"""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        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  WavaVecaPhonemeCTCTokenizer
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        super().setUp()

        lowercase_	:				List[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(' '			)
        lowercase_	:				Tuple													=	dict(zip(__UpperCamelCase     ,range(len(__UpperCamelCase			)			)			)			)

        lowercase_	:				Optional[Any]													=	{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

        lowercase_	:				List[str]													=	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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=False     ,__UpperCamelCase=20     ,__UpperCamelCase=5			)	->      Tuple[str, list]:


        '''simple docstring'''


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

        # Ensure consistency
        lowercase_	:				Optional[int]													=	tokenizer.decode(__UpperCamelCase     ,clean_up_tokenization_spaces=__UpperCamelCase			)
        if " " not in output_txt and len(__UpperCamelCase			) > 1:
             lowercase_	:				Union[str, Any]													=	(
                 tokenizer.decode([toks_ids[0]]     ,clean_up_tokenization_spaces=__UpperCamelCase			)
                 + ' '
                 + tokenizer.decode(toks_ids[1:]     ,clean_up_tokenization_spaces=__UpperCamelCase			)
             )
        if with_prefix_space:
             lowercase_	:				Any													=	' ' + output_txt
        lowercase_	:				Optional[Any]													=	tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
        return output_txt, output_ids
   def 			_UpperCAmelCase   (			self     ,**__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'			)

        # check adding a single token
        tokenizer.add_tokens('xxx'			)
        lowercase_	:				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']			)
        lowercase_	:				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

        lowercase_	:				Union[str, Any]													=	tokenizer('maɪ c'     ,do_phonemize=__UpperCamelCase			).input_ids
        self.assertEqual(__UpperCamelCase     ,[3, 200]			)  # mai should be <unk> (=3)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


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

        lowercase_	:				List[str]													=	'Hello how are you'
        lowercase_	:				Any													=	tokenizer.phonemize(__UpperCamelCase     ,phonemizer_lang='en-us'			)
        self.assertEqual(__UpperCamelCase     ,'h ə l oʊ h aʊ ɑːɹ j uː'			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'			)

        lowercase_	:				Union[str, Any]													=	'Hello how are you'
        lowercase_	:				Tuple													=	tokenizer.phonemize(__UpperCamelCase     ,phonemizer_lang='en-us'			)
        self.assertEqual(tokenizer(__UpperCamelCase			).input_ids     ,tokenizer(__UpperCamelCase     ,do_phonemize=__UpperCamelCase			).input_ids			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


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

        lowercase_	:				Any													=	tokenizer.decode(tokenizer(__UpperCamelCase			).input_ids			)

        self.assertEqual(__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


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

        lowercase_	:				Tuple													=	[
            [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
            [24, 22, 5, 24, 22, 5, 77],
        ]
        lowercase_	:				int													=	tokenizer.decode(sample_ids[0]			)
        lowercase_	:				List[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 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


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

        lowercase_	:				List[Any]													=	'Hello how are you'
        lowercase_	:				Optional[int]													=	tokenizer.phonemize(__UpperCamelCase     ,phonemizer_lang='en-us'			)
        self.assertEqual(__UpperCamelCase     ,'h ə l oʊ | h aʊ | ɑːɹ | j uː |'			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


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

        lowercase_	:				Union[str, Any]													=	'Hello how are you'
        lowercase_	:				Any													=	tokenizer.phonemize(__UpperCamelCase     ,phonemizer_lang='en-us'			)
        self.assertEqual(tokenizer(__UpperCamelCase			).input_ids     ,tokenizer(__UpperCamelCase     ,do_phonemize=__UpperCamelCase			).input_ids			)
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


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

        # fmt: off
        lowercase_	:				int													=	[
            [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
        lowercase_	:				Optional[int]													=	tokenizer.decode(sample_ids[0]			)
        lowercase_	:				Dict													=	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
        lowercase_	:				List[str]													=	tokenizer.decode(sample_ids[0]     ,filter_word_delimiter_token=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	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 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


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

        lowercase_	:				Tuple													=	'Hello how are you'
        lowercase_	:				List[Any]													=	tokenizer.phonemize(__UpperCamelCase     ,phonemizer_lang='en-us'			)

        lowercase_	:				str													=	tokenizer.decode(tokenizer(__UpperCamelCase			).input_ids     ,filter_word_delimiter_token=__UpperCamelCase			)

        self.assertEqual(__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


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

        lowercase_	:				List[Any]													=	'Hello how are you'
        lowercase_	:				int													=	tokenizer.phonemize(__UpperCamelCase     ,phonemizer_lang='en-us'			)

        lowercase_	:				Optional[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 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.tokenizer_class.from_pretrained(
            'facebook/wav2vec2-lv-60-espeak-cv-ft'     ,word_delimiter_token=__UpperCamelCase			)
        lowercase_	:				List[Any]													=	'Hello how are you'

        lowercase_	:				List[str]													=	tokenizer(__UpperCamelCase     ,phonemizer_lang='en-us'			).input_ids
        lowercase_	:				List[Any]													=	tokenizer(__UpperCamelCase     ,phonemizer_lang='fr-fr'			).input_ids

        self.assertNotEqual(__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	tokenizer.decode(__UpperCamelCase			)
        lowercase_	:				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 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


