






"""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 			lowerCAmelCase__			(    __snake_case							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =WavaVecaPhonemeCTCTokenizer
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        super().setUp()

        UpperCAmelCase__						:    Optional[int]							=							(
            '''<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(" "							)
        UpperCAmelCase__						:    Optional[Any]							=							dict(zip(snake_case__							,			range(len(snake_case__							)							)							)							)

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

        UpperCAmelCase__						:    Optional[Any]							=							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(snake_case__							) + "\n"							)
   def       __a							(		self      :	List[Any]							,			snake_case__      :	Optional[Any]							,			snake_case__      :	Optional[Any]=False							,			snake_case__      :	int=2_0							,			snake_case__      :	Tuple=5							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							[(i, tokenizer.decode([i]							,			clean_up_tokenization_spaces=snake_case__							)) for i in range(len(snake_case__							)							)]
        UpperCAmelCase__						:    Optional[Any]							=							list(filter(lambda snake_case__							: [t[0]] == tokenizer.encode(t[1]							,			do_phonemize=snake_case__							)							,			snake_case__							)							)
        if max_length is not None and len(snake_case__							) > max_length:
             UpperCAmelCase__						:    Dict							=							toks[:max_length]
        if min_length is not None and len(snake_case__							) < min_length and len(snake_case__							) > 0:
             while len(snake_case__							) < min_length:
                  UpperCAmelCase__						:    Dict							=							toks + toks
        # toks_str = [t[1] for t in toks]
        UpperCAmelCase__						:    Optional[int]							=							[t[0] for t in toks]

        # Ensure consistency
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer.decode(snake_case__							,			clean_up_tokenization_spaces=snake_case__							)
        if " " not in output_txt and len(snake_case__							) > 1:
             UpperCAmelCase__						:    Tuple							=							(
                 tokenizer.decode([toks_ids[0]]							,			clean_up_tokenization_spaces=snake_case__							)
                 + ''' '''
                 + tokenizer.decode(toks_ids[1:]							,			clean_up_tokenization_spaces=snake_case__							)
             )
        if with_prefix_space:
             UpperCAmelCase__						:    List[str]							=							''' ''' + output_txt
        UpperCAmelCase__						:    Dict							=							tokenizer.encode(snake_case__							,			add_special_tokens=snake_case__							)
        return output_txt, output_ids
   def       __a							(		self      :	Union[str, Any]							,			**snake_case__      :	str							):


        '''simple docstring'''
        kwargs.update(self.special_tokens_map							)
        return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname							,			**snake_case__							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"							)

        # check adding a single token
        tokenizer.add_tokens("xxx"							)
        UpperCAmelCase__						:    Optional[int]							=							tokenizer("m xxx ɪ"							,			do_phonemize=snake_case__							).input_ids
        self.assertEqual(snake_case__							,			[1_3, 3_9_2, 1_7]							)  # xxx should be last token

        tokenizer.add_tokens(["aaa", "bbb", "ccc"]							)
        UpperCAmelCase__						:    Dict							=							tokenizer("m aaa ɪ ccc"							,			do_phonemize=snake_case__							).input_ids
        self.assertEqual(snake_case__							,			[1_3, 3_9_3, 1_7, 3_9_5]							)  # aaa and ccc should be after xxx and 2 after aaa

        UpperCAmelCase__						:    Optional[int]							=							tokenizer("maɪ c"							,			do_phonemize=snake_case__							).input_ids
        self.assertEqual(snake_case__							,			[3, 2_0_0]							)  # mai should be <unk> (=3)
   def       __a							(		self      :	Dict							):


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

        UpperCAmelCase__						:    Any							=							'''Hello how are you'''
        UpperCAmelCase__						:    Any							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)
        self.assertEqual(snake_case__							,			"h ə l oʊ h aʊ ɑːɹ j uː"							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"							)

        UpperCAmelCase__						:    Optional[Any]							=							'''Hello how are you'''
        UpperCAmelCase__						:    str							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)
        self.assertEqual(tokenizer(snake_case__							).input_ids							,			tokenizer(snake_case__							,			do_phonemize=snake_case__							).input_ids							)
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"							)
        UpperCAmelCase__						:    int							=							'''Hello how are you'''
        UpperCAmelCase__						:    str							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)

        UpperCAmelCase__						:    str							=							tokenizer.decode(tokenizer(snake_case__							).input_ids							)

        self.assertEqual(snake_case__							,			snake_case__							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"							)

