






"""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  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      WavaVecaPhonemeCTCTokenizer
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE_						 = (
									    '''<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_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )

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

									SCREAMING_SNAKE_CASE_						 = 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(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=20       ,       SCREAMING_SNAKE_CASE_=5    ):

									"""simple docstring"""







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

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

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

									# check adding a single token
									tokenizer.add_tokens('''xxx'''    )
									SCREAMING_SNAKE_CASE_						 = tokenizer('''m xxx ɪ'''       ,       do_phonemize=SCREAMING_SNAKE_CASE_    ).input_ids
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       [13, 3_92, 17]    )  # xxx should be last token

									tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc''']    )
									SCREAMING_SNAKE_CASE_						 = tokenizer('''m aaa ɪ ccc'''       ,       do_phonemize=SCREAMING_SNAKE_CASE_    ).input_ids
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       [13, 3_93, 17, 3_95]    )  # aaa and ccc should be after xxx and 2 after aaa

									SCREAMING_SNAKE_CASE_						 = tokenizer('''maɪ c'''       ,       do_phonemize=SCREAMING_SNAKE_CASE_    ).input_ids
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       [3, 2_00]    )  # mai should be <unk> (=3)

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE_    ).input_ids    )

									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

									# fmt: off
									SCREAMING_SNAKE_CASE_						 = [
									    [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_						 = tokenizer.decode(sample_ids[0]    )
									SCREAMING_SNAKE_CASE_						 = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    )
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       batch_tokens[0]    )
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']    )

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE_    ).input_ids       ,       filter_word_delimiter_token=SCREAMING_SNAKE_CASE_    )

									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE_    ).input_ids       ,       filter_word_delimiter_token=SCREAMING_SNAKE_CASE_    )

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

									self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(SCREAMING_SNAKE_CASE_    )

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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft'''    )
									SCREAMING_SNAKE_CASE_						 = '''Hello how Are you'''
									SCREAMING_SNAKE_CASE_						 = '''hello how are you'''

									SCREAMING_SNAKE_CASE_						 = tokenizer(SCREAMING_SNAKE_CASE_    ).input_ids
									SCREAMING_SNAKE_CASE_						 = tokenizer(SCREAMING_SNAKE_CASE_    ).input_ids

									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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_						 = [
									    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
									    [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
									]
									# fmt: on

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

							@staticmethod
							def _lowercase					(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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_						 = [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_						 = tokenizer.decode(SCREAMING_SNAKE_CASE_       ,       output_char_offsets=SCREAMING_SNAKE_CASE_       ,       filter_word_delimiter_token=SCREAMING_SNAKE_CASE_    )
									# 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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )

									# 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 _lowercase					(self    ):

									"""simple docstring"""







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

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

											# transform list to ModelOutput
											SCREAMING_SNAKE_CASE_						 = 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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
															[recursive_check(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for la, la in zip(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )]
													self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

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

        # fmt: off
									SCREAMING_SNAKE_CASE_						 = [
									    [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_						 = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,       output_char_offsets=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = [tokenizer.decode(SCREAMING_SNAKE_CASE_       ,       output_char_offsets=SCREAMING_SNAKE_CASE_    ) for ids in sample_ids]
									check_list_tuples_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

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

									"""simple docstring"""







									pass

							@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

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

									"""simple docstring"""







									pass

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

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_    )
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'    ):
													SCREAMING_SNAKE_CASE_						 = tokenizer.vocab_size
													SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

													self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,       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_						 = ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
													SCREAMING_SNAKE_CASE_						 = tokenizer.add_tokens(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.vocab_size
													SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

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

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

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

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

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

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

													self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE_    )       ,       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 _lowercase					(self    ):

									"""simple docstring"""







									pass

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

									"""simple docstring"""







									pass







							def _lowercase					(self    ):

									"""simple docstring"""







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

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

"""simple docstring"""




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
						import faiss





@require_faiss
class  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_						 = 8

									# DPR tok
									SCREAMING_SNAKE_CASE_						 = [
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )
									os.makedirs(SCREAMING_SNAKE_CASE_       ,       exist_ok=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       DPR_VOCAB_FILES_NAMES['''vocab_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

