





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_							(  SCREAMING_SNAKE_CASE_					,	unittest.TestCase     ):
							lowerCamelCase_							     =       WavaVecaPhonemeCTCTokenizer
							lowerCamelCase_							     =       False


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



									"""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(__a				,					range(len(__a		)		)		)		)

									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(__a		) + """\n"""		)


							def   _snake_case	(				self     :Tuple				,					__A     :Any				,					__A     :str=False				,					__A     :Tuple=20				,					__A     :int=5		) -> Tuple[str, list]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [(i, tokenizer.decode([i]				,					clean_up_tokenization_spaces=__a		)) for i in range(len(__a		)		)]
									SCREAMING_SNAKE_CASE__								=   list(filter(lambda __A		: [t[0]] == tokenizer.encode(t[1]				,					do_phonemize=__a		)				,					__a		)		)
									if max_length is not None and len(__a		) > max_length:
											SCREAMING_SNAKE_CASE__								=   toks[:max_length]
									if min_length is not None and len(__a		) < min_length and len(__a		) > 0:
											while len(__a		) < 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(__a				,					clean_up_tokenization_spaces=__a		)
									if " " not in output_txt and len(__a		) > 1:
											SCREAMING_SNAKE_CASE__								=   (
											    tokenizer.decode([toks_ids[0]]				,					clean_up_tokenization_spaces=__a		)
											    + ' '
											    + tokenizer.decode(toks_ids[1:]				,					clean_up_tokenization_spaces=__a		)
											)
									if with_prefix_space:
											SCREAMING_SNAKE_CASE__								=   ' ' + output_txt
									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(__a				,					add_special_tokens=__a		)
									return output_txt, output_ids


							def   _snake_case	(				self     :Union[str, Any]				,					**__A     :str		) -> int:



									"""simple docstring"""



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


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



									"""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=__a		).input_ids
									self.assertEqual(__a				,					[13, 392, 17]		)  # xxx should be last token

									tokenizer.add_tokens(["""aaa""", """bbb""", """ccc"""]		)
									SCREAMING_SNAKE_CASE__								=   tokenizer("""m aaa ɪ ccc"""				,					do_phonemize=__a		).input_ids
									self.assertEqual(__a				,					[13, 393, 17, 395]		)  # aaa and ccc should be after xxx and 2 after aaa

									SCREAMING_SNAKE_CASE__								=   tokenizer("""maɪ c"""				,					do_phonemize=__a		).input_ids
									self.assertEqual(__a				,					[3, 200]		)  # mai should be <unk> (=3)


							def   _snake_case	(				self     :Any		) -> str:



									"""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(__a				,					phonemizer_lang="""en-us"""		)
									self.assertEqual(__a				,					"""h ə l oʊ h aʊ ɑːɹ j uː"""		)


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



									"""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(__a				,					phonemizer_lang="""en-us"""		)
									self.assertEqual(tokenizer(__a		).input_ids				,					tokenizer(__a				,					do_phonemize=__a		).input_ids		)


							def   _snake_case	(				self     :Optional[Any]		) -> Dict:



									"""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(__a				,					phonemizer_lang="""en-us"""		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.decode(tokenizer(__a		).input_ids		)

									self.assertEqual(__a				,					__a		)


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



									"""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(__a		)
									self.assertEqual(__a				,					batch_tokens[0]		)
									self.assertEqual(__a				,					["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]		)


							def   _snake_case	(				self     :Tuple		) -> str:



									"""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(__a				,					phonemizer_lang="""en-us"""		)
									self.assertEqual(__a				,					"""h ə l oʊ | h aʊ | ɑːɹ | j uː |"""		)


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



									"""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(__a				,					phonemizer_lang="""en-us"""		)
									self.assertEqual(tokenizer(__a		).input_ids				,					tokenizer(__a				,					do_phonemize=__a		).input_ids		)


							def   _snake_case	(				self     :Tuple		) -> Dict:



									"""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(__a		)
									self.assertEqual(__a				,					batch_tokens[0]		)
									self.assertEqual(__a				,					["""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=__a		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.batch_decode(__a				,					filter_word_delimiter_token=__a		)
									self.assertEqual(__a				,					batch_tokens[0]		)
									self.assertEqual(__a				,					["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""]		)


							def   _snake_case	(				self     :Optional[Any]		) -> Tuple:



									"""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(__a				,					phonemizer_lang="""en-us"""		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.decode(tokenizer(__a		).input_ids				,					filter_word_delimiter_token=__a		)

									self.assertEqual(__a				,					__a		)


							def   _snake_case	(				self     :Dict		) -> List[str]:



									"""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(__a				,					phonemizer_lang="""en-us"""		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.decode(tokenizer(__a		).input_ids				,					filter_word_delimiter_token=__a		)

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


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								=   tokenizer(__a				,					phonemizer_lang="""en-us"""		).input_ids
									SCREAMING_SNAKE_CASE__								=   tokenizer(__a				,					phonemizer_lang="""fr-fr"""		).input_ids

									self.assertNotEqual(__a				,					__a		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.decode(__a		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.decode(__a		)

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


							def   _snake_case	(				self     :str		) -> str:



									"""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(__a		).input_ids
									SCREAMING_SNAKE_CASE__								=   tokenizer(__a		).input_ids

									self.assertEqual(__a				,					__a		)


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



									"""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, 392, 392, 393, 392, 392, 393, 394, 394],
									    [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
									]
									# fmt: on

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


							@staticmethod
							def   _snake_case	(				__A     :Any				,					__A     :Dict		) -> Tuple:



									"""simple docstring"""



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


							def   _snake_case	(				self     :str		) -> Optional[int]:



									"""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(__a				,					output_char_offsets=__a				,					filter_word_delimiter_token=__a		)
									# 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(__a				,					__a		)		)

									# 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   _snake_case	(				self     :Union[str, Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.get_tokenizer(word_delimiter_token="""|"""		)

									def check_list_tuples_equal(__A     :int				,					__A     :Union[str, Any]		):
											self.assertTrue(isinstance(__a				,					__a		)		)
											self.assertTrue(isinstance(outputs_list[0]				,					__a		)		)

											# 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(__A     :Any				,					__A     :str		):
													if isinstance(__a				,					__a		):
															[recursive_check(__a				,					__a		) for la, la in zip(__a				,					__a		)]
													self.assertEqual(__a				,					__a		)

											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(__a				,					output_char_offsets=__a		)
									SCREAMING_SNAKE_CASE__								=   [tokenizer.decode(__a				,					output_char_offsets=__a		) for ids in sample_ids]
									check_list_tuples_equal(__a				,					__a		)


							@unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"""		)
							def   _snake_case	(				self     :Any		) -> Optional[int]:



									"""simple docstring"""



									pass


							@unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"""		)
							def   _snake_case	(				self     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									pass


							@unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing"""		)
							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									pass


							def   _snake_case	(				self     :Optional[int]		) -> str:



									"""simple docstring"""



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

													self.assertNotEqual(__a				,					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(__a		)
													SCREAMING_SNAKE_CASE__								=   tokenizer.vocab_size
													SCREAMING_SNAKE_CASE__								=   len(__a		)

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

													SCREAMING_SNAKE_CASE__								=   tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""				,					add_special_tokens=__a		)

													self.assertGreaterEqual(len(__a		)				,					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(__a		)
													SCREAMING_SNAKE_CASE__								=   tokenizer.vocab_size
													SCREAMING_SNAKE_CASE__								=   len(__a		)

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

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

													self.assertGreaterEqual(len(__a		)				,					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   _snake_case	(				self     :Dict		) -> List[str]:



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.get_tokenizers(fast=__a				,					do_lower_case=__a		)
									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(__a		)

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




'''simple docstring'''





import pickle

import numpy as np
from matplotlib import pyplot as plt



class  snake_case__						:
 def __init__(   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Dict      ,     __a						:							List[Any]      ,     __a						:							str      ,     __a						:							str      ,     __a						:							List[str]=0.2      ,     __a						:							Any=0.2  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Any													=    bp_numa
       __snake_case :   str													=    bp_numa
       __snake_case :   Optional[Any]													=    bp_numa
       __snake_case :   Any													=    conva_get[:2]
       __snake_case :   Dict													=    conva_get[2]
       __snake_case :   Optional[int]													=    size_pa
       __snake_case :   str													=    rate_w
       __snake_case :   Optional[Any]													=    rate_t
       __snake_case :   Optional[Any]													=    [
           np.mat(-1 * np.random.rand(self.conva[0]      ,     self.conva[0]  ) + 0.5  )
           for i in range(self.conva[1]  )
       ]
       __snake_case :   List[Any]													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Any													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Optional[int]													=    -2 * np.random.rand(self.conva[1]  ) + 1
       __snake_case :   Optional[Any]													=    -2 * np.random.rand(self.num_bpa  ) + 1
       __snake_case :   str													=    -2 * np.random.rand(self.num_bpa  ) + 1
 def A_   (   self						:							int      ,     __a						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       # save model dict with pickle
       __snake_case :   int													=    {
           'num_bp1': self.num_bpa,
           'num_bp2': self.num_bpa,
           'num_bp3': self.num_bpa,
           'conv1': self.conva,
           'step_conv1': self.step_conva,
           'size_pooling1': self.size_poolinga,
           'rate_weight': self.rate_weight,
           'rate_thre': self.rate_thre,
           'w_conv1': self.w_conva,
           'wkj': self.wkj,
           'vji': self.vji,
           'thre_conv1': self.thre_conva,
           'thre_bp2': self.thre_bpa,
           'thre_bp3': self.thre_bpa,
       }
       with open(__a      ,     'wb'  ) as f:
             pickle.dump(__a      ,     __a  )

