






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 							_UpperCamelCase			(a_							,		unittest.TestCase			):
      snake_case_  =							WavaVecaPhonemeCTCTokenizer
      snake_case_  =							False


      def  __UpperCAmelCase		(	self	)->	str:
           super().setUp()

           __lowerCAmelCase								=					(
               "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
               "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
               "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
               "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
               "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
               "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
               "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
               "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
               "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
               "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
               "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
               "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
               "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
           ).split(" "	)
           __lowerCAmelCase								=					dict(zip(__UpperCamelCase					,						range(len(__UpperCamelCase	)	)	)	)

           __lowerCAmelCase								=					{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase=False					,						__UpperCamelCase=2_0					,						__UpperCamelCase=5	)->	Tuple[str, list]:
           __lowerCAmelCase								=					[(i, tokenizer.decode([i]					,						clean_up_tokenization_spaces=__UpperCamelCase	)) for i in range(len(__UpperCamelCase	)	)]
           __lowerCAmelCase								=					list(filter(lambda __UpperCamelCase	: [t[0]] == tokenizer.encode(t[1]					,						do_phonemize=__UpperCamelCase	)					,						__UpperCamelCase	)	)
           if max_length is not None and len(__UpperCamelCase	) > max_length:
                __lowerCAmelCase								=					toks[:max_length]
           if min_length is not None and len(__UpperCamelCase	) < min_length and len(__UpperCamelCase	) > 0:
                while len(__UpperCamelCase	) < min_length:
                     __lowerCAmelCase								=					toks + toks
        # toks_str = [t[1] for t in toks]
           __lowerCAmelCase								=					[t[0] for t in toks]

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


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Any:
           kwargs.update(self.special_tokens_map	)
           return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)

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

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

           __lowerCAmelCase								=					tokenizer("maɪ c"					,						do_phonemize=__UpperCamelCase	).input_ids
           self.assertEqual(__UpperCamelCase					,						[3, 2_0_0]	)  # mai should be <unk> (=3)


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)

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


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)

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


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)
           __lowerCAmelCase								=					"Hello how are you"
           __lowerCAmelCase								=					tokenizer.phonemize(__UpperCamelCase					,						phonemizer_lang="en-us"	)

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

           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)

           __lowerCAmelCase								=					[
               [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],
           ]
           __lowerCAmelCase								=					tokenizer.decode(sample_ids[0]	)
           __lowerCAmelCase								=					tokenizer.batch_decode(__UpperCamelCase	)
           self.assertEqual(__UpperCamelCase					,						batch_tokens[0]	)
           self.assertEqual(__UpperCamelCase					,						["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]	)


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained(
               "facebook/wav2vec2-lv-60-espeak-cv-ft"					,						word_delimiter_token="|"	)
           tokenizer.add_tokens("|"	)

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


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained(
               "facebook/wav2vec2-lv-60-espeak-cv-ft"					,						word_delimiter_token="|"	)
           tokenizer.add_tokens("|"	)

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


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained(
               "facebook/wav2vec2-lv-60-espeak-cv-ft"					,						word_delimiter_token="|"	)
           tokenizer.add_tokens("|"	)

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

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


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

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

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

           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


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

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

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

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


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained(
               "facebook/wav2vec2-lv-60-espeak-cv-ft"					,						word_delimiter_token=__UpperCamelCase	)
           __lowerCAmelCase								=					"Hello how are you"

           __lowerCAmelCase								=					tokenizer(__UpperCamelCase					,						phonemizer_lang="en-us"	).input_ids
           __lowerCAmelCase								=					tokenizer(__UpperCamelCase					,						phonemizer_lang="fr-fr"	).input_ids

           self.assertNotEqual(__UpperCamelCase					,						__UpperCamelCase	)

           __lowerCAmelCase								=					tokenizer.decode(__UpperCamelCase	)
           __lowerCAmelCase								=					tokenizer.decode(__UpperCamelCase	)

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


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"	)
           __lowerCAmelCase								=					"Hello how Are you"
           __lowerCAmelCase								=					"hello how are you"

           __lowerCAmelCase								=					tokenizer(__UpperCamelCase	).input_ids
           __lowerCAmelCase								=					tokenizer(__UpperCamelCase	).input_ids

           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


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

           # fmt: off
           __lowerCAmelCase								=					[
               [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

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


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           __lowerCAmelCase								=					[d[key] for d in offsets]
           return retrieved_list


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.get_tokenizer(word_delimiter_token="|"	)
           tokenizer.add_tokens("|"	)

           # fmt: off
           # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
           __lowerCAmelCase								=					[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

           __lowerCAmelCase								=					tokenizer.decode(__UpperCamelCase					,						output_char_offsets=__UpperCamelCase					,						filter_word_delimiter_token=__UpperCamelCase	)
           # check Wav2Vec2CTCTokenizerOutput keys for char
           self.assertEqual(len(outputs.keys()	)					,						2	)
           self.assertTrue("text" in outputs	)
           self.assertTrue("char_offsets" in outputs	)
           self.assertTrue(isinstance(__UpperCamelCase					,						__UpperCamelCase	)	)