									# BART tok
									SCREAMING_SNAKE_CASE_						 = [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''bart_tokenizer'''    )
									os.makedirs(SCREAMING_SNAKE_CASE_       ,       exist_ok=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       BART_VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       BART_VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''bart_tokenizer'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									shutil.rmtree(self.tmpdirname    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = Dataset.from_dict(
									    {
									        '''id''': ['''0''', '''1'''],
									        '''text''': ['''foo''', '''bar'''],
									        '''title''': ['''Foo''', '''Bar'''],
									        '''embeddings''': [np.ones(self.retrieval_vector_size    ), 2 * np.ones(self.retrieval_vector_size    )],
									    }    )
									dataset.add_faiss_index('''embeddings'''       ,       string_factory='''Flat'''       ,       metric_type=faiss.METRIC_INNER_PRODUCT    )
									return dataset

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       )
									with patch('''transformers.models.rag.retrieval_rag.load_dataset'''    ) as mock_load_dataset:
											SCREAMING_SNAKE_CASE_						 = dataset
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									return retriever

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''custom'''       ,       )
									if from_disk:
											SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dataset'''    )
											SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''index.faiss'''    )
											dataset.get_index('''embeddings'''    ).save(os.path.join(self.tmpdirname       ,       '''index.faiss'''    )    )
											dataset.drop_index('''embeddings'''    )
											dataset.save_to_disk(os.path.join(self.tmpdirname       ,       '''dataset'''    )    )
											del dataset
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       index=CustomHFIndex(config.retrieval_vector_size       ,       SCREAMING_SNAKE_CASE_    )       ,       )
									return retriever

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = Dataset.from_dict(
									    {
									        '''id''': ['''0''', '''1'''],
									        '''text''': ['''foo''', '''bar'''],
									        '''title''': ['''Foo''', '''Bar'''],
									        '''embeddings''': [np.ones(self.retrieval_vector_size + 1    ), 2 * np.ones(self.retrieval_vector_size + 1    )],
									    }    )
									dataset.add_faiss_index('''embeddings'''       ,       string_factory='''Flat'''       ,       metric_type=faiss.METRIC_INNER_PRODUCT    )

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''    )
									dataset.save_faiss_index('''embeddings'''       ,       index_file_name + '''.index.dpr'''    )
									pickle.dump(dataset['''id''']       ,       open(index_file_name + '''.index_meta.dpr'''       ,       '''wb'''    )    )

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''psgs_w100.tsv.pkl'''    )
									SCREAMING_SNAKE_CASE_						 = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
									pickle.dump(SCREAMING_SNAKE_CASE_       ,       open(SCREAMING_SNAKE_CASE_       ,       '''wb'''    )    )

									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''legacy'''       ,       index_path=self.tmpdirname       ,       )
									SCREAMING_SNAKE_CASE_						 = RagRetriever(
									    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()    )
									return retriever

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(doc_dicts[0]['''id'''][0]       ,       '''1'''    )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0]       ,       '''0'''    )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											with patch('''transformers.models.rag.retrieval_rag.load_dataset'''    ) as mock_load_dataset:
													SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
													retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
													self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = np.array(
													    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
													SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
													self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(doc_dicts[0]['''id'''][0]       ,       '''1'''    )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0]       ,       '''0'''    )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(doc_dicts[0]['''id'''][0]       ,       '''1'''    )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0]       ,       '''0'''    )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''text''']    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(doc_dicts[0]['''text'''][0]       ,       '''bar'''    )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''text'''][0]       ,       '''foo'''    )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = retriever(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (
									    out['''context_input_ids'''],
									    out['''context_attention_mask'''],
									    out['''retrieved_doc_embeds'''],
									)
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       np.ndarray    )

									SCREAMING_SNAKE_CASE_						 = retriever(
									    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''       ,       )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (  # noqa: F841
									    out['''context_input_ids'''],
									    out['''context_attention_mask'''],
									    out['''retrieved_doc_embeds'''],
									    out['''doc_ids'''],
									)
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )







							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dpr_ctx_encoder_tokenizer()
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = retriever(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_    )

									self.assertEqual(
									    len(SCREAMING_SNAKE_CASE_    )       ,       6    )  # check whether the retriever output consist of 6 attributes including tokenized docs
									self.assertEqual(
									    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')    )       ,       SCREAMING_SNAKE_CASE_    )  # check for doc token related keys in dictionary.