


'''simple docstring'''



import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class  UpperCamelCase__(     __A		,       unittest.TestCase ):
				__magic_name__       :   List[Any]							= RoFormerTokenizer
				__magic_name__       :   List[Any]							= RoFormerTokenizerFast
				__magic_name__       :   Optional[int]							= True
				__magic_name__       :   Union[str, Any]							= True




				def   a__(	self    :				Dict   )->  Tuple:

										"""simple docstring"""




										super().setUp()




				def   a__(	self    :				Union[str, Any]   ,					**lowerCAmelCase    :				Any   )->  Any:

										"""simple docstring"""




										return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''   ,					**UpperCamelCase__   )




				def   a__(	self    :				Any   ,					**lowerCAmelCase    :				str   )->  Union[str, Any]:

										"""simple docstring"""




										return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''   ,					**UpperCamelCase__   )




				def   a__(	self    :				str   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			'永和服装饰品有限公司,今天天气非常好'
										UpperCAmelCase         =			'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
										return input_text, output_text




				def   a__(	self    :				Optional[int]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			self.get_chinese_input_output_texts()
										UpperCAmelCase         =			tokenizer.tokenize(UpperCamelCase__   )

										self.assertListEqual(UpperCamelCase__   ,					output_text.split()   )

										UpperCAmelCase         =			tokens + [tokenizer.unk_token]
										UpperCAmelCase         =			[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__   )   ,					UpperCamelCase__   )




				def   a__(	self    :				Union[str, Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_rust_tokenizer()
										UpperCAmelCase         =			self.get_chinese_input_output_texts()
										UpperCAmelCase         =			tokenizer.tokenize(UpperCamelCase__   )
										self.assertListEqual(UpperCamelCase__   ,					output_text.split()   )
										UpperCAmelCase         =			tokens + [tokenizer.unk_token]
										UpperCAmelCase         =			[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__   )   ,					UpperCamelCase__   )




				def   a__(	self    :				Optional[int]   )->  Any:

										"""simple docstring"""




										pass




				def   a__(	self    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										pass





				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										pass









'''simple docstring'''



import warnings

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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			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__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