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

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

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

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

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

                def recursive_check(__UpperCamelCase					,						__UpperCamelCase	):
                     if isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                          [recursive_check(__UpperCamelCase					,						__UpperCamelCase	) for la, la in zip(__UpperCamelCase					,						__UpperCamelCase	)]
                     self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)

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

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


      @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"	)
      def  __UpperCAmelCase		(	self	)->	Dict:
           pass


      @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"	)
      def  __UpperCAmelCase		(	self	)->	Any:
           pass


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


      @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"	)
      def  __UpperCAmelCase		(	self	)->	Any:
           pass


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.get_tokenizers(do_lower_case=__UpperCamelCase	)
           for tokenizer in tokenizers:
                with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
                     __lowerCAmelCase								=					tokenizer.vocab_size
                     __lowerCAmelCase								=					len(__UpperCamelCase	)

                     self.assertNotEqual(__UpperCamelCase					,						0	)

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

                     __lowerCAmelCase								=					["aaaaa bbbbbb", "cccccccccdddddddd"]
                     __lowerCAmelCase								=					tokenizer.add_tokens(__UpperCamelCase	)
                     __lowerCAmelCase								=					tokenizer.vocab_size
                     __lowerCAmelCase								=					len(__UpperCamelCase	)

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

                     __lowerCAmelCase								=					tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"					,						add_special_tokens=__UpperCamelCase	)

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

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

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

                     __lowerCAmelCase								=					tokenizer.encode(
                         ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"					,						add_special_tokens=__UpperCamelCase	)

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


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


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


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

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














import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf



class 							_UpperCamelCase			(enum.Enum			):
      snake_case_  =							0
      snake_case_  =							1
      snake_case_  =							2



@add_end_docstrings(a_			)
class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    """


      def __init__(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	Optional[int]:
           super().__init__(*__UpperCamelCase					,						**__UpperCamelCase	)
           self.check_model_type(
               TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING	)
           if "prefix" not in self._preprocess_params:
                # This is very specific. The logic is quite complex and needs to be done
                # as a "default".
                # It also defines both some preprocess_kwargs and generate_kwargs
                # which is why we cannot put them in their respective methods.
                __lowerCAmelCase								=					None
                if self.model.config.prefix is not None:
                     __lowerCAmelCase								=					self.model.config.prefix
                if prefix is None and self.model.__class__.__name__ in [
                    "XLNetLMHeadModel",
                    "TransfoXLLMHeadModel",
                    "TFXLNetLMHeadModel",
                    "TFTransfoXLLMHeadModel",
                ]:
                     # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                     __lowerCAmelCase								=					self.XL_PREFIX
                if prefix is not None:
                     # Recalculate some generate_kwargs linked to prefix.
                     __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					self._sanitize_parameters(prefix=__UpperCamelCase					,						**self._forward_params	)
                     __lowerCAmelCase								=					{**self._preprocess_params, **preprocess_params}
                     __lowerCAmelCase								=					{**self._forward_params, **forward_params}


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						**__UpperCamelCase					,						)->	int:
           __lowerCAmelCase								=					{}
           if prefix is not None:
                __lowerCAmelCase								=					prefix
           if prefix:
                __lowerCAmelCase								=					self.tokenizer(
                    __UpperCamelCase					,						padding=__UpperCamelCase					,						add_special_tokens=__UpperCamelCase					,						return_tensors=self.framework	)
                __lowerCAmelCase								=					prefix_inputs["input_ids"].shape[-1]

           if handle_long_generation is not None:
                if handle_long_generation not in {"hole"}:
                     raise ValueError(
                         F"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
                         " [None, 'hole']"	)
                __lowerCAmelCase								=					handle_long_generation

           preprocess_params.update(__UpperCamelCase	)
           __lowerCAmelCase								=					generate_kwargs

           __lowerCAmelCase								=					{}
           if return_full_text is not None and return_type is None:
                if return_text is not None:
                     raise ValueError("`return_text` is mutually exclusive with `return_full_text`"	)
                if return_tensors is not None:
                     raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`"	)
                __lowerCAmelCase								=					ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
           if return_tensors is not None and return_type is None:
                if return_text is not None:
                     raise ValueError("`return_text` is mutually exclusive with `return_tensors`"	)
                __lowerCAmelCase								=					ReturnType.TENSORS
           if return_type is not None:
                __lowerCAmelCase								=					return_type
           if clean_up_tokenization_spaces is not None:
                __lowerCAmelCase								=					clean_up_tokenization_spaces

           if stop_sequence is not None:
                __lowerCAmelCase								=					self.tokenizer.encode(__UpperCamelCase					,						add_special_tokens=__UpperCamelCase	)
                if len(__UpperCamelCase	) > 1:
                     warnings.warn(
                         "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
                         " the stop sequence will be used as the stop sequence string in the interim."	)
                __lowerCAmelCase								=					stop_sequence_ids[0]

           return preprocess_params, forward_params, postprocess_params


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	List[str]:
           # Parse arguments
           if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                kwargs.update({"add_space_before_punct_symbol": True}	)