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

        lowercase_	:				str													=	tokenizer(__UpperCamelCase			).input_ids
        lowercase_	:				Optional[int]													=	tokenizer(__UpperCamelCase			).input_ids

        self.assertEqual(__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


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

        # fmt: off
        lowercase_	:				List[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

        lowercase_	:				Tuple													=	tokenizer.batch_decode(__UpperCamelCase			)
        self.assertEqual(__UpperCamelCase     ,['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']			)
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	[d[key] for d in offsets]
        return retrieved_list
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	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"
        lowercase_	:				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

        lowercase_	:				Any													=	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 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.get_tokenizer(word_delimiter_token='|'			)

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

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

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

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

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

        # fmt: off
        lowercase_	:				int													=	[
            [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
        lowercase_	:				str													=	tokenizer.batch_decode(__UpperCamelCase     ,output_char_offsets=__UpperCamelCase			)
        lowercase_	:				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 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        pass
   @unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


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


        '''simple docstring'''


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


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.get_tokenizers(do_lower_case=__UpperCamelCase			)
        for tokenizer in tokenizers:
             with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
                  lowercase_	:				Any													=	tokenizer.vocab_size
                  lowercase_	:				Union[str, Any]													=	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)

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

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

                  lowercase_	:				Tuple													=	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			)

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

                  lowercase_	:				List[str]													=	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 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


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


        '''simple docstring'''


        pass







   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.get_tokenizers(fast=__UpperCamelCase     ,do_lower_case=__UpperCamelCase			)
        for tokenizer in tokenizers:
             with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
                  lowercase_	:				Union[str, Any]													=	['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
                  lowercase_	:				Optional[int]													=	tokenizer.convert_tokens_to_string(__UpperCamelCase			)

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


"""simple docstring"""







import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     print('Loading config file...'      )

     def flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Any=""     ,			__SCREAMING_SNAKE_CASE							:     List[Any]="."      ):
          lowercase_	:				List[str]													=	[]
          for k, v in d.items():
               lowercase_	:				Dict													=	parent_key + sep + k if parent_key else k
               if isinstance(__SCREAMING_SNAKE_CASE     ,			collections.abc.MutableMapping      ):
                    items.extend(flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			sep=__SCREAMING_SNAKE_CASE      ).items()      )
               else:
                    items.append((new_key, v)      )
          return dict(__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Optional[Any]													=	argparse.Namespace()
     with open(__SCREAMING_SNAKE_CASE     ,			'r'      ) as yaml_file:
          try:
               lowercase_	:				str													=	yaml.load(__SCREAMING_SNAKE_CASE     ,			Loader=yaml.FullLoader      )

               lowercase_	:				List[Any]													=	flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE      )
               for k, v in flat_cfg.items():
                    setattr(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          except yaml.YAMLError as exc:
               logger.error('Error while loading config file: {}. Error message: {}'.format(__SCREAMING_SNAKE_CASE     ,			str(__SCREAMING_SNAKE_CASE      )      )      )
     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     lowercase_	:				int													=	MobileViTVaConfig()

     lowercase_	:				List[str]													=	False

     # dataset
     if task_name.startswith('imagenet1k_'      ):
          lowercase_	:				List[Any]													=	10_00
          if int(task_name.strip().split('_'      )[-1]      ) == 3_84:
               lowercase_	:				str													=	3_84
          else:
               lowercase_	:				Dict													=	2_56
          lowercase_	:				int													=	'imagenet-1k-id2label.json'
     elif task_name.startswith('imagenet21k_to_1k_'      ):
          lowercase_	:				int													=	2_10_00
          if int(task_name.strip().split('_'      )[-1]      ) == 3_84:
               lowercase_	:				Optional[Any]													=	3_84
          else:
               lowercase_	:				Tuple													=	2_56
          lowercase_	:				List[str]													=	'imagenet-22k-id2label.json'
     elif task_name.startswith('ade20k_'      ):
          lowercase_	:				int													=	1_51
          lowercase_	:				Optional[Any]													=	5_12
          lowercase_	:				str													=	'ade20k-id2label.json'
          lowercase_	:				List[Any]													=	True
     elif task_name.startswith('voc_'      ):
          lowercase_	:				Union[str, Any]													=	21
          lowercase_	:				Tuple													=	5_12
          lowercase_	:				List[str]													=	'pascal-voc-id2label.json'
          lowercase_	:				str													=	True

