






from typing import TYPE_CHECKING

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


A__	:			List[Any]			   = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			int			   = ['FNetTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Union[str, Any]			   = ['FNetTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Tuple			   = [
							    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'FNetForMaskedLM',
							    'FNetForMultipleChoice',
							    'FNetForNextSentencePrediction',
							    'FNetForPreTraining',
							    'FNetForQuestionAnswering',
							    'FNetForSequenceClassification',
							    'FNetForTokenClassification',
							    'FNetLayer',
							    'FNetModel',
							    'FNetPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							A__	:			Union[str, Any]			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       CTRLTokenizer
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__    =			['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
								lowercase__    =			dict(zip(lowerCamelCase,  range(len(lowerCamelCase  )  )  )  )
								lowercase__    =			['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
								lowercase__    =			{'''unk_token''': '''<unk>'''}

								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']  )
								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write(json.dumps(lowerCamelCase  ) + '''\n'''  )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write('''\n'''.join(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    Union[str, Any],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map  )
								return CTRLTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt react readapt apt'''
								return input_text, output_text




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			CTRLTokenizer(self.vocab_file,  self.merges_file,  **self.special_tokens_map  )
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
								lowercase__    =			tokenizer.tokenize(lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			tokens + [tokenizer.unk_token]

								lowercase__    =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),  lowerCamelCase  )




