


import warnings

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



class      __lowerCamelCase       (_lowerCamelCase		):
       _lowercase											=			["""image_processor""", """tokenizer"""]
       _lowercase											=			"""ChineseCLIPImageProcessor"""
       _lowercase											=			("""BertTokenizer""", """BertTokenizerFast""")







       def __init__(       self:      Tuple,A_:      Optional[int]=None,A_:      Dict=None,**A_:      Union[str, 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_ )
            __UpperCamelCase      =       self.image_processor







       def __call__(       self:      str,A_:      int=None,A_:      Tuple=None,A_:      Union[str, Any]=None,**A_:      Dict ):






            '''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:
                 __UpperCamelCase      =       self.tokenizer(lowerCAmelCase_,return_tensors=lowerCAmelCase_,**lowerCAmelCase_ )

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

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







       def 						snake_case_     (       self:      Optional[int],*A_:      Union[str, Any],**A_:      Optional[int] ):






            '''simple docstring'''




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







       def 						snake_case_     (       self:      List[Any],*A_:      Any,**A_:      str ):






            '''simple docstring'''




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







       @property
       def 						snake_case_     (       self:      Optional[int] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.tokenizer.model_input_names
            __UpperCamelCase      =       self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







       @property
       def 						snake_case_     (       self:      List[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




'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'    ,   return_dict=lowerCAmelCase_  ).to(lowerCAmelCase_  )
								_snake_case   						=		AutoTokenizer.from_pretrained('google/mt5-small'  )

								_snake_case   						=		tokenizer('Hello there'    ,   return_tensors='pt'  ).input_ids
								_snake_case   						=		tokenizer('Hi I am'    ,   return_tensors='pt'  ).input_ids

								_snake_case   						=		model(input_ids.to(lowerCAmelCase_  )    ,   labels=labels.to(lowerCAmelCase_  )  ).loss
								_snake_case   						=		-(labels.shape[-1] * loss.item())

								_snake_case   						=		-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 1E-4  )

