					List[str]           =							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)

																								SCREAMING_SNAKE_CASE							:					Dict           =							['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
																								SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer.add_tokens(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer.vocab_size
																								SCREAMING_SNAKE_CASE							:					Tuple           =							len(UpperCamelCase__		)

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

																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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		)

																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer.add_special_tokens(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.vocab_size
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)

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

																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							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			:       List[str]		):





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												pass



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.get_tokenizers(fast=UpperCamelCase__					,	do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE							:					Tuple           =							['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
																								SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer.convert_tokens_to_string(UpperCamelCase__		)

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






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


__UpperCamelCase	: int    		=  logging.get_logger(__name__)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						def constraint_to_multiple_of(_lowercase	,						_lowercase	,						_lowercase=0	,						_lowercase=None		):
												SCREAMING_SNAKE_CASE							:					int           =							round(val / multiple		) * multiple

												if max_val is not None and x > max_val:
																		SCREAMING_SNAKE_CASE							:					Dict           =							math.floor(val / multiple		) * multiple

												if x < min_val:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							math.ceil(val / multiple		) * multiple

												return x

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(output_size, output_size) if isinstance(_lowercase	,						_lowercase		) else output_size

						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_image_size(_lowercase		)
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							output_size

						# determine new height and width
						SCREAMING_SNAKE_CASE							:					Dict           =							output_height / input_height
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width		) < abs(1 - scale_height		):
																		# fit width
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_width
												else:
																		# fit height
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_height

						SCREAMING_SNAKE_CASE							:					List[str]           =							constraint_to_multiple_of(scale_height * input_height	,						multiple=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							constraint_to_multiple_of(scale_width * input_width	,						multiple=_lowercase		)

						return (new_height, new_width)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""pixel_values"""]


						def __init__( self			:       int					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Dict[str, int] = None					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BILINEAR					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Union[int, float] = 1 / 255					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							size if size is not None else {'''height''': 384, '''width''': 384}
												SCREAMING_SNAKE_CASE							:					Any           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							do_resize
												SCREAMING_SNAKE_CASE							:					Any           =							size
												SCREAMING_SNAKE_CASE							:					str           =							keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					int           =							resample
												SCREAMING_SNAKE_CASE							:					Any           =							do_rescale
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rescale_factor
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							do_normalize
												SCREAMING_SNAKE_CASE							:					str           =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BICUBIC					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							get_size_dict(UpperCamelCase__		)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							get_resize_output_image_size(
												    UpperCamelCase__					,	output_size=(size['''height'''], size['''width'''])					,	keep_aspect_ratio=UpperCamelCase__					,	multiple=UpperCamelCase__					,	)
												return resize(UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[int, float]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												return rescale(UpperCamelCase__					,	scale=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												return normalize(UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       ImageInput					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       PILImageResampling = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       float = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       ChannelDimension = ChannelDimension.FIRST					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					Tuple           =							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:					str           =							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:					Dict           =							make_list_of_images(UpperCamelCase__		)

												if not valid_images(UpperCamelCase__		):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

												if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

												if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE							:					Tuple           =							[to_numpy_array(UpperCamelCase__		) for image in images]

												if do_resize:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[self.resize(image=UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__		) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.rescale(image=UpperCamelCase__					,	scale=UpperCamelCase__		) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.normalize(image=UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[to_channel_dimension_format(UpperCamelCase__					,	UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Tuple           =							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Tuple] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																		if len(UpperCamelCase__		) != len(UpperCamelCase__		):
																								raise ValueError(
																								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

																		if is_torch_tensor(UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							target_sizes.numpy()

																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]

																		for idx in range(len(UpperCamelCase__		)		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							torch.nn.functional.interpolate(
																								    logits[idx].unsqueeze(dim=0		)					,	size=target_sizes[idx]					,	mode='''bilinear'''					,	align_corners=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							resized_logits[0].argmax(dim=0		)
																								semantic_segmentation.append(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							logits.argmax(dim=1		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

												return semantic_segmentation





