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







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					["image_processor", "tokenizer"]
	_lowerCAmelCase											=					"EfficientNetImageProcessor"
	_lowerCAmelCase											=					("BertTokenizer", "BertTokenizerFast")





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					super().__init__(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE		)





	def __call__(      self					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__="max_length"					,							UpperCamelCase__=64					,							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:
									A__	:						int								=				self.tokenizer(
									    _SCREAMING_SNAKE_CASE					,							padding=_SCREAMING_SNAKE_CASE					,							max_length=_SCREAMING_SNAKE_CASE					,							return_tensors=_SCREAMING_SNAKE_CASE					,							**_SCREAMING_SNAKE_CASE		)

					if images is not None:
									A__	:						Union[str, Any]								=				self.image_processor(_SCREAMING_SNAKE_CASE					,							return_tensors=_SCREAMING_SNAKE_CASE					,							**_SCREAMING_SNAKE_CASE		)

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





	def 	__snake_case					(      self					,							*UpperCamelCase__					,							**UpperCamelCase__		):
					return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE					,							**_SCREAMING_SNAKE_CASE		)





	def 	__snake_case					(      self					,							*UpperCamelCase__					,							**UpperCamelCase__		):
					return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE					,							**_SCREAMING_SNAKE_CASE		)





	@property
	def 	__snake_case					(      self		):
					A__	:						Optional[Any]								=				self.tokenizer.model_input_names
					A__	:						List[Any]								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
"""simple docstring"""







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'	)
      __lowerCamelCase    		=					'Hugging Face大法好，谁用谁知道。'
      __lowerCamelCase    		=					'Hugging Face大法好,谁用谁知道。<unk>'
      __lowerCamelCase    		=					'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

      __lowerCamelCase    		=					tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokens + [tokenizer.unk_token]

      __lowerCamelCase    		=					[13789, 13283, 1421, 8, 10, 1164, 13608, 16528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokenizer.decode(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)






