"""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 	UpperCamelCase    (					lowercase__   ,			unittest.TestCase		):
				UpperCAmelCase       :   Optional[Any]					=	BioGptTokenizer
				UpperCAmelCase       :   Dict					=	False



				def       _lowercase  (self       :   Optional[Any])					->			Any:
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__snake_case    :							List[str]   	=				[
					    '''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    :							Any   	=				dict(zip(UpperCAmelCase__			,						range(len(UpperCAmelCase__))))
					__snake_case    :							Union[str, Any]   	=				['''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    :							Union[str, Any]   	=				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       _lowercase  (self       :   List[Any]			,						_A       :   Union[str, Any])					->			List[str]:
					__snake_case    :							Dict   	=				'''lower newer'''
					__snake_case    :							str   	=				'''lower newer'''
					return input_text, output_text



				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					__snake_case    :							List[Any]   	=				BioGptTokenizer(self.vocab_file			,						self.merges_file)

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

					__snake_case    :							Optional[int]   	=				tokens + ['''<unk>''']
					__snake_case    :							Any   	=				[14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__)			,						UpperCAmelCase__)






				@slow
				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Dict   	=				BioGptTokenizer.from_pretrained('microsoft/biogpt')

					__snake_case    :							List[str]   	=				tokenizer.encode('sequence builders'			,						add_special_tokens=UpperCAmelCase__)
					__snake_case    :							Optional[int]   	=				tokenizer.encode('multi-sequence build'			,						add_special_tokens=UpperCAmelCase__)

					__snake_case    :							str   	=				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__)
					__snake_case    :							Optional[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


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