     # orig_config
     lowercase_	:				Optional[int]													=	load_orig_config_file(__SCREAMING_SNAKE_CASE      )
     assert getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.name'     ,			-1      ) == "mobilevit_v2", "Invalid model"
     lowercase_	:				Optional[Any]													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.mitv2.width_multiplier'     ,			1.0      )
     assert (
         getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.mitv2.attn_norm_layer'     ,			-1      ) == "layer_norm_2d"
     ), "Norm layers other than layer_norm_2d is not supported"
     lowercase_	:				Any													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.activation.name'     ,			'swish'      )
     # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

     if is_segmentation_model:
          lowercase_	:				Any													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.segmentation.output_stride'     ,			16      )
          if "_deeplabv3" in task_name:
               lowercase_	:				Any													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.segmentation.deeplabv3.aspp_rates'     ,			[12, 24, 36]      )
               lowercase_	:				Union[str, Any]													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.segmentation.deeplabv3.aspp_out_channels'     ,			5_12      )
               lowercase_	:				Any													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.segmentation.deeplabv3.aspp_dropout'     ,			0.1      )

    # id2label
     lowercase_	:				Optional[Any]													=	'huggingface/label-files'
     lowercase_	:				List[Any]													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			repo_type='dataset'      )     ,			'r'      )      )
     lowercase_	:				List[str]													=	{int(__SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
     lowercase_	:				int													=	idalabel
     lowercase_	:				List[Any]													=	{v: k for k, v in idalabel.items()}

     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				List[Any]													=	dct.pop(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	val



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=False      ):
     if base_model:
          lowercase_	:				int													=	''
     else:
          lowercase_	:				str													=	'mobilevitv2.'

     lowercase_	:				Dict													=	[]
     for k in state_dict.keys():
          if k[:8] == "encoder.":
               lowercase_	:				Dict													=	k[8:]
          else:
               lowercase_	:				Union[str, Any]													=	k

          if ".block." in k:
               lowercase_	:				List[str]													=	k_new.replace('.block.'     ,			'.'      )
          if ".conv." in k:
               lowercase_	:				List[Any]													=	k_new.replace('.conv.'     ,			'.convolution.'      )
          if ".norm." in k:
               lowercase_	:				str													=	k_new.replace('.norm.'     ,			'.normalization.'      )

          if "conv_1." in k:
               lowercase_	:				Dict													=	k_new.replace('conv_1.'     ,			F'''{model_prefix}conv_stem.'''      )
          for i in [1, 2]:
               if F'''layer_{i}.''' in k:
                    lowercase_	:				Tuple													=	k_new.replace(F'''layer_{i}.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.layer.'''      )
          if ".exp_1x1." in k:
               lowercase_	:				Any													=	k_new.replace('.exp_1x1.'     ,			'.expand_1x1.'      )
          if ".red_1x1." in k:
               lowercase_	:				str													=	k_new.replace('.red_1x1.'     ,			'.reduce_1x1.'      )

          for i in [3, 4, 5]:
               if F'''layer_{i}.0.''' in k:
                    lowercase_	:				Tuple													=	k_new.replace(F'''layer_{i}.0.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''      )
               if F'''layer_{i}.1.local_rep.0.''' in k:
                    lowercase_	:				Any													=	k_new.replace(F'''layer_{i}.1.local_rep.0.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''      )
               if F'''layer_{i}.1.local_rep.1.''' in k:
                    lowercase_	:				List[Any]													=	k_new.replace(F'''layer_{i}.1.local_rep.1.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''      )

          for i in [3, 4, 5]:
               if i == 3:
                    lowercase_	:				Dict													=	[0, 1]
               elif i == 4:
                    lowercase_	:				int													=	[0, 1, 2, 3]
               elif i == 5:
                    lowercase_	:				List[str]													=	[0, 1, 2]

               for j in j_in:
                    if F'''layer_{i}.1.global_rep.{j}.''' in k:
                         lowercase_	:				List[str]													=	k_new.replace(
                             F'''layer_{i}.1.global_rep.{j}.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''      )
               if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
                    lowercase_	:				int													=	k_new.replace(
                        F'''layer_{i}.1.global_rep.{j+1}.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''      )

               if F'''layer_{i}.1.conv_proj.''' in k:
                    lowercase_	:				str													=	k_new.replace(F'''layer_{i}.1.conv_proj.'''     ,			F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''      )

