


import json
import os
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		WavaVecaPhonemeCTCTokenizer
				SCREAMING_SNAKE_CASE__				:			Tuple						=		False


				def 		__magic_name__(       self			:Any	)		->			Optional[Any]:
										super().setUp()

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

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

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


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

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


				def 		__magic_name__(       self			:Optional[int]			,			**lowerCAmelCase__			:Dict	)		->			Dict:
										kwargs.update(self.special_tokens_map	)
										return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:Any	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)

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

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

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


				def 		__magic_name__(       self			:Any	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Any					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)

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


				def 		__magic_name__(       self			:Optional[int]	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''Hello how are you'''
										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.phonemize(lowerCAmelCase__			,			phonemizer_lang='''en-us'''	)
										self.assertEqual(tokenizer(lowerCAmelCase__	).input_ids			,			tokenizer(lowerCAmelCase__			,			do_phonemize=lowerCAmelCase__	).input_ids	)


				def 		__magic_name__(       self			:Optional[int]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Any					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		'''Hello how are you'''
										__SCREAMING_SNAKE_CASE						:     Any					=		tokenizer.phonemize(lowerCAmelCase__			,			phonemizer_lang='''en-us'''	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer.decode(tokenizer(lowerCAmelCase__	).input_ids	)

										self.assertEqual(lowerCAmelCase__			,			lowerCAmelCase__	)


				def 		__magic_name__(       self			:Any	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)

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


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

										__SCREAMING_SNAKE_CASE						:     List[str]					=		'''Hello how are you'''
										__SCREAMING_SNAKE_CASE						:     List[str]					=		tokenizer.phonemize(lowerCAmelCase__			,			phonemizer_lang='''en-us'''	)
										self.assertEqual(lowerCAmelCase__			,			'''h ə l oʊ | h aʊ | ɑːɹ | j uː |'''	)


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

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


				def 		__magic_name__(       self			:str	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     int					=		self.tokenizer_class.from_pretrained(
										    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''			,			word_delimiter_token='''|'''	)
										tokenizer.add_tokens('''|'''	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     List[str]					=		tokenizer.decode(sample_ids[0]	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tokenizer.batch_decode(lowerCAmelCase__	)
										self.assertEqual(lowerCAmelCase__			,			batch_tokens[0]	)
										self.assertEqual(lowerCAmelCase__			,			['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']	)

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


				def 		__magic_name__(       self			:Any	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.tokenizer_class.from_pretrained(
										    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''			,			word_delimiter_token='''|'''	)
										tokenizer.add_tokens('''|'''	)

										__SCREAMING_SNAKE_CASE						:     Any					=		'''Hello how are you'''
										__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer.phonemize(lowerCAmelCase__			,			phonemizer_lang='''en-us'''	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.decode(tokenizer(lowerCAmelCase__	).input_ids			,			filter_word_delimiter_token=lowerCAmelCase__	)

										self.assertEqual(lowerCAmelCase__			,			lowerCAmelCase__	)


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

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

										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.decode(tokenizer(lowerCAmelCase__	).input_ids			,			filter_word_delimiter_token=lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.tokenizer_class.from_pretrained(
										    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''			,			word_delimiter_token=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		'''Hello how are you'''

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tokenizer(lowerCAmelCase__			,			phonemizer_lang='''en-us'''	).input_ids
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tokenizer(lowerCAmelCase__			,			phonemizer_lang='''fr-fr'''	).input_ids

										self.assertNotEqual(lowerCAmelCase__			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tokenizer.decode(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tokenizer.decode(lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Any	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Any					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)
										__SCREAMING_SNAKE_CASE						:     str					=		'''Hello how Are you'''
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		'''hello how are you'''

										__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer(lowerCAmelCase__	).input_ids
										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer(lowerCAmelCase__	).input_ids

										self.assertEqual(lowerCAmelCase__			,			lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''	)
										tokenizer.add_tokens(['''!''', '''?''']	)
										tokenizer.add_special_tokens({'''cls_token''': '''$$$'''}	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		[
										    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
										    [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
										]
										# fmt: on

