



"""simple docstring"""





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__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					BioGptTokenizer
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									super().setUp()

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

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






							def        lowerCamelCase_   (  self	,       snake_case						)			->							Tuple:
									_UpperCAmelCase							 =		'lower newer'
									_UpperCAmelCase							 =		'lower newer'
									return input_text, output_text






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		BioGptTokenizer(self.vocab_file	,       self.merges_file						)

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

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






							@slow
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		BioGptTokenizer.from_pretrained('microsoft/biogpt'						)

									_UpperCAmelCase							 =		tokenizer.encode('sequence builders'	,       add_special_tokens=UpperCAmelCase__						)
									_UpperCAmelCase							 =		tokenizer.encode('multi-sequence build'	,       add_special_tokens=UpperCAmelCase__						)

									_UpperCAmelCase							 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__						)
									_UpperCAmelCase							 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__	,       UpperCAmelCase__						)

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







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''RobertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


