
'''simple docstring'''



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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: str										=  FunnelTokenizer
						_lowercase						: int										=  FunnelTokenizerFast
						_lowercase						: str										=  True
						_lowercase						: Tuple										=  True




						def    _lowercase      (   self ):

												"""simple docstring"""



												super().setUp()

												_lowerCAmelCase    		=					[
												    """<unk>""",
												    """<cls>""",
												    """<sep>""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												_lowerCAmelCase    		=					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       ,  **_lowercase ):

												"""simple docstring"""



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




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



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




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""UNwant\u00E9d,running"""
												_lowerCAmelCase    		=					"""unwanted, running"""
												return input_text, output_text




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.tokenizer_class(self.vocab_file )

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




						def    _lowercase      (   self ):

												"""simple docstring"""



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

																		_lowerCAmelCase    		=					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__)
