





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   a_ (			A__		,							unittest.TestCase      ):
			A__      :			List[Any]       	=  BioGptTokenizer
			A__      :			Any       	=  False

			def      lowerCAmelCase(   self   :  str						):






							"""simple docstring"""
							super().setUp()

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

							snake_case   :					Optional[int]    						=				os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES['''vocab_file''']						)
							snake_case   :					List[str]    						=				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   :  Optional[int]     , UpperCAmelCase__   :  Dict						):






							"""simple docstring"""
							snake_case   :					List[str]    						=				'''lower newer'''
							snake_case   :					List[str]    						=				'''lower newer'''
							return input_text, output_text

			def      lowerCAmelCase(   self   :  Tuple						):






							"""simple docstring"""
							snake_case   :					List[str]    						=				BioGptTokenizer(self.vocab_file     , self.merges_file						)

							snake_case   :					str    						=				'''lower'''
							snake_case   :					Optional[Any]    						=				['''low''', '''er</w>''']
							snake_case   :					Union[str, Any]    						=				tokenizer.tokenize(UpperCamelCase_						)
							self.assertListEqual(UpperCamelCase_     , UpperCamelCase_						)

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



			@slow
			def      lowerCAmelCase(   self   :  Any						):






							"""simple docstring"""
							snake_case   :					Tuple    						=				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''						)

							snake_case   :					Optional[int]    						=				tokenizer.encode('''sequence builders'''     , add_special_tokens=UpperCamelCase_						)
							snake_case   :					Optional[Any]    						=				tokenizer.encode('''multi-sequence build'''     , add_special_tokens=UpperCamelCase_						)

							snake_case   :					int    						=				tokenizer.build_inputs_with_special_tokens(UpperCamelCase_						)
							snake_case   :					List[Any]    						=				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_torch_available


__snake_case				 =	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoForCausalLM''',
		    '''GPTNeoForQuestionAnswering''',
		    '''GPTNeoForSequenceClassification''',
		    '''GPTNeoForTokenClassification''',
		    '''GPTNeoModel''',
		    '''GPTNeoPreTrainedModel''',
		    '''load_tf_weights_in_gpt_neo''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxGPTNeoForCausalLM''',
		    '''FlaxGPTNeoModel''',
		    '''FlaxGPTNeoPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neo import (
				    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoForCausalLM,
				    GPTNeoForQuestionAnswering,
				    GPTNeoForSequenceClassification,
				    GPTNeoForTokenClassification,
				    GPTNeoModel,
				    GPTNeoPreTrainedModel,
				    load_tf_weights_in_gpt_neo,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
		import sys

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