



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        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Optional[int]								=     WavaVecaPhonemeCTCTokenizer
				A_   :     List[Any]								=     False


				def __UpperCamelCase						(							self      :  int       )		->     int:
											super().setUp()

											A      						=    (
											    '<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(' '       )
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )

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

											A      						=    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      :  Tuple						, __UpperCamelCase      :  int						, __UpperCamelCase      :  str=False						, __UpperCamelCase      :  Optional[int]=20						, __UpperCamelCase      :  Any=5       )		->     Tuple[str, list]:
											A      						=    [(i, tokenizer.decode([i]						, clean_up_tokenization_spaces=__UpperCamelCase       )) for i in range(len(__UpperCamelCase       )       )]
											A      						=    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:
																		A      						=    toks[:max_length]
											if min_length is not None and len(__UpperCamelCase       ) < min_length and len(__UpperCamelCase       ) > 0:
																		while len(__UpperCamelCase       ) < min_length:
																									A      						=    toks + toks
        # toks_str = [t[1] for t in toks]
											A      						=    [t[0] for t in toks]

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


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  Dict       )		->     str:
											kwargs.update(self.special_tokens_map       )
											return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


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

											# check adding a single token
											tokenizer.add_tokens('xxx'       )
											A      						=    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']       )
											A      						=    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

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


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

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


				def __UpperCamelCase						(							self      :  int       )		->     int:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

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


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

											A      						=    tokenizer.decode(tokenizer(__UpperCamelCase       ).input_ids       )

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

											A      						=    [
											    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
											    [24, 22, 5, 24, 22, 5, 77],
											]
											A      						=    tokenizer.decode(sample_ids[0]       )
											A      						=    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      :  str       )		->     Optional[int]:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

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


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

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


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

											# fmt: off
											A      						=    [
											    [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
											A      						=    tokenizer.decode(sample_ids[0]       )
											A      						=    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
											A      						=    tokenizer.decode(sample_ids[0]						, filter_word_delimiter_token=__UpperCamelCase       )
											A      						=    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      :  Union[str, Any]       )		->     Tuple:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )

											A      						=    tokenizer.decode(tokenizer(__UpperCamelCase       ).input_ids						, filter_word_delimiter_token=__UpperCamelCase       )

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


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

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )

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

											A      						=    tokenizer(__UpperCamelCase						, phonemizer_lang='en-us'       ).input_ids
											A      						=    tokenizer(__UpperCamelCase						, phonemizer_lang='fr-fr'       ).input_ids

											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    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]       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )
											A      						=    'Hello how Are you'
											A      						=    'hello how are you'

											A      						=    tokenizer(__UpperCamelCase       ).input_ids
											A      						=    tokenizer(__UpperCamelCase       ).input_ids

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


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

											# fmt: off
											A      						=    [
											    [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

											A      						=    tokenizer.batch_decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']       )


				@staticmethod
				def __UpperCamelCase						(							__UpperCamelCase      :  Dict						, __UpperCamelCase      :  Dict       )		->     Optional[Any]:
											A      						=    [d[key] for d in offsets]
											return retrieved_list


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											A      						=    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"
											A      						=    [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

											A      						=    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]       )		->     List[str]:
											A      						=    self.get_tokenizer(word_delimiter_token='|'       )

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

																		# transform list to ModelOutput
																		A      						=    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      :  List[Any]						, __UpperCamelCase      :  Optional[int]       ):
																									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
											A      						=    [
											    [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
											A      						=    tokenizer.batch_decode(__UpperCamelCase						, output_char_offsets=__UpperCamelCase       )
											A      						=    [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      :  List[str]       )		->     int:
											pass


				@unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'       )
				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											pass


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


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


				def __UpperCamelCase						(							self      :  Dict       )		->     Tuple:
											A      						=    self.get_tokenizers(do_lower_case=__UpperCamelCase       )
											for tokenizer in tokenizers:
																		with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
																									A      						=    tokenizer.vocab_size
																									A      						=    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)

