





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Any       =       XLMTokenizer
						__magic_name__:			str       =       False






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







							"""simple docstring"""


							super().setUp()

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

							snake_case_		:			Tuple											=	os.path.join(self.tmpdirname		,   VOCAB_FILES_NAMES['vocab_file']	)
							snake_case_		:			Optional[int]											=	os.path.join(self.tmpdirname		,   VOCAB_FILES_NAMES['merges_file']	)
							with open(self.vocab_file		,   'w'	) as fp:
								fp.write(json.dumps(_A	)	)
							with open(self.merges_file		,   'w'	) as fp:
								fp.write('\n'.join(_A	)	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      int	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	'lower newer'
							snake_case_		:			Optional[Any]											=	'lower newer'
							return input_text, output_text






						def UpperCAmelCase_				(      self      :      int	)							->    int:







							"""simple docstring"""


							snake_case_		:			Dict											=	XLMTokenizer(self.vocab_file		,   self.merges_file	)

							snake_case_		:			Optional[Any]											=	'lower'
							snake_case_		:			Any											=	['low', 'er</w>']
							snake_case_		:			int											=	tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

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






						@slow
						def UpperCAmelCase_				(      self      :      str	)							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	XLMTokenizer.from_pretrained('xlm-mlm-en-2048'	)

							snake_case_		:			str											=	tokenizer.encode('sequence builders'		,   add_special_tokens=_A	)
							snake_case_		:			int											=	tokenizer.encode('multi-sequence build'		,   add_special_tokens=_A	)

							snake_case_		:			Dict											=	tokenizer.build_inputs_with_special_tokens(_A	)
							snake_case_		:			int											=	tokenizer.build_inputs_with_special_tokens(_A		,   _A	)

							assert encoded_sentence == [0] + text + [1]
							assert encoded_pair == [0] + text + [1] + text_a + [1]








from ....utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      str		,   _A      :      List[Any]		,   _A      :      List[Any]=None		,   _A      :      str=2048	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	config.__dict__
							snake_case_		:			List[str]											=	modal_hidden_size
							if num_labels:
								snake_case_		:			int											=	num_labels

