

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 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      BioGptTokenizer
				__SCREAMING_SNAKE_CASE :			Dict	      =      False




				def    a							( self	):
											super().setUp()

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

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




				def    a							( self				,       snake_case	):
											snake_case_          =   'lower newer'
											snake_case_          =   'lower newer'
											return input_text, output_text




				def    a							( self	):
											snake_case_          =   BioGptTokenizer(self.vocab_file				,       self.merges_file	)

											snake_case_          =   'lower'
											snake_case_          =   ['low', 'er</w>']
											snake_case_          =   tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

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




				@slow
				def    a							( self	):
											snake_case_          =   BioGptTokenizer.from_pretrained('microsoft/biogpt'	)

											snake_case_          =   tokenizer.encode('sequence builders'				,       add_special_tokens=snake_case	)
											snake_case_          =   tokenizer.encode('multi-sequence build'				,       add_special_tokens=snake_case	)

											snake_case_          =   tokenizer.build_inputs_with_special_tokens(snake_case	)
											snake_case_          =   tokenizer.build_inputs_with_special_tokens(snake_case				,       snake_case	)

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase		:       Tuple						    =	{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       int						    =	[
					    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """XLMRobertaXLForCausalLM""",
					    """XLMRobertaXLForMaskedLM""",
					    """XLMRobertaXLForMultipleChoice""",
					    """XLMRobertaXLForQuestionAnswering""",
					    """XLMRobertaXLForSequenceClassification""",
					    """XLMRobertaXLForTokenClassification""",
					    """XLMRobertaXLModel""",
					    """XLMRobertaXLPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[Any]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)