																									A      						=    ['aaaaa bbbbbb', 'cccccccccdddddddd']
																									A      						=    tokenizer.add_tokens(__UpperCamelCase       )
																									A      						=    tokenizer.vocab_size
																									A      						=    len(__UpperCamelCase       )

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

																									A      						=    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       )

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

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

																									A      						=    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      :  Dict       )		->     Union[str, Any]:
											pass


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


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

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




import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     VideoToVideoSDPipeline
				A_   :     Optional[Any]								=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'}       ) - {'image', 'width', 'height'}
				A_   :     List[str]								=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'}       ) - {'image'}
				A_   :     Tuple								=     PipelineTesterMixin.required_optional_params - {'latents'}
				A_   :     Dict								=     False

				# No `output_type`.
				A_   :     str								=     frozenset(
				    [
				        'num_inference_steps',
				        'generator',
				        'latents',
				        'return_dict',
				        'callback',
				        'callback_steps',
				    ]       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[Any]:
											torch.manual_seed(0       )
											A      						=    UNetaDConditionModel(
											    block_out_channels=(32, 64, 64, 64)						, layers_per_block=2						, sample_size=32						, in_channels=4						, out_channels=4						, down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')						, up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')						, cross_attention_dim=32						, attention_head_dim=4						, )
											A      						=    DDIMScheduler(
											    beta_start=0.0_0_0_8_5						, beta_end=0.0_1_2						, beta_schedule='scaled_linear'						, clip_sample=__UpperCamelCase						, set_alpha_to_one=__UpperCamelCase						, )
											torch.manual_seed(0       )
											A      						=    AutoencoderKL(
											    block_out_channels=[32, 64]						, in_channels=3						, out_channels=3						, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						, up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						, latent_channels=4						, sample_size=128						, )
											torch.manual_seed(0       )
											A      						=    CLIPTextConfig(
											    bos_token_id=0						, eos_token_id=2						, hidden_size=32						, intermediate_size=37						, layer_norm_eps=1e-05						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=1_000						, hidden_act='gelu'						, projection_dim=512						, )
											A      						=    CLIPTextModel(__UpperCamelCase       )
											A      						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

											A      						=    {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  str=0       )		->     Optional[int]:
											# 3 frames
											A      						=    floats_tensor((1, 3, 3, 32, 32)						, rng=random.Random(__UpperCamelCase       )       ).to(__UpperCamelCase       )

											if str(__UpperCamelCase       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(__UpperCamelCase       )
											else:
																		A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
											A      						=    {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'video': video,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'pt',
											}
											return inputs


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
											A      						=    self.get_dummy_components()
											A      						=    VideoToVideoSDPipeline(**__UpperCamelCase       )
											A      						=    sd_pipe.to(__UpperCamelCase       )
											sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    'np'
											A      						=    sd_pipe(**__UpperCamelCase       ).frames
											A      						=    frames[0][-3:, -3:, -1]

											assert frames[0].shape == (32, 32, 3)
											A      						=    np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()						, reason='XFormers attention is only available with CUDA and `xformers` installed'						, )
				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCamelCase						, expected_max_diff=5e-3       )


				@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'       )
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											pass


				@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'       )
				def __UpperCamelCase						(							self      :  str       )		->     Any:
											pass


				@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'       )
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											pass


				def __UpperCamelCase						(							self      :  Any       )		->     Any:
											return super().test_progress_bar()





@slow
@skip_mps
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											A      						=    VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'						, torch_dtype=torch.floataa       )
											pipe.enable_model_cpu_offload()

											# 10 frames
											A      						=    torch.Generator(device='cpu'       ).manual_seed(0       )
											A      						=    torch.randn((1, 10, 3, 1_024, 576)						, generator=__UpperCamelCase       )
											A      						=    video.to('cuda'       )

											A      						=    'Spiderman is surfing'

											A      						=    pipe(__UpperCamelCase						, video=__UpperCamelCase						, generator=__UpperCamelCase						, num_inference_steps=3						, output_type='pt'       ).frames

											A      						=    np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]       )
											assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1e-2