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


				@staticmethod
				def 		__magic_name__(       lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Union[str, Any]	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		[d[key] for d in offsets]
										return retrieved_list


				def 		__magic_name__(       self			:int	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Union[str, 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"
										__SCREAMING_SNAKE_CASE						:     Any					=		[11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
										# fmt: on

										__SCREAMING_SNAKE_CASE						:     Any					=		tokenizer.decode(lowerCAmelCase__			,			output_char_offsets=lowerCAmelCase__			,			filter_word_delimiter_token=lowerCAmelCase__	)
										# check Wav2Vec2CTCTokenizerOutput keys for char
										self.assertEqual(len(outputs.keys()	)			,			2	)
										self.assertTrue('''text''' in outputs	)
										self.assertTrue('''char_offsets''' in outputs	)
										self.assertTrue(isinstance(lowerCAmelCase__			,			lowerCAmelCase__	)	)

										# check that order of chars is correct and identical for both outputs
										self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''char_offsets''']			,			'''char'''	)	)			,			outputs.text	)
										self.assertListEqual(
										    self.get_from_offsets(outputs['''char_offsets''']			,			'''char'''	)			,			['''k''', '''s''', '''ɾ''', '''ɾ''', '''|''', '''ɾ''', '''l''', '''|''', '''ɭʲ''']	)

										# check that offsets are actually correct for char
										# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
										# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
										self.assertListEqual(
										    self.get_from_offsets(outputs['''char_offsets''']			,			'''start_offset'''	)			,			[0, 1, 4, 7, 9, 11, 12, 15, 16]	)
										self.assertListEqual(
										    self.get_from_offsets(outputs['''char_offsets''']			,			'''end_offset'''	)			,			[1, 4, 6, 9, 10, 12, 15, 16, 17]	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.get_tokenizer(word_delimiter_token='''|'''	)

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

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

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

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

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

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

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

										# char
										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.batch_decode(lowerCAmelCase__			,			output_char_offsets=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		[tokenizer.decode(lowerCAmelCase__			,			output_char_offsets=lowerCAmelCase__	) for ids in sample_ids]
										check_list_tuples_equal(lowerCAmelCase__			,			lowerCAmelCase__	)


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


				@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'''	)
				def 		__magic_name__(       self			:int	)		->			str:
										pass


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


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


				def 		__magic_name__(       self			:Optional[Any]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.get_tokenizers(do_lower_case=lowerCAmelCase__	)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
																						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.vocab_size
																						__SCREAMING_SNAKE_CASE						:     Any					=		len(lowerCAmelCase__	)

																						self.assertNotEqual(lowerCAmelCase__			,			0	)

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

																						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
																						__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer.add_tokens(lowerCAmelCase__	)
																						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.vocab_size
																						__SCREAMING_SNAKE_CASE						:     Any					=		len(lowerCAmelCase__	)

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

																						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''			,			add_special_tokens=lowerCAmelCase__	)

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

																						__SCREAMING_SNAKE_CASE						:     str					=		{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.add_special_tokens(lowerCAmelCase__	)
																						__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer.vocab_size
																						__SCREAMING_SNAKE_CASE						:     Dict					=		len(lowerCAmelCase__	)

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

																						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer.encode(
																						    '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''			,			add_special_tokens=lowerCAmelCase__	)

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


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


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



				def 		__magic_name__(       self			:Dict	)		->			Any:
										# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
										# is not the case for Wav2Vec2PhonemeCTCTokenizer.
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.get_tokenizers(fast=lowerCAmelCase__			,			do_lower_case=lowerCAmelCase__	)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
																						__SCREAMING_SNAKE_CASE						:     int					=		['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
																						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tokenizer.convert_tokens_to_string(lowerCAmelCase__	)

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




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
						__SCREAMING_SNAKE_CASE						:     List[str]					=		Image.open(requests.get(lowercase__    ,       stream=lowercase__      ).raw      ).convert('''RGB'''      )

						return image


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		[]
						# fmt: off

						# vision encoder
						rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')      )
						rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')      )
						rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')      )
						rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')      )
						rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')      )
						rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')      )

						for i in range(config.vision_config.num_hidden_layers      ):
												rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')      )
												rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')      )

