
import warnings

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






class     UpperCAmelCase						(		A_					):
   A__   :						Any			=	["image_processor", "tokenizer"]
   A__   :						Tuple			=	"CLIPImageProcessor"
   A__   :						Union[str, Any]			=	("CLIPTokenizer", "CLIPTokenizerFast")



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


          '''simple docstring'''


          snake_case			:  Tuple     							= None
          if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."       ,		snake_case__       ,		)
                 snake_case			:  Optional[int]     							= kwargs.pop("feature_extractor"					)

          snake_case			:  int     							= 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__(snake_case__       ,		snake_case__					)



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


          '''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			:  List[Any]     							= self.tokenizer(snake_case__       ,		return_tensors=snake_case__       ,		**snake_case__					)

          if images is not None:
                 snake_case			:  Dict     							= self.image_processor(snake_case__       ,		return_tensors=snake_case__       ,		**snake_case__					)

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



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


          '''simple docstring'''


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



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


          '''simple docstring'''


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



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Any:


          '''simple docstring'''


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



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      int:


          '''simple docstring'''


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



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[str]:


          '''simple docstring'''


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







__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





