



'''simple docstring'''

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class 			__magic_name__		(					unittest.TestCase     ):
							lowerCAmelCase :					Tuple            =      EsmTokenizer







							def 							__lowercase    (							self      : Any	):
									super().setUp()
									_a  :   Dict				 =							tempfile.mkdtemp()
									# fmt: off
									_a  :   List[str]				 =							["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
									# fmt: on
									_a  :   Any				 =							os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']	)
									with open(self.vocab_file			,'w'			,encoding='utf-8'	) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)







							def 							__lowercase    (							self      : str			,**_UpperCAmelCase      : Optional[Any]	):
									return [self.get_tokenizer(**_UpperCAmelCase	)]







							def 							__lowercase    (							self      : Tuple			,**_UpperCAmelCase      : str	):
									return self.tokenizer_class.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : Dict	):
									_a  :   Tuple				 =							self.tokenizer_class(self.vocab_file	)

									_a  :   Any				 =							tokenizer.tokenize('LAGVS'	)
									self.assertListEqual(_UpperCAmelCase			,['L', 'A', 'G', 'V', 'S']	)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase	)			,[4, 5, 6, 7, 8]	)







							def 							__lowercase    (							self      : Dict	):
									_a  :   List[str]				 =							self.tokenizer_class(self.vocab_file	)

									_a  :   Tuple				 =							'LAGVS'
									self.assertListEqual(tokenizer.encode(_UpperCAmelCase	)			,[0, 4, 5, 6, 7, 8, 2]	)







							def 							__lowercase    (							self      : List[str]	):
									_a  :   List[Any]				 =							self.tokenizer_class(self.vocab_file	)

									_a  :   Dict				 =							['LAGVS', 'WCB']
									_a  :   Optional[int]				 =							tokenizer(_UpperCAmelCase			,padding=_UpperCAmelCase	)['input_ids']

									self.assertListEqual(_UpperCAmelCase			,[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)







							def 							__lowercase    (							self      : List[str]	):
									_a  :   Dict				 =							self.tokenizer_class(self.vocab_file	)

									_a  :   int				 =							['LAGVS', 'WCB']
									_a  :   Optional[int]				 =							tokenizer(_UpperCAmelCase			,padding=_UpperCAmelCase	)['input_ids']

									self.assertListEqual(_UpperCAmelCase			,[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)







							def 							__lowercase    (							self      : List[str]	):
									_a  :   List[Any]				 =							self.get_tokenizers(fast=_UpperCAmelCase	)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
													_a  :   int				 =							'<unk>'
													_a  :   Optional[int]				 =							'<mask>'

													_a  :   Optional[int]				 =							tokenizer.tokenize(_UpperCAmelCase	)
													_a  :   Optional[Any]				 =							tokenizer.tokenize(_UpperCAmelCase	)

													self.assertEqual(len(_UpperCAmelCase	)			,1	)
													self.assertEqual(len(_UpperCAmelCase	)			,1	)
													self.assertEqual(token_a[0]			,_UpperCAmelCase	)
													self.assertEqual(token_a[0]			,_UpperCAmelCase	)









'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase		    =     {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5Tokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5TokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''T5EncoderModel''',
							    '''T5ForConditionalGeneration''',
							    '''T5Model''',
							    '''T5PreTrainedModel''',
							    '''load_tf_weights_in_t5''',
							    '''T5ForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFT5EncoderModel''',
							    '''TFT5ForConditionalGeneration''',
							    '''TFT5Model''',
							    '''TFT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''FlaxT5EncoderModel''',
							    '''FlaxT5ForConditionalGeneration''',
							    '''FlaxT5Model''',
							    '''FlaxT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

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




