





import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_      :			int		  =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      :			Dict		  =      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE_      :			Optional[int]		  =      {
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

SCREAMING_SNAKE_CASE_      :			Dict		  =      {
    '''microsoft/biogpt''': 1024,
}



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Dict:
					__lowercase					=    set()
					__lowercase					=    word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)       )
										__lowercase					=    char
					return pairs


class 		snake_case_      (       __UpperCAmelCase	):







	'''simple docstring'''
	__UpperCamelCase        =		VOCAB_FILES_NAMES
	__UpperCamelCase        =		PRETRAINED_VOCAB_FILES_MAP
	__UpperCamelCase        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__UpperCamelCase        =		['''input_ids''', '''attention_mask''']




	def __init__(	self       :    int				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Tuple="<unk>"				,			__lowerCamelCase       :    Tuple="<s>"				,			__lowerCamelCase       :    Union[str, Any]="</s>"				,			__lowerCamelCase       :    Dict="</s>"				,			__lowerCamelCase       :    Any="<pad>"				,			**__lowerCamelCase       :    str				,			)					->   Optional[int]:

						'''simple docstring'''




						super().__init__(
						    bos_token=lowercase_				,			eos_token=lowercase_				,			sep_token=lowercase_				,			unk_token=lowercase_				,			pad_token=lowercase_				,			**lowercase_				,			)

						try:
											import sacremoses
						except ImportError:
											raise ImportError(
											    'You need to install sacremoses to use BioGptTokenizer. '
											    'See https://pypi.org/project/sacremoses/ for installation.'      )

						__lowercase					=    '''en'''
						__lowercase					=    sacremoses
						# cache of sm.MosesTokenizer instance
						__lowercase					=    {}
						__lowercase					=    {}
						with open(lowercase_				,			encoding='utf-8'      ) as vocab_handle:
											__lowercase					=    json.load(lowercase_      )
						__lowercase					=    {v: k for k, v in self.encoder.items()}
						with open(lowercase_				,			encoding='utf-8'      ) as merges_handle:
											__lowercase					=    merges_handle.read().split('\n'      )[:-1]
						__lowercase					=    [tuple(merge.split()[:2]      ) for merge in merges]
						__lowercase					=    dict(zip(lowercase_				,			range(len(lowercase_      )      )      )      )
						__lowercase					=    {}




	@property
	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

						'''simple docstring'''




						return len(self.encoder      )




	def       UpperCAmelCase (	self       :    int      )					->   str:

						'''simple docstring'''




						return dict(self.encoder				,			**self.added_tokens_encoder      )




	def       UpperCAmelCase (	self       :    Any				,			__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[Any]      )					->   Optional[int]:

						'''simple docstring'''




						if lang not in self.cache_moses_tokenizer:
											__lowercase					=    self.sm.MosesTokenizer(lang=lowercase_      )
											__lowercase					=    moses_tokenizer
						return self.cache_moses_tokenizer[lang].tokenize(
						    lowercase_				,			aggressive_dash_splits=lowercase_				,			return_str=lowercase_				,			escape=lowercase_      )




	def       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Tuple      )					->   List[Any]:

						'''simple docstring'''




						if lang not in self.cache_moses_detokenizer:
											__lowercase					=    self.sm.MosesDetokenizer(lang=lowercase_      )
											__lowercase					=    moses_detokenizer
						return self.cache_moses_detokenizer[lang].detokenize(lowercase_      )




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    str      )					->   List[str]:

						'''simple docstring'''




						__lowercase					=    tuple(token[:-1]      ) + (token[-1] + '''</w>''',)
						if token in self.cache:
											return self.cache[token]
						__lowercase					=    get_pairs(lowercase_      )

						if not pairs:
											return token + "</w>"

						while True:
											__lowercase					=    min(lowercase_				,			key=lambda __lowerCamelCase      : self.bpe_ranks.get(lowercase_				,			float('inf'      )      )      )
											if bigram not in self.bpe_ranks:
																break
											__lowercase					=    bigram
											__lowercase					=    []
											__lowercase					=    0
											while i < len(lowercase_      ):
																try:
																					__lowercase					=    word.index(lowercase_				,			lowercase_      )
																except ValueError:
																					new_word.extend(word[i:]      )
																					break
																else:
																					new_word.extend(word[i:j]      )
																					__lowercase					=    j

																if word[i] == first and i < len(lowercase_      ) - 1 and word[i + 1] == second:
																					new_word.append(first + second      )
																					i += 2
																else:
																					new_word.append(word[i]      )
																					i += 1
											__lowercase					=    tuple(lowercase_      )
											__lowercase					=    new_word
											if len(lowercase_      ) == 1:
																break
											else:
																__lowercase					=    get_pairs(lowercase_      )
						__lowercase					=    ''' '''.join(lowercase_      )
						if word == "\n  </w>":
											__lowercase					=    '''\n</w>'''
						__lowercase					=    word
						return word




