
"""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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					WavaVecaPhonemeCTCTokenizer
				SCREAMING_SNAKE_CASE_:							Any							=					False


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Tuple:







											"""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(UpperCAmelCase_				,     range(len(UpperCAmelCase_  )  )  )  )

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

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


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Optional[int]=False				,     UpperCAmelCase_	:			str=20				,     UpperCAmelCase_	:			int=5  )		->  Tuple[str, list]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[(i, tokenizer.decode([i]				,     clean_up_tokenization_spaces=UpperCAmelCase_  )) for i in range(len(UpperCAmelCase_  )  )]
											_lowerCAmelCase  	=			list(filter(lambda UpperCAmelCase_  : [t[0]] == tokenizer.encode(t[1]				,     do_phonemize=UpperCAmelCase_  )				,     UpperCAmelCase_  )  )
											if max_length is not None and len(UpperCAmelCase_  ) > max_length:
																		_lowerCAmelCase  	=			toks[:max_length]
											if min_length is not None and len(UpperCAmelCase_  ) < min_length and len(UpperCAmelCase_  ) > 0:
																		while len(UpperCAmelCase_  ) < min_length:
																									_lowerCAmelCase  	=			toks + toks
        # toks_str = [t[1] for t in toks]
											_lowerCAmelCase  	=			[t[0] for t in toks]

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


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     **UpperCAmelCase_	:			Dict  )		->  List[str]:







											"""simple docstring"""




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


				def 				__lowerCamelCase	(					self	:			int  )		->  int:







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

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

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


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  str:







											"""simple docstring"""




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

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


				def 				__lowerCamelCase	(					self	:			Any  )		->  Tuple:







											"""simple docstring"""




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

											_lowerCAmelCase  	=			'Hello how are you'
											_lowerCAmelCase  	=			tokenizer.phonemize(UpperCAmelCase_				,     phonemizer_lang='en-us'  )
											self.assertEqual(tokenizer(UpperCAmelCase_  ).input_ids				,     tokenizer(UpperCAmelCase_				,     do_phonemize=UpperCAmelCase_  ).input_ids  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )
											_lowerCAmelCase  	=			'Hello how are you'
											_lowerCAmelCase  	=			tokenizer.phonemize(UpperCAmelCase_				,     phonemizer_lang='en-us'  )

											_lowerCAmelCase  	=			tokenizer.decode(tokenizer(UpperCAmelCase_  ).input_ids  )

											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str  )		->  Optional[Any]:







											"""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(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     batch_tokens[0]  )
											self.assertEqual(UpperCAmelCase_				,     ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Optional[int]:







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


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Union[str, Any]:







											"""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(UpperCAmelCase_				,     phonemizer_lang='en-us'  )
											self.assertEqual(tokenizer(UpperCAmelCase_  ).input_ids				,     tokenizer(UpperCAmelCase_				,     do_phonemize=UpperCAmelCase_  ).input_ids  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""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(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     batch_tokens[0]  )
											self.assertEqual(UpperCAmelCase_				,     ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']  )

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


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







											"""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(UpperCAmelCase_				,     phonemizer_lang='en-us'  )

											_lowerCAmelCase  	=			tokenizer.decode(tokenizer(UpperCAmelCase_  ).input_ids				,     filter_word_delimiter_token=UpperCAmelCase_  )

											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Tuple:







											"""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(UpperCAmelCase_				,     phonemizer_lang='en-us'  )

											_lowerCAmelCase  	=			tokenizer.decode(tokenizer(UpperCAmelCase_  ).input_ids				,     filter_word_delimiter_token=UpperCAmelCase_  )

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


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'				,     word_delimiter_token=UpperCAmelCase_  )
											_lowerCAmelCase  	=			'Hello how are you'

											_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     phonemizer_lang='en-us'  ).input_ids
											_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     phonemizer_lang='fr-fr'  ).input_ids

											self.assertNotEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			tokenizer.decode(UpperCAmelCase_  )
											_lowerCAmelCase  	=			tokenizer.decode(UpperCAmelCase_  )

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


				def 				__lowerCamelCase	(					self	:			str  )		->  List[str]:







											"""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(UpperCAmelCase_  ).input_ids
											_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_  ).input_ids

											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str  )		->  List[Any]:







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


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Any  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[d[key] for d in offsets]
											return retrieved_list


				def 				__lowerCamelCase	(					self	:			Dict  )		->  List[Any]:







											"""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(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 				__lowerCamelCase	(					self	:			int  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_tokenizer(word_delimiter_token='|'  )

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

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

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

																		def recursive_check(UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[Any]  ):
																									if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ):
																																[recursive_check(UpperCAmelCase_				,     UpperCAmelCase_  ) for la, la in zip(UpperCAmelCase_				,     UpperCAmelCase_  )]
																									self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

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

        # fmt: off
											_lowerCAmelCase  	=			[
											    [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
											    [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
											]
											# fmt: on

											# We assume that `decode` works as expected. All we will check now is
											# the output type is correct and the output is identical to `decode`

