



"""simple docstring"""

import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin







@require_jieba
class    __lowercase     (					_UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							int							       =		CpmAntTokenizer
						_A		:							List[Any]							       =		False







						def 						__UpperCamelCase							(self       ):
											super().setUp()

											snake_case_					:		Optional[int] 					=					[
											    """<d>""",
											    """</d>""",
											    """<s>""",
											    """</s>""",
											    """</_>""",
											    """<unk>""",
											    """<pad>""",
											    """</n>""",
											    """我""",
											    """是""",
											    """C""",
											    """P""",
											    """M""",
											    """A""",
											    """n""",
											    """t""",
											]
											snake_case_					:		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]       )       )







						@tooslow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""       )
											snake_case_					:		str 					=					"""今天天气真好！"""
											snake_case_					:		Dict 					=					["""今天""", """天气""", """真""", """好""", """！"""]
											snake_case_					:		Any 					=					tokenizer.tokenize(lowercase__       )
											self.assertListEqual(lowercase__   ,		lowercase__       )
											snake_case_					:		Dict 					=					"""今天天气真好！"""
											snake_case_					:		Union[str, Any] 					=					[tokenizer.bos_token] + tokens

											snake_case_					:		Union[str, Any] 					=					[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__       )   ,		lowercase__       )

											snake_case_					:		int 					=					tokenizer.decode(lowercase__       )
											self.assertEqual(lowercase__   ,		lowercase__       )









"""simple docstring"""

import warnings

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







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







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

											snake_case_					:		str 					=					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__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







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

											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:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







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







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







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					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."""   ,		lowercase__   ,		)
											return self.image_processor_class




