




"""simple docstring"""




import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin






@require_phonemizer
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						WavaVecaPhonemeCTCTokenizer
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a		, range(len(_a ) ) ) )

							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(_a ) + """\n""" )





				def    _lowerCAmelCase				(       self		, _a		, _a=False		, _a=20		, _a=5 ):





							"""simple docstring"""
							lowerCamelCase			      =					[(i, tokenizer.decode([i]		, clean_up_tokenization_spaces=_a )) for i in range(len(_a ) )]
							lowerCamelCase			      =					list(filter(lambda _a : [t[0]] == tokenizer.encode(t[1]		, do_phonemize=_a )		, _a ) )
							if max_length is not None and len(_a ) > max_length:
										lowerCamelCase			      =					toks[:max_length]
							if min_length is not None and len(_a ) < min_length and len(_a ) > 0:
										while len(_a ) < 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(_a		, clean_up_tokenization_spaces=_a )
							if " " not in output_txt and len(_a ) > 1:
										lowerCamelCase			      =					(
										    tokenizer.decode([toks_ids[0]]		, clean_up_tokenization_spaces=_a )
										    + """ """
										    + tokenizer.decode(toks_ids[1:]		, clean_up_tokenization_spaces=_a )
										)
							if with_prefix_space:
										lowerCamelCase			      =					""" """ + output_txt
							lowerCamelCase			      =					tokenizer.encode(_a		, add_special_tokens=_a )
							return output_txt, output_ids





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							kwargs.update(self.special_tokens_map )
							return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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=_a ).input_ids
							self.assertEqual(_a		, [13, 392, 17] )  # xxx should be last token

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

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
							lowerCamelCase			      =					"""Hello how are you"""
							lowerCamelCase			      =					tokenizer.phonemize(_a		, phonemizer_lang="""en-us""" )

							lowerCamelCase			      =					tokenizer.decode(tokenizer(_a ).input_ids )

							self.assertEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a )
							self.assertEqual(_a		, batch_tokens[0] )
							self.assertEqual(_a		, ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a		, phonemizer_lang="""en-us""" )
							self.assertEqual(_a		, """h ə l oʊ | h aʊ | ɑːɹ | j uː |""" )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a		, phonemizer_lang="""en-us""" )
							self.assertEqual(tokenizer(_a ).input_ids		, tokenizer(_a		, do_phonemize=_a ).input_ids )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a )
							self.assertEqual(_a		, batch_tokens[0] )
							self.assertEqual(_a		, ["""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=_a )
							lowerCamelCase			      =					tokenizer.batch_decode(_a		, filter_word_delimiter_token=_a )
							self.assertEqual(_a		, batch_tokens[0] )
							self.assertEqual(_a		, ["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a		, phonemizer_lang="""en-us""" )

							lowerCamelCase			      =					tokenizer.decode(tokenizer(_a ).input_ids		, filter_word_delimiter_token=_a )

							self.assertEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a		, phonemizer_lang="""en-us""" )

							lowerCamelCase			      =					tokenizer.decode(tokenizer(_a ).input_ids		, filter_word_delimiter_token=_a )

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.tokenizer_class.from_pretrained(
							    """facebook/wav2vec2-lv-60-espeak-cv-ft"""		, word_delimiter_token=_a )
							lowerCamelCase			      =					"""Hello how are you"""

							lowerCamelCase			      =					tokenizer(_a		, phonemizer_lang="""en-us""" ).input_ids
							lowerCamelCase			      =					tokenizer(_a		, phonemizer_lang="""fr-fr""" ).input_ids

							self.assertNotEqual(_a		, _a )

							lowerCamelCase			      =					tokenizer.decode(_a )
							lowerCamelCase			      =					tokenizer.decode(_a )

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a ).input_ids
							lowerCamelCase			      =					tokenizer(_a ).input_ids

							self.assertEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a )
							self.assertEqual(_a		, ["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""] )





				@staticmethod
				def    _lowerCAmelCase				(       _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					[d[key] for d in offsets]
							return retrieved_list





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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(_a		, output_char_offsets=_a		, filter_word_delimiter_token=_a )
							# 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(_a		, _a ) )

							# 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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_tokenizer(word_delimiter_token="""|""" )

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

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

										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(_a		, output_char_offsets=_a )
							lowerCamelCase			      =					[tokenizer.decode(_a		, output_char_offsets=_a ) for ids in sample_ids]
							check_list_tuples_equal(_a		, _a )