       print(f'''Model saved： {save_path}'''  )
 @classmethod
 def A_   (   cls						:							List[Any]      ,     __a						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       # read saved model
       with open(__a      ,     'rb'  ) as f:
             __snake_case :   Dict													=    pickle.load(__a  )  # noqa: S301

       __snake_case :   Tuple													=    model_dic.get('conv1'  )
       conv_get.append(model_dic.get('step_conv1'  )  )
       __snake_case :   Optional[Any]													=    model_dic.get('size_pooling1'  )
       __snake_case :   int													=    model_dic.get('num_bp1'  )
       __snake_case :   Optional[Any]													=    model_dic.get('num_bp2'  )
       __snake_case :   Optional[Any]													=    model_dic.get('num_bp3'  )
       __snake_case :   Any													=    model_dic.get('rate_weight'  )
       __snake_case :   Optional[int]													=    model_dic.get('rate_thre'  )
       # create model instance
       __snake_case :   Any													=    CNN(__a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a  )
       # modify model parameter
       __snake_case :   Dict													=    model_dic.get('w_conv1'  )
       __snake_case :   Any													=    model_dic.get('wkj'  )
       __snake_case :   List[str]													=    model_dic.get('vji'  )
       __snake_case :   int													=    model_dic.get('thre_conv1'  )
       __snake_case :   Optional[int]													=    model_dic.get('thre_bp2'  )
       __snake_case :   List[Any]													=    model_dic.get('thre_bp3'  )
       return conv_ins
 def A_   (   self						:							List[Any]      ,     __a						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       return 1 / (1 + np.exp(-1 * x  ))
 def A_   (   self						:							Any      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       return round(__a      ,     3  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Dict      ,     __a						:							Optional[int]      ,     __a						:							List[str]  )      ->   str:







       '''simple docstring'''
       # convolution process
       __snake_case :   int													=    convs[0]
       __snake_case :   List[str]													=    convs[1]
       __snake_case :   Optional[Any]													=    np.shape(__a  )[0]
       # get the data slice of original image data, data_focus
       __snake_case :   str													=    []
       for i_focus in range(0      ,     size_data - size_conv + 1      ,     __a  ):
             for j_focus in range(0      ,     size_data - size_conv + 1      ,     __a  ):
                   __snake_case :   Any													=    data[
                       i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                   ]
                   data_focus.append(__a  )
        # calculate the feature map of every single kernel, and saved as list of matrix
       __snake_case :   Optional[int]													=    []
       __snake_case :   Dict													=    int((size_data - size_conv) / conv_step + 1  )
       for i_map in range(__a  ):
             __snake_case :   Optional[int]													=    []
             for i_focus in range(len(__a  )  ):
                   __snake_case :   List[Any]													=    (
                       np.sum(np.multiply(data_focus[i_focus]      ,     w_convs[i_map]  )  )
                       - thre_convs[i_map]
                   )
                   featuremap.append(self.sig(__a  )  )
             __snake_case :   List[Any]													=    np.asmatrix(__a  ).reshape(
                 __a      ,     __a  )
             data_featuremap.append(__a  )

       # expanding the data slice to One dimenssion
       __snake_case :   Union[str, Any]													=    []
       for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(__a  )  )
       __snake_case :   List[Any]													=    np.asarray(__a  )
       return focus_list, data_featuremap
 def A_   (   self						:							Any      ,     __a						:							int      ,     __a						:							Tuple      ,     __a						:							List[Any]="average_pool"  )      ->   Dict:







       '''simple docstring'''
       # pooling process
       __snake_case :   List[str]													=    len(featuremaps[0]  )
       __snake_case :   Tuple													=    int(size_map / size_pooling  )
       __snake_case :   int													=    []
       for i_map in range(len(__a  )  ):
             __snake_case :   str													=    featuremaps[i_map]
             __snake_case :   Optional[Any]													=    []
             for i_focus in range(0      ,     __a      ,     __a  ):
                   for j_focus in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Dict													=    feature_map[
                             i_focus : i_focus + size_pooling,
                             j_focus : j_focus + size_pooling,
                         ]
                         if pooling_type == "average_pool":
                               # average pooling
                               map_pooled.append(np.average(__a  )  )
                         elif pooling_type == "max_pooling":
                               # max pooling
                               map_pooled.append(np.max(__a  )  )
             __snake_case :   List[str]													=    np.asmatrix(__a  ).reshape(__a      ,     __a  )
             featuremap_pooled.append(__a  )
       return featuremap_pooled
 def A_   (   self						:							List[str]      ,     __a						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       # expanding three dimension data to one dimension list
       __snake_case :   Tuple													=    []
       for i in range(len(__a  )  ):
             __snake_case :   Optional[int]													=    np.shape(data[i]  )
             __snake_case :   List[str]													=    data[i].reshape(1      ,     shapes[0] * shapes[1]  )
             __snake_case :   List[Any]													=    data_listed.getA().tolist()[0]
             data_expanded.extend(__a  )
       __snake_case :   Optional[int]													=    np.asarray(__a  )
       return data_expanded
 def A_   (   self						:							Union[str, Any]      ,     __a						:							int  )      ->   Any:







       '''simple docstring'''
       # expanding matrix to one dimension list
       __snake_case :   int													=    np.asarray(__a  )
       __snake_case :   str													=    np.shape(__a  )
       __snake_case :   Any													=    data_mat.reshape(1      ,     shapes[0] * shapes[1]  )
       return data_expanded
 def A_   (   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Optional[int]      ,     __a						:							List[str]      ,     __a						:							int      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    []
       __snake_case :   Tuple													=    0
       for i_map in range(__a  ):
             __snake_case :   Union[str, Any]													=    np.ones((size_map, size_map)  )
             for i in range(0      ,     __a      ,     __a  ):
                   for j in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Any													=    pd_pool[
                             i_pool
                         ]
                         __snake_case :   List[Any]													=    i_pool + 1
             __snake_case :   List[Any]													=    np.multiply(
                 __a      ,     np.multiply(out_map[i_map]      ,     (1 - out_map[i_map])  )  )
             pd_all.append(__a  )
       return pd_all
 def A_   (   self						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Dict      ,     __a						:							Tuple      ,     __a						:							Optional[int]=bool  )      ->   List[Any]:







       '''simple docstring'''
       # model traning
       print('----------------------Start Training-------------------------'  )
       print((' - - Shape: Train_Data  ', np.shape(__a  ))  )
       print((' - - Shape: Teach_Data  ', np.shape(__a  ))  )
       __snake_case :   str													=    0
       __snake_case :   List[str]													=    []
       __snake_case :   List[Any]													=    10000
       while rp < n_repeat and mse >= error_accuracy:
             __snake_case :   int													=    0
             print(f'''-------------Learning Time {rp}--------------'''  )
             for p in range(len(__a  )  ):
                   # print('------------Learning Image: %d--------------'%p)
                   __snake_case :   List[Any]													=    np.asmatrix(datas_train[p]  )
                   __snake_case :   Optional[Any]													=    np.asarray(datas_teach[p]  )
                   __snake_case							,     __snake_case :   List[Any]													=    self.convolute(
                       __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
                   __snake_case :   Tuple													=    self.pooling(__a      ,     self.size_poolinga  )
                   __snake_case :   Dict													=    np.shape(__a  )
                   __snake_case :   Tuple													=    self._expand(__a  )
                   __snake_case :   str													=    data_bp_input

                   __snake_case :   List[Any]													=    np.dot(__a      ,     self.vji.T  ) - self.thre_bpa
                   __snake_case :   Any													=    self.sig(__a  )
                   __snake_case :   Tuple													=    np.dot(__a      ,     self.wkj.T  ) - self.thre_bpa
                   __snake_case :   Optional[Any]													=    self.sig(__a  )