          if "pre_norm_attn.0." in k:
               lowercase_	:				Optional[Any]													=	k_new.replace('pre_norm_attn.0.'     ,			'layernorm_before.'      )
          if "pre_norm_attn.1." in k:
               lowercase_	:				Any													=	k_new.replace('pre_norm_attn.1.'     ,			'attention.'      )
          if "pre_norm_ffn.0." in k:
               lowercase_	:				List[str]													=	k_new.replace('pre_norm_ffn.0.'     ,			'layernorm_after.'      )
          if "pre_norm_ffn.1." in k:
               lowercase_	:				int													=	k_new.replace('pre_norm_ffn.1.'     ,			'ffn.conv1.'      )
          if "pre_norm_ffn.3." in k:
               lowercase_	:				str													=	k_new.replace('pre_norm_ffn.3.'     ,			'ffn.conv2.'      )

          if "classifier.1." in k:
               lowercase_	:				Union[str, Any]													=	k_new.replace('classifier.1.'     ,			'classifier.'      )

          if "seg_head." in k:
               lowercase_	:				Optional[int]													=	k_new.replace('seg_head.'     ,			'segmentation_head.'      )
          if ".aspp_layer." in k:
               lowercase_	:				Dict													=	k_new.replace('.aspp_layer.'     ,			'.'      )
          if ".aspp_pool." in k:
               lowercase_	:				Dict													=	k_new.replace('.aspp_pool.'     ,			'.'      )

          rename_keys.append((k, k_new)      )
     return rename_keys



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
     lowercase_	:				str													=	[]
     for k in state_dict.keys():
          if k.startswith('seg_head.aux_head.'      ):
               keys_to_ignore.append(__SCREAMING_SNAKE_CASE      )
     for k in keys_to_ignore:
          state_dict.pop(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       ):
     lowercase_	:				Union[str, Any]													=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
     lowercase_	:				Any													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,			stream=__SCREAMING_SNAKE_CASE      ).raw      )
     return im



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				Tuple													=	get_mobilevitva_config(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # load original state_dict
     lowercase_	:				Tuple													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )

     # load huggingface model
     if task_name.startswith('ade20k_'      ) or task_name.startswith('voc_'      ):
          lowercase_	:				Tuple													=	MobileViTVaForSemanticSegmentation(__SCREAMING_SNAKE_CASE      ).eval()
          lowercase_	:				Optional[int]													=	False
     else:
          lowercase_	:				Any													=	MobileViTVaForImageClassification(__SCREAMING_SNAKE_CASE      ).eval()
          lowercase_	:				int													=	False

     # remove and rename some keys of load the original model
     lowercase_	:				Any													=	checkpoint
     remove_unused_keys(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Union[str, Any]													=	create_rename_keys(__SCREAMING_SNAKE_CASE     ,			base_model=__SCREAMING_SNAKE_CASE      )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # load modified state_dict
     model.load_state_dict(__SCREAMING_SNAKE_CASE      )

     # Check outputs on an image, prepared by MobileViTImageProcessor
     lowercase_	:				Union[str, Any]													=	MobileViTImageProcessor(crop_size=config.image_size     ,			size=config.image_size + 32      )
     lowercase_	:				Any													=	image_processor(images=prepare_img()     ,			return_tensors='pt'      )
     lowercase_	:				Optional[int]													=	model(**__SCREAMING_SNAKE_CASE      )

     # verify classification model
     if task_name.startswith('imagenet'      ):
          lowercase_	:				List[str]													=	outputs.logits
          lowercase_	:				int													=	logits.argmax(-1      ).item()
          print('Predicted class:'     ,			model.config.idalabel[predicted_class_idx]      )
          if task_name.startswith('imagenet1k_256'      ) and config.width_multiplier == 1.0:
               # expected_logits for base variant
               lowercase_	:				Optional[int]													=	torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01]      )
               assert torch.allclose(logits[0, :3]     ,			__SCREAMING_SNAKE_CASE     ,			atol=1E-4      )

     Path(__SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE      )
     print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''      )
     model.save_pretrained(__SCREAMING_SNAKE_CASE      )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
     image_processor.save_pretrained(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--task",
           default="imagenet1k_256",
           type=str,
           help=(
               "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
               "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
           ),
           choices=[
               "imagenet1k_256",
               "imagenet1k_384",
               "imagenet21k_to_1k_256",
               "imagenet21k_to_1k_384",
               "ade20k_deeplabv3",
               "voc_deeplabv3",
           ],
       )

       parser.add_argument(
           "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
       )
       parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
       )

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_mobilevitva_checkpoint(
           args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
       )