				@unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				@unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





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





							"""simple docstring"""
							pass





				@unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_tokenizers(do_lower_case=_a )
							for tokenizer in tokenizers:
										with self.subTest(f'{tokenizer.__class__.__name__}' ):
													lowerCamelCase			      =					tokenizer.vocab_size
													lowerCamelCase			      =					len(_a )

													self.assertNotEqual(_a		, 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(_a )
													lowerCamelCase			      =					tokenizer.vocab_size
													lowerCamelCase			      =					len(_a )

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

													lowerCamelCase			      =					tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""		, add_special_tokens=_a )

													self.assertGreaterEqual(len(_a )		, 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(_a )
													lowerCamelCase			      =					tokenizer.vocab_size
													lowerCamelCase			      =					len(_a )

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

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

													self.assertGreaterEqual(len(_a )		, 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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





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





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# 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=_a		, do_lower_case=_a )
							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(_a )

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









"""simple docstring"""




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

import numpy as np

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


if is_torch_available():
					import torch


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["pixel_values"]





				def __init__(       self		, _a = True		, _a = None		, _a = PILImageResampling.BILINEAR		, _a = True		, _a = None		, _a = True		, _a = 1 / 255		, _a = True		, _a = None		, _a = None		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a )
							lowerCamelCase			      =					size if size is not None else {"""shortest_edge""": 256}
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_resize
							lowerCamelCase			      =					size
							lowerCamelCase			      =					resample
							lowerCamelCase			      =					do_center_crop
							lowerCamelCase			      =					crop_size
							lowerCamelCase			      =					do_rescale
							lowerCamelCase			      =					rescale_factor
							lowerCamelCase			      =					do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase			      =					image_std if image_std is not None else IMAGENET_STANDARD_STD





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = PILImageResampling.BICUBIC		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							if "shortest_edge" not in size:
										raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
							lowerCamelCase			      =					get_resize_output_image_size(_a		, size=size["""shortest_edge"""]		, default_to_square=_a )
							return resize(_a		, size=_a		, resample=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a )
							if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
							return center_crop(_a		, size=(size["""height"""], size["""width"""])		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a ):





							"""simple docstring"""
							return rescale(_a		, scale=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							return normalize(_a		, mean=_a		, std=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = ChannelDimension.FIRST		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					do_resize if do_resize is not None else self.do_resize
							lowerCamelCase			      =					size if size is not None else self.size
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					resample if resample is not None else self.resample
							lowerCamelCase			      =					do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase			      =					crop_size if crop_size is not None else self.crop_size
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase			      =					rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase			      =					do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else self.image_mean
							lowerCamelCase			      =					image_std if image_std is not None else self.image_std

							lowerCamelCase			      =					make_list_of_images(_a )

							if not valid_images(_a ):
										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:
										raise ValueError("""Size must be specified if do_resize is True.""" )

							if do_center_crop and crop_size is None:
										raise ValueError("""Crop size must be specified if do_center_crop 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.
							lowerCamelCase			      =					[to_numpy_array(_a ) for image in images]

							if do_resize:
										lowerCamelCase			      =					[self.resize(image=_a		, size=_a		, resample=_a ) for image in images]

							if do_center_crop:
										lowerCamelCase			      =					[self.center_crop(image=_a		, size=_a ) for image in images]

							if do_rescale:
										lowerCamelCase			      =					[self.rescale(image=_a		, scale=_a ) for image in images]

							if do_normalize:
										lowerCamelCase			      =					[self.normalize(image=_a		, mean=_a		, std=_a ) for image in images]

							lowerCamelCase			      =					[to_channel_dimension_format(_a		, _a ) for image in images]

							lowerCamelCase			      =					{"""pixel_values""": images}
							return BatchFeature(data=_a		, tensor_type=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					outputs.logits

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

										if is_torch_tensor(_a ):
													lowerCamelCase			      =					target_sizes.numpy()

										lowerCamelCase			      =					[]

										for idx in range(len(_a ) ):
													lowerCamelCase			      =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0 )		, size=target_sizes[idx]		, mode="""bilinear"""		, align_corners=_a )
													lowerCamelCase			      =					resized_logits[0].argmax(dim=0 )
													semantic_segmentation.append(_a )
							else:
										lowerCamelCase			      =					logits.argmax(dim=1 )
										lowerCamelCase			      =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

							return semantic_segmentation



