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






class 						UpperCAmelCase	(		a__				):
   A__      :	Optional[int]		  =  ["image_processor", "tokenizer"]
   A__      :	Any		  =  "EfficientNetImageProcessor"
   A__      :	List[str]		  =  ("BertTokenizer", "BertTokenizerFast")



   def __init__(self	:      Optional[int]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      Any				)	->			Optional[int]:


          '''simple docstring'''


          super().__init__(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_				)



   def __call__(self	:      List[str]					,		snake_case__	:      List[str]=None					,		snake_case__	:      int=None					,		snake_case__	:      Tuple="max_length"					,		snake_case__	:      Dict=64					,		snake_case__	:      Dict=None					,		**snake_case__	:      Optional[Any]				)	->			List[str]:


          '''simple docstring'''


          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 :     str           =   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:
                 snake_case :     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:
                 snake_case :     Tuple           =   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        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		*snake_case__	:      Dict					,		**snake_case__	:      Any				)	->			Optional[Any]:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		*snake_case__	:      Dict					,		**snake_case__	:      Any				)	->			Dict:


          '''simple docstring'''


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



   @property
   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   self.tokenizer.model_input_names
          snake_case :     List[str]           =   self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







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 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