        UpperCAmelCase__						:    List[str]							=							[
            [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
            [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
        ]
        UpperCAmelCase__						:    str							=							tokenizer.decode(sample_ids[0]							)
        UpperCAmelCase__						:    Optional[int]							=							tokenizer.batch_decode(snake_case__							)
        self.assertEqual(snake_case__							,			batch_tokens[0]							)
        self.assertEqual(snake_case__							,			["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]							)
   def       __a							(		self      :	List[str]							):


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

        UpperCAmelCase__						:    int							=							'''Hello how are you'''
        UpperCAmelCase__						:    List[str]							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)
        self.assertEqual(snake_case__							,			"h ə l oʊ | h aʊ | ɑːɹ | j uː |"							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.tokenizer_class.from_pretrained(
            "facebook/wav2vec2-lv-60-espeak-cv-ft"							,			word_delimiter_token="|"							)
        tokenizer.add_tokens("|"							)

        UpperCAmelCase__						:    Tuple							=							'''Hello how are you'''
        UpperCAmelCase__						:    str							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)
        self.assertEqual(tokenizer(snake_case__							).input_ids							,			tokenizer(snake_case__							,			do_phonemize=snake_case__							).input_ids							)
   def       __a							(		self      :	Dict							):


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

        # fmt: off
        UpperCAmelCase__						:    List[str]							=							[
            [1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
            [tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
        ]
        # fmt: on

        # decode with word_del_token filter
        UpperCAmelCase__						:    Optional[int]							=							tokenizer.decode(sample_ids[0]							)
        UpperCAmelCase__						:    str							=							tokenizer.batch_decode(snake_case__							)
        self.assertEqual(snake_case__							,			batch_tokens[0]							)
        self.assertEqual(snake_case__							,			["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]							)

        # decode with no word_del_token filter
        UpperCAmelCase__						:    str							=							tokenizer.decode(sample_ids[0]							,			filter_word_delimiter_token=snake_case__							)
        UpperCAmelCase__						:    int							=							tokenizer.batch_decode(snake_case__							,			filter_word_delimiter_token=snake_case__							)
        self.assertEqual(snake_case__							,			batch_tokens[0]							)
        self.assertEqual(snake_case__							,			["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"]							)
   def       __a							(		self      :	int							):


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

        UpperCAmelCase__						:    Union[str, Any]							=							'''Hello how are you'''
        UpperCAmelCase__						:    Any							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)

        UpperCAmelCase__						:    Dict							=							tokenizer.decode(tokenizer(snake_case__							).input_ids							,			filter_word_delimiter_token=snake_case__							)

        self.assertEqual(snake_case__							,			snake_case__							)
   def       __a							(		self      :	List[Any]							):


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

        UpperCAmelCase__						:    str							=							'''Hello how are you'''
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer.phonemize(snake_case__							,			phonemizer_lang="en-us"							)

        UpperCAmelCase__						:    Union[str, Any]							=							tokenizer.decode(tokenizer(snake_case__							).input_ids							,			filter_word_delimiter_token=snake_case__							)

        self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |"							)]							).strip()							,			snake_case__							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.tokenizer_class.from_pretrained(
            "facebook/wav2vec2-lv-60-espeak-cv-ft"							,			word_delimiter_token=snake_case__							)
        UpperCAmelCase__						:    Any							=							'''Hello how are you'''

        UpperCAmelCase__						:    Tuple							=							tokenizer(snake_case__							,			phonemizer_lang="en-us"							).input_ids
        UpperCAmelCase__						:    Dict							=							tokenizer(snake_case__							,			phonemizer_lang="fr-fr"							).input_ids

        self.assertNotEqual(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							tokenizer.decode(snake_case__							)
        UpperCAmelCase__						:    int							=							tokenizer.decode(snake_case__							)

        self.assertEqual(snake_case__							,			"h ə l oʊ h aʊ ɑːɹ j uː"							)
        self.assertEqual(snake_case__							,			"ɛ l o h aʊ a ʁ j u"							)
   def       __a							(		self      :	List[Any]							):


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

        UpperCAmelCase__						:    Optional[Any]							=							tokenizer(snake_case__							).input_ids
        UpperCAmelCase__						:    int							=							tokenizer(snake_case__							).input_ids

        self.assertEqual(snake_case__							,			snake_case__							)
   def       __a							(		self      :	List[str]							):


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

        # fmt: off
        UpperCAmelCase__						:    List[Any]							=							[
            [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
            [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
        ]
        # fmt: on

        UpperCAmelCase__						:    Dict							=							tokenizer.batch_decode(snake_case__							)
        self.assertEqual(snake_case__							,			["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]							)
   @staticmethod
   def       __a							(		snake_case__      :	Any							,			snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							[d[key] for d in offsets]
        return retrieved_list
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[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"
        UpperCAmelCase__						:    Tuple							=							[1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
        # fmt: on