											# char
											_lowerCAmelCase  	=			tokenizer.batch_decode(UpperCAmelCase_				,     output_char_offsets=UpperCAmelCase_  )
											_lowerCAmelCase  	=			[tokenizer.decode(UpperCAmelCase_				,     output_char_offsets=UpperCAmelCase_  ) for ids in sample_ids]
											check_list_tuples_equal(UpperCAmelCase_				,     UpperCAmelCase_  )


				@unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'  )
				def 				__lowerCamelCase	(					self	:			Any  )		->  str:







											"""simple docstring"""




											pass


				@unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'  )
				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											pass


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







											"""simple docstring"""




											pass


				@unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'  )
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Optional[Any]:







											"""simple docstring"""




											pass


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_tokenizers(do_lower_case=UpperCAmelCase_  )
											for tokenizer in tokenizers:
																		with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
																									_lowerCAmelCase  	=			tokenizer.vocab_size
																									_lowerCAmelCase  	=			len(UpperCAmelCase_  )

																									self.assertNotEqual(UpperCAmelCase_				,     0  )

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

																									_lowerCAmelCase  	=			['aaaaa bbbbbb', 'cccccccccdddddddd']
																									_lowerCAmelCase  	=			tokenizer.add_tokens(UpperCAmelCase_  )
																									_lowerCAmelCase  	=			tokenizer.vocab_size
																									_lowerCAmelCase  	=			len(UpperCAmelCase_  )

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

																									_lowerCAmelCase  	=			tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'				,     add_special_tokens=UpperCAmelCase_  )

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

																									_lowerCAmelCase  	=			{'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
																									_lowerCAmelCase  	=			tokenizer.add_special_tokens(UpperCAmelCase_  )
																									_lowerCAmelCase  	=			tokenizer.vocab_size
																									_lowerCAmelCase  	=			len(UpperCAmelCase_  )

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

																									_lowerCAmelCase  	=			tokenizer.encode(
																									    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'				,     add_special_tokens=UpperCAmelCase_  )

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


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







											"""simple docstring"""




											pass


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







											"""simple docstring"""




											pass







				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_tokenizers(fast=UpperCAmelCase_				,     do_lower_case=UpperCAmelCase_  )
											for tokenizer in tokenizers:
																		with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
																									_lowerCAmelCase  	=			['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
																									_lowerCAmelCase  	=			tokenizer.convert_tokens_to_string(UpperCAmelCase_  )

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








"""simple docstring"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
							        f"""stage{idx}.patch_embed.proj.weight""",
							    )						)
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
							        f"""stage{idx}.patch_embed.proj.bias""",
							    )						)
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
							        f"""stage{idx}.patch_embed.norm.weight""",
							    )						)
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
							        f"""stage{idx}.patch_embed.norm.bias""",
							    )						)
							return embed






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
							    )						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")						)
							return attention_weights






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')						)
							return token






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							head.append(('layernorm.weight', 'norm.weight')						)
							head.append(('layernorm.bias', 'norm.bias')						)
							head.append(('classifier.weight', 'head.weight')						)
							head.append(('classifier.bias', 'head.bias')						)
							return head






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000

							_lowerCAmelCase  	=			'huggingface/label-files'
							_lowerCAmelCase  	=			num_labels
							_lowerCAmelCase  	=			json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  repo_type='dataset'						)						)     ,  'r'						)						)
							_lowerCAmelCase  	=			{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

							_lowerCAmelCase  	=			idalabel
							_lowerCAmelCase  	=			{v: k for k, v in idalabel.items()}

							_lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

							# For depth size 13 (13 = 1+2+10)
							if cvt_model.rsplit('/'     ,  1						)[-1][4:6] == "13":
														_lowerCAmelCase  	=			[1, 2, 10]

							# For depth size 21 (21 = 1+4+16)
							elif cvt_model.rsplit('/'     ,  1						)[-1][4:6] == "21":
														_lowerCAmelCase  	=			[1, 4, 16]

							# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
							else:
														_lowerCAmelCase  	=			[2, 2, 20]
														_lowerCAmelCase  	=			[3, 12, 16]
														_lowerCAmelCase  	=			[192, 768, 1024]

							_lowerCAmelCase  	=			CvtForImageClassification(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
							_lowerCAmelCase  	=			image_size
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  map_location=torch.device('cpu'						)						)

							_lowerCAmelCase  	=			OrderedDict()
							_lowerCAmelCase  	=			[]

							for idx in range(len(config.depth						)						):
														if config.cls_token[idx]:
																					_lowerCAmelCase  	=			list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE						)
														for cnt in range(config.depth[idx]						):
																					_lowerCAmelCase  	=			list_of_state_dict + attention(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(SCREAMING_SNAKE_CASE						)
							for i in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			original_weights[list_of_state_dict[i][1]]

							model.load_state_dict(SCREAMING_SNAKE_CASE						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
	_snake_case          =						argparse.ArgumentParser()
	parser.add_argument(
	    '''--cvt_model''',
	    default='''cvt-w24''',
	    type=str,
	    help='''Name of the cvt model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--image_size''',
	    default=3_8_4,
	    type=int,
	    help='''Input Image Size''',
	)
	parser.add_argument(
	    '''--cvt_file_name''',
	    default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
	    type=str,
	    help='''Input Image Size''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)

	_snake_case          =						parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






