
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_ (			_a					,			unittest.TestCase    ):
			a	:			Tuple					   =							FunnelTokenizer
			a	:			Dict					   =							FunnelTokenizerFast
			a	:			str					   =							True
			a	:			List[Any]					   =							True






			def    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									return FunnelTokenizer.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									return FunnelTokenizerFast.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					"""UNwant\u00E9d,running"""
									_lowercase					       =					"""unwanted, running"""
									return input_text, output_text






			def    UpperCamelCase_				(      self  ):
									_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    UpperCamelCase_				(      self  ):
									_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  )

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class