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Dict=False      )					->   List[Any]:

						'''simple docstring'''




						if bypass_tokenizer:
											__lowercase					=    text.split()
						else:
											__lowercase					=    self.moses_tokenize(lowercase_				,			self.lang      )

						__lowercase					=    []
						for token in text:
											if token:
																split_tokens.extend(list(self.bpe(lowercase_      ).split(' '      )      )      )

						return split_tokens




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    int      )					->   str:

						'''simple docstring'''




						return self.encoder.get(lowercase_				,			self.encoder.get(self.unk_token      )      )




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    str      )					->   Optional[int]:

						'''simple docstring'''




						return self.decoder.get(lowercase_				,			self.unk_token      )




	def       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    Tuple      )					->   List[str]:

						'''simple docstring'''




						__lowercase					=    [t.replace(' '				,			''      ).replace('</w>'				,			' '      ) for t in tokens]
						__lowercase					=    ''''''.join(lowercase_      ).split()
						# detokenize
						__lowercase					=    self.moses_detokenize(lowercase_				,			self.lang      )
						return text




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    str = None      )					->   List[int]:

						'''simple docstring'''




						if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
						__lowercase					=    [self.sep_token_id]
						return sep + token_ids_a + sep + token_ids_a




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    List[Any]				,			__lowerCamelCase       :    Tuple = None				,			__lowerCamelCase       :    Tuple = False      )					->   List[int]:

						'''simple docstring'''




						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase_				,			token_ids_a=lowercase_				,			already_has_special_tokens=lowercase_      )
						# no bos used in fairseq
						if token_ids_a is not None:
											return [1] + ([0] * len(lowercase_      )) + [1] + ([0] * len(lowercase_      ))
						return [1] + ([0] * len(lowercase_      ))




	def       UpperCAmelCase (	self       :    List[str]				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    str = None      )					->   List[int]:

						'''simple docstring'''




						__lowercase					=    [self.sep_token_id]

						# no bos used in fairseq
						if token_ids_a is None:
											return len(token_ids_a + sep      ) * [0]
						return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]				,			__lowerCamelCase       :    List[str] = None      )					->   Tuple[str]:

						'''simple docstring'''




						if not os.path.isdir(lowercase_      ):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
											return
						__lowercase					=    os.path.join(
						    lowercase_				,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
						__lowercase					=    os.path.join(
						    lowercase_				,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

						with open(lowercase_				,			'w'				,			encoding='utf-8'      ) as f:
											f.write(json.dumps(self.encoder				,			indent=2				,			sort_keys=lowercase_				,			ensure_ascii=lowercase_      ) + '\n'      )

						__lowercase					=    0
						with open(lowercase_				,			'w'				,			encoding='utf-8'      ) as writer:
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,			key=lambda __lowerCamelCase      : kv[1]      ):
																if index != token_index:
																					logger.warning(
																					    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																					    ' Please check that the tokenizer is not corrupted!'      )
																					__lowercase					=    token_index
																writer.write(' '.join(lowercase_      ) + '\n'      )
																index += 1

						return vocab_file, merge_file




	def __getstate__(	self       :    Any      )					->   List[str]:

						'''simple docstring'''




						__lowercase					=    self.__dict__.copy()
						__lowercase					=    None
						return state






	def __setstate__(	self       :    Optional[int]				,			__lowerCamelCase       :    Optional[int]      )					->   List[str]:

						'''simple docstring'''




						__lowercase					=    d

						try:
											import sacremoses
						except ImportError:
											raise ImportError(
											    'You need to install sacremoses to use XLMTokenizer. '
											    'See https://pypi.org/project/sacremoses/ for installation.'      )

						__lowercase					=    sacremoses











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    Path(self.tmpdirname      )
			save_json(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['vocab_file']      )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['spm_file']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




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

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor_add_kwargs.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    processor(__lowerCamelCase				,			return_tensors='np'      )

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1E-2      )




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

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]      )




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			self.assertListEqual(
			    processor.model_input_names				,			feature_extractor.model_input_names				,			msg='`processor` and `feature_extractor` model input names do not match'				,			)