        UpperCAmelCase__						:    List[Any]							=							tokenizer.decode(snake_case__							,			output_char_offsets=snake_case__							,			filter_word_delimiter_token=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(snake_case__							,			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, 1_1, 1_2, 1_5, 1_6]							)
        self.assertListEqual(
            self.get_from_offsets(outputs["char_offsets"]							,			"end_offset"							)							,			[1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7]							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							self.get_tokenizer(word_delimiter_token="|"							)

        def check_list_tuples_equal(snake_case__      :	List[str]							,			snake_case__      :	Optional[int]							):
             self.assertTrue(isinstance(snake_case__							,			snake_case__							)							)
             self.assertTrue(isinstance(outputs_list[0]							,			snake_case__							)							)

             # transform list to ModelOutput
             UpperCAmelCase__						:    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(snake_case__      :	Dict							,			snake_case__      :	Union[str, Any]							):
                  if isinstance(snake_case__							,			snake_case__							):
                       [recursive_check(snake_case__							,			snake_case__							) for la, la in zip(snake_case__							,			snake_case__							)]
                  self.assertEqual(snake_case__							,			snake_case__							)

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

        # fmt: off
        UpperCAmelCase__						:    Union[str, Any]							=							[
            [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
            [2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
        ]
        # 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
        UpperCAmelCase__						:    Optional[int]							=							tokenizer.batch_decode(snake_case__							,			output_char_offsets=snake_case__							)
        UpperCAmelCase__						:    str							=							[tokenizer.decode(snake_case__							,			output_char_offsets=snake_case__							) for ids in sample_ids]
        check_list_tuples_equal(snake_case__							,			snake_case__							)
   @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        pass
   @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        pass
   @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        pass
   @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        pass
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							self.get_tokenizers(do_lower_case=snake_case__							)
        for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'							):
                  UpperCAmelCase__						:    List[str]							=							tokenizer.vocab_size
                  UpperCAmelCase__						:    Union[str, Any]							=							len(snake_case__							)

                  self.assertNotEqual(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)

                  UpperCAmelCase__						:    List[Any]							=							['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
                  UpperCAmelCase__						:    int							=							tokenizer.add_tokens(snake_case__							)
                  UpperCAmelCase__						:    Optional[Any]							=							tokenizer.vocab_size
                  UpperCAmelCase__						:    str							=							len(snake_case__							)

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

                  UpperCAmelCase__						:    str							=							tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"							,			add_special_tokens=snake_case__							)

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

                  UpperCAmelCase__						:    Tuple							=							{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
                  UpperCAmelCase__						:    int							=							tokenizer.add_special_tokens(snake_case__							)
                  UpperCAmelCase__						:    Dict							=							tokenizer.vocab_size
                  UpperCAmelCase__						:    List[Any]							=							len(snake_case__							)

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

                  UpperCAmelCase__						:    Tuple							=							tokenizer.encode(
                      ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"							,			add_special_tokens=snake_case__							)

                  self.assertGreaterEqual(len(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       __a							(		self      :	Dict							):


        '''simple docstring'''
        pass
   @unittest.skip("The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode."							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        pass




   def       __a							(		self      :	Dict							):


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

                  self.assertIsInstance(output["text"]							,			snake_case__							)




'''simple docstring'''




import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    if model.config.model_type == "gpt2":
        return model.transformer.h[0].mlp.c_fc
    return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
      import torch
      import torch.nn as nn





      class 					A							(				nn.Module		):





           def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






               """simple docstring"""






               super().__init__()
               A			:  List[Any]   =		module
               A			:  Any   =		nn.Sequential(
                   nn.Linear(module.in_features		,     SCREAMING_SNAKE_CASE		,     bias=SCREAMING_SNAKE_CASE      )		,     nn.Linear(SCREAMING_SNAKE_CASE		,     module.out_features		,     bias=SCREAMING_SNAKE_CASE      )		,     )
               A			:  List[str]   =		(2.0 / (5 * min(module.in_features		,     module.out_features      ))) ** 0.5
               nn.init.normal_(self.adapter[0].weight		,     std=SCREAMING_SNAKE_CASE      )
               nn.init.zeros_(self.adapter[1].weight      )
               self.adapter.to(module.weight.device      )





           def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     *SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )					->							str:






