


import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			_a				(     UpperCamelCase__	,      unittest.TestCase     ):
							_lowercase      :     str     =					FunnelTokenizer
							_lowercase      :     Optional[int]     =					FunnelTokenizerFast
							_lowercase      :     int     =					True
							_lowercase      :     Union[str, Any]     =					True

							def  lowerCamelCase_					(				self:      Any				)     ->  str:





									"""simple docstring"""







									super().setUp()

									lowercase__				= [
									    '''<unk>''',
									    '''<cls>''',
									    '''<sep>''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									lowercase__				= 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  lowerCamelCase_					(				self:      Union[str, Any]       ,		**UpperCamelCase_:      List[str]				)     ->  Tuple:





									"""simple docstring"""







									return FunnelTokenizer.from_pretrained(self.tmpdirname       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      List[str]       ,		**UpperCamelCase_:      List[str]				)     ->  str:





									"""simple docstring"""







									return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      Tuple				)     ->  List[Any]:





									"""simple docstring"""







									lowercase__				= '''UNwant\u00E9d,running'''
									lowercase__				= '''unwanted, running'''
									return input_text, output_text

							def  lowerCamelCase_					(				self:      Dict				)     ->  Optional[int]:





									"""simple docstring"""







									lowercase__				= self.tokenizer_class(self.vocab_file				)

									lowercase__				= tokenizer.tokenize('''UNwant\u00E9d,running'''				)
									self.assertListEqual(UpperCamelCase_       ,		['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)       ,		[7, 4, 5, 10, 8, 9]				)


							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= self.get_tokenizers(do_lower_case=UpperCamelCase_				)
									for tokenizer in tokenizers:
											lowercase__				= tokenizer('''UNwant\u00E9d,running'''				)
											lowercase__				= len(inputs['''input_ids''']				) - 1
											self.assertListEqual(inputs['''token_type_ids''']       ,		[2] + [0] * sentence_len				)

											lowercase__				= tokenizer('''UNwant\u00E9d,running'''       ,		'''UNwant\u00E9d,running'''				)
											self.assertListEqual(inputs['''token_type_ids''']       ,		[2] + [0] * sentence_len + [1] * sentence_len				)




'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase          =						{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViTMAEForPreTraining""",
						    """ViTMAELayer""",
						    """ViTMAEModel""",
						    """ViTMAEPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TFViTMAEForPreTraining""",
						    """TFViTMAEModel""",
						    """TFViTMAEPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						_lowercase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
