



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 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Optional[int]        =  BioGptTokenizer
       UpperCAmelCase__				:					Union[str, Any]        =  False
       def 					snake_case_       (			self       )     ->		List[str]:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCamelCase    :  int							=	[
              '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>',
          ]
          UpperCamelCase    :  Any							=	dict(zip(SCREAMING_SNAKE_CASE_,			range(len(SCREAMING_SNAKE_CASE_       )       )       )       )
          UpperCamelCase    :  int							=	['l o 123', 'lo w 1456', 'e r</w> 1789', '']

          UpperCamelCase    :  str							=	os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file']       )
          UpperCamelCase    :  Optional[int]							=	os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['merges_file']       )
          with open(self.vocab_file,			'w'       ) as fp:
             fp.write(json.dumps(SCREAMING_SNAKE_CASE_       )       )
          with open(self.merges_file,			'w'       ) as fp:
             fp.write('\n'.join(SCREAMING_SNAKE_CASE_       )       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          UpperCamelCase    :  Union[str, Any]							=	'lower newer'
          UpperCamelCase    :  str							=	'lower newer'
          return input_text, output_text
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Union[str, Any]							=	BioGptTokenizer(self.vocab_file,			self.merges_file       )

          UpperCamelCase    :  List[Any]							=	'lower'
          UpperCamelCase    :  str							=	['low', 'er</w>']
          UpperCamelCase    :  Tuple							=	tokenizer.tokenize(SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	tokens + ['<unk>']
          UpperCamelCase    :  Dict							=	[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_       )






       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Union[str, Any]							=	BioGptTokenizer.from_pretrained('microsoft/biogpt'       )

          UpperCamelCase    :  Any							=	tokenizer.encode('sequence builders',			add_special_tokens=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	tokenizer.encode('multi-sequence build',			add_special_tokens=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[Any]							=	tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Tuple							=	tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

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








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