               """simple docstring"""






               return self.module(SCREAMING_SNAKE_CASE		,     *SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      ) + self.adapter(SCREAMING_SNAKE_CASE      )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					A							(				unittest.TestCase		):
     # We keep the constants inside the init function and model loading inside setUp function

     # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
     # Therefore here we use only bloom-1b3 to test our module
     __magic_name__          =						'''bigscience/bloom-1b7'''

     # Constant values
     __magic_name__          =						2.1_09_65_95_52_69_25_74

     __magic_name__          =						'''Hello my name is'''
     __magic_name__          =						set()
     EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''		)
     EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''		)
     EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''		)
     __magic_name__          =						10





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Any   =		AutoTokenizer.from_pretrained(self.model_name      )





class 					A							(				__snake_case		):





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






         """simple docstring"""






         super().setUp()

         # Models and tokenizer
         A			:  Optional[Any]   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     torch_dtype=torch.floataa		,     device_map='''auto'''      )
         A			:  List[Any]   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.model_fpaa
         del self.model_abit

         gc.collect()
         torch.cuda.empty_cache()





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






         """simple docstring"""






         A			:  Any   =		self.model_abit.config

         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,     '''quantization_config'''      )      )

         A			:  List[str]   =		config.to_dict()
         A			:  List[Any]   =		config.to_diff_dict()

         A			:  Union[str, Any]   =		config.to_json_string()





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         from bitsandbytes.nn import Paramsabit

         A			:  Optional[Any]   =		self.model_fpaa.get_memory_footprint()
         A			:  Any   =		self.model_abit.get_memory_footprint()

         self.assertAlmostEqual(mem_fpaa / mem_abit		,     self.EXPECTED_RELATIVE_DIFFERENCE      )
         A			:  Union[str, Any]   =		get_some_linear_layer(self.model_abit      )
         self.assertTrue(linear.weight.__class__ == Paramsabit      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         from transformers import TaPreTrainedModel

         self.model_fpaa.get_memory_footprint()
         self.model_abit.get_memory_footprint()

         for name, module in self.model_abit.named_modules():
             if isinstance(SCREAMING_SNAKE_CASE		,     torch.nn.Linear      ):
                 if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                     # 4-bit parameters are packed in uint8 variables
                     self.assertTrue(module.weight.dtype == torch.uinta      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  List[str]   =		self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0      )		,     max_new_tokens=10      )

         self.assertIn(self.tokenizer.decode(output_sequences[0]		,     skip_special_tokens=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





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






         """simple docstring"""






         A			:  str   =		BitsAndBytesConfig()
         A			:  Optional[Any]   =		True

         A			:  int   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )

         A			:  Union[str, Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  Tuple   =		model_abit_from_config.generate(
             input_ids=encoded_input['''input_ids'''].to(0      )		,     max_new_tokens=10      )

         self.assertIn(self.tokenizer.decode(output_sequences[0]		,     skip_special_tokens=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





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






         """simple docstring"""






         with self.assertRaises(SCREAMING_SNAKE_CASE      ), tempfile.TemporaryDirectory() as tmpdirname:
             self.model_abit.save_pretrained(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		BitsAndBytesConfig()

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             A			:  int   =		AutoModelForCausalLM.from_pretrained(
                 self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''		,     bnb_abit_quant_type='''nf4'''		,     )





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






         """simple docstring"""






         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with `str`
             self.model_abit.to('''cpu'''      )

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with a `dtype``
             self.model_abit.to(torch.floataa      )

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with a `device`
             self.model_abit.to(torch.device('''cuda:0'''      )      )

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with a `device`
             self.model_abit.float()

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with a `device`
             self.model_abit.half()

         # Test if we did not break anything
         A			:  int   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         A			:  Dict   =		self.model_fpaa.to(torch.floataa      )
         A			:  Union[str, Any]   =		self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0      )		,     max_new_tokens=10      )

         # Check this does not throw an error
         A			:  str   =		self.model_fpaa.to('''cpu'''      )

         # Check this does not throw an error
         A			:  List[str]   =		self.model_fpaa.half()

         # Check this does not throw an error
         A			:  Tuple   =		self.model_fpaa.float()





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Tuple   =		AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa      )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					A							(				unittest.TestCase		):





     @classmethod
     def 							__lowerCAmelCase				(				cls      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  int   =		'''t5-small'''
         A			:  Any   =		'''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
         A			:  List[Any]   =		AutoTokenizer.from_pretrained(cls.model_name      )
         A			:  List[str]   =		'''Translate in German: Hello, my dog is cute'''





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         gc.collect()
         torch.cuda.empty_cache()





