
import warnings

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







class   _lowercase						(       _SCREAMING_SNAKE_CASE				):


     '''simple docstring'''





     _A								=							["image_processor", "tokenizer"]
     _A								=							"ChineseCLIPImageProcessor"
     _A								=							("BertTokenizer", "BertTokenizerFast")



     def __init__(      self ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					Optional[Any]:
          UpperCAmelCase__							: 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." ,			A_ ,			)
               UpperCAmelCase__							: int 							=						kwargs.pop("feature_extractor"  )

          UpperCAmelCase__							: List[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__(A_ ,			A_  )
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processor



     def __call__(      self ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					Dict:

          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__							: Optional[Any] 							=						self.tokenizer(A_ ,			return_tensors=A_ ,			**A_  )

          if images is not None:
               UpperCAmelCase__							: Any 							=						self.image_processor(A_ ,			return_tensors=A_ ,			**A_  )

          if text is not None and images is not None:
               UpperCAmelCase__							: Optional[int] 							=						image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**A_  ) ,			tensor_type=A_  )



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					str:
          return self.tokenizer.batch_decode(*A_ ,			**A_  )



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					str:
          return self.tokenizer.decode(*A_ ,			**A_  )



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.tokenizer.model_input_names
          UpperCAmelCase__							: int 							=						self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )



     @property
     def  lowerCAmelCase__       (      self  )->					Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,			A_ ,			)
          return self.image_processor_class





"""simple docstring"""




import math



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5       ,    int(math.sqrt(lowerCAmelCase		) + 1		)       ,    6		):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def 							a__ (				lowerCAmelCase	:       int = 1_0001		):






     '''simple docstring'''

     try:
          UpperCAmelCase__							: List[str] 							=						int(lowerCAmelCase		)
     except (TypeError, ValueError):
          raise TypeError("Parameter nth must be int or castable to int."		) from None
     if nth <= 0:
          raise ValueError("Parameter nth must be greater than or equal to one."		)
     UpperCAmelCase__							: list[int] 							=						[]
     UpperCAmelCase__							: str 							=						2
     while len(lowerCAmelCase		) < nth:
          if is_prime(lowerCAmelCase		):
               primes.append(lowerCAmelCase		)
               num += 1
          else:
               num += 1
     return primes[len(lowerCAmelCase		) - 1]


if __name__ == "__main__":
    print(f"""{solution() = }""")