                   # --------------Model Leaning ------------------------
                   # calculate error and gradient---------------
                   __snake_case :   Tuple													=    np.multiply(
                       (data_teach - bp_outa)      ,     np.multiply(__a      ,     (1 - bp_outa)  )  )
                   __snake_case :   Tuple													=    np.multiply(
                       np.dot(__a      ,     self.wkj  )      ,     np.multiply(__a      ,     (1 - bp_outa)  )  )
                   __snake_case :   Union[str, Any]													=    np.dot(__a      ,     self.vji  )

                   __snake_case :   Optional[int]													=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                   __snake_case :   Tuple													=    pd_conva_pooled.T.getA().tolist()
                   __snake_case :   Optional[Any]													=    self._calculate_gradient_from_pool(
                       __a      ,     __a      ,     shape_featuremapa[0]      ,     shape_featuremapa[1]      ,     self.size_poolinga      ,     )
                   # weight and threshold learning process---------
                   # convolution layer
                   for k_conv in range(self.conva[1]  ):
                         __snake_case :   int													=    self._expand_mat(pd_conva_all[k_conv]  )
                         __snake_case :   Optional[int]													=    self.rate_weight * np.dot(__a      ,     __a  )

                         __snake_case :   List[Any]													=    self.w_conva[k_conv] + delta_w.reshape(
                             (self.conva[0], self.conva[0])  )

                         __snake_case :   Optional[Any]													=    (
                             self.thre_conva[k_conv]
                             - np.sum(pd_conva_all[k_conv]  ) * self.rate_thre
                         )
                   # all connected layer
                   __snake_case :   Tuple													=    self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                   __snake_case :   Optional[Any]													=    self.vji + pd_j_all.T * bp_outa * self.rate_weight
                   __snake_case :   str													=    self.thre_bpa - pd_k_all * self.rate_thre
                   __snake_case :   Optional[int]													=    self.thre_bpa - pd_j_all * self.rate_thre
                   # calculate the sum error of all single image
                   __snake_case :   Any													=    np.sum(abs(data_teach - bp_outa  )  )
                   error_count += errors
                   # print('   ----Teach      ',data_teach)
                   # print('   ----BP_output  ',bp_out3)
             __snake_case :   Tuple													=    rp + 1
             __snake_case :   Tuple													=    error_count / patterns
             all_mse.append(__a  )

       def draw_error():
             __snake_case :   Union[str, Any]													=    [error_accuracy for i in range(int(n_repeat * 1.2  )  )]
             plt.plot(__a      ,     '+-'  )
             plt.plot(__a      ,     'r--'  )
             plt.xlabel('Learning Times'  )
             plt.ylabel('All_mse'  )
             plt.grid(__a      ,     alpha=0.5  )
             plt.show()

       print('------------------Training Complished---------------------'  )
       print((' - - Training epoch: ', rp, f'''     - - Mse: {mse:.6f}''')  )
       if draw_e:
             draw_error()
       return mse
 def A_   (   self						:							Tuple      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       # model predict
       __snake_case :   str													=    []
       print('-------------------Start Testing-------------------------'  )
       print((' - - Shape: Test_Data  ', np.shape(__a  ))  )
       for p in range(len(__a  )  ):
             __snake_case :   int													=    np.asmatrix(datas_test[p]  )
             __snake_case							,     __snake_case :   str													=    self.convolute(
                 __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
             __snake_case :   List[str]													=    self.pooling(__a      ,     self.size_poolinga  )
             __snake_case :   List[Any]													=    self._expand(__a  )

             __snake_case :   Optional[Any]													=    data_bp_input
             __snake_case :   Optional[Any]													=    bp_outa * self.vji.T - self.thre_bpa
             __snake_case :   Any													=    self.sig(__a  )
             __snake_case :   Any													=    bp_outa * self.wkj.T - self.thre_bpa
             __snake_case :   str													=    self.sig(__a  )
             produce_out.extend(bp_outa.getA().tolist()  )
       __snake_case :   List[Any]													=    [list(map(self.do_round      ,     __a  )  ) for each in produce_out]
       return np.asarray(__a  )





 def A_   (   self						:							Optional[Any]      ,     __a						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       # return the data of image after convoluting process so we can check it out
       __snake_case :   int													=    np.asmatrix(__a  )
       __snake_case							,     __snake_case :   int													=    self.convolute(
           __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
       __snake_case :   Dict													=    self.pooling(__a      ,     self.size_poolinga  )

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass



