




import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Tuple							 =					BioGptTokenizer
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					False





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a    =			[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "w</w>",
							    "r</w>",
							    "t</w>",
							    "lo",
							    "low",
							    "er</w>",
							    "low</w>",
							    "lowest</w>",
							    "newer</w>",
							    "wider</w>",
							    "<unk>",
							]
							a    =			dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase							)							)							)							)
							a    =			["l o 123", "lo w 1456", "e r</w> 1789", ""]

							a    =			os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["vocab_file"]							)
							a    =			os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["merges_file"]							)
							with open(self.vocab_file					,      "w"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							)							)
							with open(self.merges_file					,      "w"							) as fp:
										fp.write("\n".join(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple							)					->     Optional[int]:
							a    =			"lower newer"
							a    =			"lower newer"
							return input_text, output_text





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							a    =			BioGptTokenizer(self.vocab_file					,      self.merges_file							)

							a    =			"lower"
							a    =			["low", "er</w>"]
							a    =			tokenizer.tokenize(__lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokens + ["<unk>"]
							a    =			[14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)					,      __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Any:
							a    =			BioGptTokenizer.from_pretrained("microsoft/biogpt"							)

							a    =			tokenizer.encode("sequence builders"					,      add_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.encode("multi-sequence build"					,      add_special_tokens=__lowerCamelCase							)

							a    =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
							a    =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase					,      __lowerCamelCase							)

							self.assertTrue(encoded_sentence == [2] + text							)
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)












import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				Dict       			=		{'vocab_file': 'spiece.model'}

__lowerCAmelCase		:				Optional[int]       			=		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__lowerCAmelCase		:				Dict       			=		{'bert_for_seq_generation': 512}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							int							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					["""input_ids""", """attention_mask"""]





				def __init__(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     str="<s>"					,      __lowerCamelCase				:     Optional[int]="</s>"					,      __lowerCamelCase				:     int="<unk>"					,      __lowerCamelCase				:     List[str]="<pad>"					,      __lowerCamelCase				:     Union[str, Any]="<::::>"					,      __lowerCamelCase				:     Optional[Dict[str, Any]] = None					,      **__lowerCamelCase				:     List[str]					,      )					->     None:
							a    =			{} if sp_model_kwargs is None else sp_model_kwargs

							# Add extra_ids to the special token list
							super().__init__(
							    bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      sp_model_kwargs=self.sp_model_kwargs					,      **__lowerCamelCase					,      )

							a    =			vocab_file

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							return self.sp_model.get_piece_size()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[int]:
							a    =			{self.convert_ids_to_tokens(__lowerCamelCase							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab





				def __getstate__(      self				:     Dict							)					->     Optional[Any]:
							a    =			self.__dict__.copy()
							a    =			None
							return state





				def __setstate__(      self				:     Optional[Any]					,      __lowerCamelCase				:     Dict							)					->     Optional[Any]:
							a    =			d

							# for backward compatibility
							if not hasattr(self					,      "sp_model_kwargs"							):
										a    =			{}

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     str							)					->     List[str]:
							return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Union[str, Any]							)					->     int:
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			self.sp_model.IdToPiece(__lowerCamelCase							)
							return token





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     Dict							)					->     Any:
							a    =			[]
							a    =			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(__lowerCamelCase							) + token
													a    =			[]
										else:
													current_sub_tokens.append(__lowerCamelCase							)
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a    =			os.path.join(
							    __lowerCamelCase					,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.vocab_file							):
										copyfile(self.vocab_file					,      __lowerCamelCase							)
							elif not os.path.isfile(self.vocab_file							):
										with open(__lowerCamelCase					,      "wb"							) as fi:
													a    =			self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)