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






         """simple docstring"""






         from transformers import TaForConditionalGeneration

         A			:  int   =		TaForConditionalGeneration._keep_in_fpaa_modules
         A			:  Optional[Any]   =		None

         # test with `t5-small`
         A			:  Optional[Any]   =		TaForConditionalGeneration.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         A			:  str   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  List[Any]   =		model.generate(**SCREAMING_SNAKE_CASE      )

         # test with `flan-t5-small`
         A			:  Any   =		TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         A			:  Optional[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  List[Any]   =		model.generate(**SCREAMING_SNAKE_CASE      )
         A			:  str   =		modules





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






         """simple docstring"""






         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         A			:  List[str]   =		TaForConditionalGeneration.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )

         # there was a bug with decoders - this test checks that it is fixed
         self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q		,     bnb.nn.Linearabit      )      )

         A			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  Any   =		model.generate(**SCREAMING_SNAKE_CASE      )

         # test with `flan-t5-small`
         A			:  str   =		TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         A			:  Optional[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  Tuple   =		model.generate(**SCREAMING_SNAKE_CASE      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()
         # model_name
         A			:  Tuple   =		'''bigscience/bloom-560m'''
         A			:  int   =		'''t5-small'''

         # Different types of model

         A			:  Optional[int]   =		AutoModel.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         # Sequence classification model
         A			:  Optional[Any]   =		AutoModelForSequenceClassification.from_pretrained(
             self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         # CausalLM model
         A			:  List[str]   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         # Seq2seq model
         A			:  Optional[int]   =		AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.base_model
         del self.sequence_model
         del self.model_abit
         del self.seq_to_seq_model

         gc.collect()
         torch.cuda.empty_cache()





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






         """simple docstring"""






         from bitsandbytes.nn import Paramsabit

         self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit      )

         # Other heads should be nn.Parameter
         self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter      )
         self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter      )
         self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.pipe

         gc.collect()
         torch.cuda.empty_cache()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Optional[Any]   =		pipeline(
             '''text-generation'''		,     model=self.model_name		,     model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa}		,     max_new_tokens=self.MAX_NEW_TOKENS		,     )

         # Real second forward pass
         A			:  str   =		self.pipe(self.input_text      )
         self.assertIn(pipeline_output[0]['''generated_text''']		,     self.EXPECTED_OUTPUTS      )





@require_torch_multi_gpu
class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''balanced'''      )

         # Check correct device map
         self.assertEqual(set(model_parallel.hf_device_map.values()      )		,     {0, 1}      )

         # Check that inference pass works on the model
         A			:  Optional[int]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         # Second real batch
         A			:  Optional[Any]   =		model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0      )		,     max_new_tokens=10      )
         self.assertIn(self.tokenizer.decode(output_parallel[0]		,     skip_special_tokens=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  int   =		'''facebook/opt-350m'''
         super().setUp()





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






         """simple docstring"""






         if version.parse(importlib.metadata.version('''bitsandbytes'''      )      ) < version.parse('''0.37.0'''      ):
             return

         # Step 1: freeze all parameters
         A			:  Tuple   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE      )

         self.assertEqual(set(model.hf_device_map.values()      )		,     {torch.cuda.current_device()}      )

         for param in model.parameters():
             A			:  Optional[int]   =		False  # freeze the model - train adapters later
             if param.ndim == 1:
                 # cast the small parameters (e.g. layernorm) to fp32 for stability
                 A			:  Tuple   =		param.data.to(torch.floataa      )

        # Step 2: add adapters
         for _, module in model.named_modules():
             if "OPTAttention" in repr(type(SCREAMING_SNAKE_CASE      )      ):
                 A			:  Tuple   =		LoRALayer(module.q_proj		,     rank=16      )
                 A			:  Tuple   =		LoRALayer(module.k_proj		,     rank=16      )
                 A			:  str   =		LoRALayer(module.v_proj		,     rank=16      )

        # Step 3: dummy batch
         A			:  Dict   =		self.tokenizer('''Test batch '''		,     return_tensors='''pt'''      ).to(0      )

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
             A			:  str   =		model.forward(**SCREAMING_SNAKE_CASE      )
             out.logits.norm().backward()

         for module in model.modules():
             if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 self.assertTrue(module.adapter[1].weight.grad is not None      )
                 self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0      )
             elif isinstance(SCREAMING_SNAKE_CASE		,     nn.Embedding      ):
                 self.assertTrue(module.weight.grad is None      )





class 					A							(				__snake_case		):
     __magic_name__          =						'''gpt2-xl'''
     __magic_name__          =						3.31_91_85_48_54_15_21_87