						# QFormer
						rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')      )
						rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')      )

						# fmt: on
						return rename_keys


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						for i in range(config.vision_config.num_hidden_layers      ):
												# read in original q and v biases
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''      )
												__SCREAMING_SNAKE_CASE						:     int					=		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''      )

												# next, set bias in the state dict
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.cat((q_bias, torch.zeros_like(lowercase__    ,       requires_grad=lowercase__      ), v_bias)      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		qkv_bias


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		364 if '''coco''' in model_name else 224
						__SCREAMING_SNAKE_CASE						:     List[str]					=		BlipaVisionConfig(image_size=lowercase__      ).to_dict()

						# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
						# seems like flan-T5 models don't have bos_token_id properly set?
						if "opt-2.7b" in model_name:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		OPTConfig.from_pretrained('''facebook/opt-2.7b'''    ,       eos_token_id=lowercase__      ).to_dict()
						elif "opt-6.7b" in model_name:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		OPTConfig.from_pretrained('''facebook/opt-6.7b'''    ,       eos_token_id=lowercase__      ).to_dict()
						elif "t5-xl" in model_name:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		TaConfig.from_pretrained('''google/flan-t5-xl'''    ,       dense_act_fn='''gelu'''    ,       bos_token_id=1      ).to_dict()
						elif "t5-xxl" in model_name:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		TaConfig.from_pretrained('''google/flan-t5-xxl'''    ,       dense_act_fn='''gelu'''    ,       bos_token_id=1      ).to_dict()

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		BlipaConfig(vision_config=lowercase__    ,       text_config=lowercase__      )

						return config, image_size


@torch.no_grad()
def        _UpperCamelCase							(      lowercase__    ,       lowercase__=None    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		(
						    AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''      )
						    if '''opt''' in model_name
						    else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''      )
						)
						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer('''\n'''    ,       add_special_tokens=lowercase__      ).input_ids[0]
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		get_blipa_config(lowercase__    ,       eos_token_id=lowercase__      )

						__SCREAMING_SNAKE_CASE						:     int					=		BlipaForConditionalGeneration(lowercase__      ).eval()

						__SCREAMING_SNAKE_CASE						:     int					=		{
						    '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
						    '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
						    '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
						    '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
						    '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
						    '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
						    '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
						}

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		model_name_to_original[model_name]

						# load original model
						print('''Loading original model...'''      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		'''cuda''' if torch.cuda.is_available() else '''cpu'''
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		load_model_and_preprocess(
						    name=lowercase__    ,       model_type=lowercase__    ,       is_eval=lowercase__    ,       device=lowercase__      )
						original_model.eval()
						print('''Done!'''      )

						# update state dict keys
						__SCREAMING_SNAKE_CASE						:     List[str]					=		original_model.state_dict()
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		create_rename_keys(lowercase__      )
						for src, dest in rename_keys:
												rename_key(lowercase__    ,       lowercase__    ,       lowercase__      )

						# some keys can be renamed efficiently
						for key, val in state_dict.copy().items():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		state_dict.pop(lowercase__      )
												if key.startswith('''Qformer.bert'''      ):
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		key.replace('''Qformer.bert'''    ,       '''qformer'''      )
												if "attention.self" in key:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		key.replace('''self'''    ,       '''attention'''      )
												if "opt_proj" in key:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		key.replace('''opt_proj'''    ,       '''language_projection'''      )
												if "t5_proj" in key:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		key.replace('''t5_proj'''    ,       '''language_projection'''      )
												if key.startswith('''opt'''      ):
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		key.replace('''opt'''    ,       '''language'''      )
												if key.startswith('''t5'''      ):
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		key.replace('''t5'''    ,       '''language'''      )
												__SCREAMING_SNAKE_CASE						:     Tuple					=		val

						# read in qv biases
						read_in_q_v_bias(lowercase__    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		hf_model.load_state_dict(lowercase__    ,       strict=lowercase__      )
						assert len(lowercase__      ) == 0
						assert unexpected_keys == ["qformer.embeddings.position_ids"]