           return super()._parse_and_tokenize(*__UpperCamelCase					,						**__UpperCamelCase	)


      def __call__(	self					,						__UpperCamelCase					,						**__UpperCamelCase	)->	List[Any]:
           return super().__call__(__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase=""					,						__UpperCamelCase=None					,						**__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					self.tokenizer(
               prefix + prompt_text					,						padding=__UpperCamelCase					,						add_special_tokens=__UpperCamelCase					,						return_tensors=self.framework	)
           __lowerCAmelCase								=					prompt_text

           if handle_long_generation == "hole":
                __lowerCAmelCase								=					inputs["input_ids"].shape[-1]
                if "max_new_tokens" in generate_kwargs:
                     __lowerCAmelCase								=					generate_kwargs["max_new_tokens"]
                else:
                     __lowerCAmelCase								=					generate_kwargs.get("max_length"					,						self.model.config.max_length	) - cur_len
                     if new_tokens < 0:
                          raise ValueError("We cannot infer how many new tokens are expected"	)
                if cur_len + new_tokens > self.tokenizer.model_max_length:
                     __lowerCAmelCase								=					self.tokenizer.model_max_length - new_tokens
                     if keep_length <= 0:
                          raise ValueError(
                              "We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
                              " models max length"	)

                     __lowerCAmelCase								=					inputs["input_ids"][:, -keep_length:]
                     if "attention_mask" in inputs:
                          __lowerCAmelCase								=					inputs["attention_mask"][:, -keep_length:]

           return inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						**__UpperCamelCase	)->	Optional[Any]:
           __lowerCAmelCase								=					model_inputs["input_ids"]
           __lowerCAmelCase								=					model_inputs.get("attention_mask"					,						__UpperCamelCase	)
           # Allow empty prompts
           if input_ids.shape[1] == 0:
                __lowerCAmelCase								=					None
                __lowerCAmelCase								=					None
                __lowerCAmelCase								=					1
           else:
                __lowerCAmelCase								=					input_ids.shape[0]
           __lowerCAmelCase								=					model_inputs.pop("prompt_text"	)

           # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
           # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
           __lowerCAmelCase								=					generate_kwargs.pop("prefix_length"					,						0	)
           if prefix_length > 0:
                __lowerCAmelCase								=					"max_new_tokens" in generate_kwargs or (
                    "generation_config" in generate_kwargs
                    and generate_kwargs["generation_config"].max_new_tokens is not None
                )
                if not has_max_new_tokens:
                     __lowerCAmelCase								=					generate_kwargs.get("max_length"	) or self.model.config.max_length
                     generate_kwargs["max_length"] += prefix_length
                __lowerCAmelCase								=					"min_new_tokens" in generate_kwargs or (
                    "generation_config" in generate_kwargs
                    and generate_kwargs["generation_config"].min_new_tokens is not None
                )
                if not has_min_new_tokens and "min_length" in generate_kwargs:
                     generate_kwargs["min_length"] += prefix_length

        # BS x SL
           __lowerCAmelCase								=					self.model.generate(input_ids=__UpperCamelCase					,						attention_mask=__UpperCamelCase					,						**__UpperCamelCase	)
           __lowerCAmelCase								=					generated_sequence.shape[0]
           if self.framework == "pt":
                __lowerCAmelCase								=					generated_sequence.reshape(__UpperCamelCase					,						out_b // in_b					,						*generated_sequence.shape[1:]	)
           elif self.framework == "tf":
                __lowerCAmelCase								=					tf.reshape(__UpperCamelCase					,						(in_b, out_b // in_b, *generated_sequence.shape[1:])	)
           return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase=ReturnType.FULL_TEXT					,						__UpperCamelCase=True	)->	Any:
           __lowerCAmelCase								=					model_outputs["generated_sequence"][0]
           __lowerCAmelCase								=					model_outputs["input_ids"]
           __lowerCAmelCase								=					model_outputs["prompt_text"]
           __lowerCAmelCase								=					generated_sequence.numpy().tolist()
           __lowerCAmelCase								=					[]
           for sequence in generated_sequence:
                if return_type == ReturnType.TENSORS:
                     __lowerCAmelCase								=					{"generated_token_ids": sequence}
                elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                     # Decode text
                     __lowerCAmelCase								=					self.tokenizer.decode(
                         __UpperCamelCase					,						skip_special_tokens=__UpperCamelCase					,						clean_up_tokenization_spaces=__UpperCamelCase					,						)

                     # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                     if input_ids is None:
                          __lowerCAmelCase								=					0
                     else:
                          __lowerCAmelCase								=					len(
                              self.tokenizer.decode(
                                  input_ids[0]					,						skip_special_tokens=__UpperCamelCase					,						clean_up_tokenization_spaces=__UpperCamelCase					,						)	)

                     if return_type == ReturnType.FULL_TEXT:
                          __lowerCAmelCase								=					prompt_text + text[prompt_length:]
                     else:
                          __lowerCAmelCase								=					text[prompt_length:]

                     __lowerCAmelCase								=					{"generated_text": all_text}
                records.append(__UpperCamelCase	)

           return records