						__SCREAMING_SNAKE_CASE						:     List[str]					=		load_demo_image()
						__SCREAMING_SNAKE_CASE						:     Any					=		vis_processors['''eval'''](lowercase__      ).unsqueeze(0      ).to(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer(['''\n''']    ,       return_tensors='''pt'''      ).input_ids.to(lowercase__      )

						# create processor
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		BlipImageProcessor(
						    size={'''height''': image_size, '''width''': image_size}    ,       image_mean=lowercase__    ,       image_std=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     int					=		BlipaProcessor(image_processor=lowercase__    ,       tokenizer=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		processor(images=lowercase__    ,       return_tensors='''pt'''      ).pixel_values.to(lowercase__      )

						# make sure processor creates exact same pixel values
						assert torch.allclose(lowercase__    ,       lowercase__      )

						original_model.to(lowercase__      )
						hf_model.to(lowercase__      )
						with torch.no_grad():
												if "opt" in model_name:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}      ).logits
																		__SCREAMING_SNAKE_CASE						:     Dict					=		hf_model(lowercase__    ,       lowercase__      ).logits
												else:
																		__SCREAMING_SNAKE_CASE						:     int					=		original_model(
																		    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}      ).logits
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		input_ids.masked_fill(input_ids == tokenizer.pad_token_id    ,       -100      )
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		hf_model(lowercase__    ,       lowercase__    ,       labels=lowercase__      ).logits

						assert original_logits.shape == logits.shape
						print('''First values of original logits:'''    ,       original_logits[0, :3, :3]      )
						print('''First values of HF logits:'''    ,       logits[0, :3, :3]      )

						# assert values
						if model_name == "blip2-flan-t5-xl":
												__SCREAMING_SNAKE_CASE						:     Dict					=		torch.tensor(
												    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]    ,       device=lowercase__      )
												assert torch.allclose(logits[0, :3, :3]    ,       lowercase__    ,       atol=1e-4      )
						elif model_name == "blip2-flan-t5-xl-coco":
												__SCREAMING_SNAKE_CASE						:     Any					=		torch.tensor(
												    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]    ,       device=lowercase__      )
						else:
												# cast to same type
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		logits.dtype
												assert torch.allclose(original_logits.to(lowercase__      )    ,       lowercase__    ,       atol=1e-2      )
						print('''Looks ok!'''      )

						print('''Generating a caption...'''      )
						__SCREAMING_SNAKE_CASE						:     Any					=		''''''
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(lowercase__    ,       return_tensors='''pt'''      ).input_ids.to(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		original_model.generate({'''image''': original_pixel_values}      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hf_model.generate(
						    lowercase__    ,       lowercase__    ,       do_sample=lowercase__    ,       num_beams=5    ,       max_length=30    ,       min_length=1    ,       top_p=0.9    ,       repetition_penalty=1.0    ,       length_penalty=1.0    ,       temperature=1    ,       )
						print('''Original generation:'''    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		input_ids.shape[1]
						__SCREAMING_SNAKE_CASE						:     Any					=		processor.batch_decode(outputs[:, prompt_length:]    ,       skip_special_tokens=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[text.strip() for text in output_text]
						print('''HF generation:'''    ,       lowercase__      )

						if pytorch_dump_folder_path is not None:
												processor.save_pretrained(lowercase__      )
												hf_model.save_pretrained(lowercase__      )

						if push_to_hub:
												processor.push_to_hub(F'''nielsr/{model_name}'''      )
												hf_model.push_to_hub(F'''nielsr/{model_name}'''      )


if __name__ == "__main__":
						__lowerCAmelCase	: List[str]  				=argparse.ArgumentParser()
						__lowerCAmelCase	: Tuple  				=[
						    'blip2-opt-2.7b',
						    'blip2-opt-6.7b',
						    'blip2-opt-2.7b-coco',
						    'blip2-opt-6.7b-coco',
						    'blip2-flan-t5-xl',
						    'blip2-flan-t5-xl-coco',
						    'blip2-flan-t5-xxl',
						]
						parser.add_argument(
						    '--model_name',
						    default='blip2-opt-2.7b',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model and processor to the hub after converting',
						)

						__lowerCAmelCase	: List[Any]  				=parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
