
'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Dict				=							["image_processor", "tokenizer"]
 lowercase_					:    Tuple				=							"FlavaImageProcessor"
 lowercase_					:    Union[str, Any]				=							("BertTokenizer", "BertTokenizerFast")







 def __init__(					self		:  int			,				snake_case__		:  Dict=None			,				snake_case__		:  Optional[Any]=None			,				**snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, 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."			,				snake_case__			,				)
     UpperCAmelCase__      :					List[str]				       =							kwargs.pop("feature_extractor" )

   UpperCAmelCase__      :					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__ )
   UpperCAmelCase__      :					Tuple				       =							self.image_processor







 def __call__(					self		:  Any			,				snake_case__		:  Optional[ImageInput] = None			,				snake_case__		:  Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None			,				snake_case__		:  bool = True			,				snake_case__		:  Union[bool, str, PaddingStrategy] = False			,				snake_case__		:  Union[bool, str, TruncationStrategy] = False			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  int = 0			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  Optional[bool] = None			,				snake_case__		:  Optional[bool] = None			,				snake_case__		:  Optional[bool] = None			,				snake_case__		:  Optional[bool] = None			,				snake_case__		:  bool = False			,				snake_case__		:  bool = False			,				snake_case__		:  bool = False			,				snake_case__		:  bool = False			,				snake_case__		:  bool = True			,				snake_case__		:  Optional[Union[str, TensorType]] = None			,				**snake_case__		:  Union[str, Any]			,				):



   '''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__      :					int				       =							self.tokenizer(
         text=snake_case__			,				add_special_tokens=snake_case__			,				padding=snake_case__			,				truncation=snake_case__			,				max_length=snake_case__			,				stride=snake_case__			,				pad_to_multiple_of=snake_case__			,				return_token_type_ids=snake_case__			,				return_attention_mask=snake_case__			,				return_overflowing_tokens=snake_case__			,				return_special_tokens_mask=snake_case__			,				return_offsets_mapping=snake_case__			,				return_length=snake_case__			,				verbose=snake_case__			,				return_tensors=snake_case__			,				**snake_case__			,				)
   if images is not None:
     UpperCAmelCase__      :					List[Any]				       =							self.image_processor(
         snake_case__			,				return_image_mask=snake_case__			,				return_codebook_pixels=snake_case__			,				return_tensors=snake_case__			,				**snake_case__			,				)

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







 def 							UpperCamelCase  (					self		:  Optional[int]			,				*snake_case__		:  Optional[Any]			,				**snake_case__		:  Optional[Any] ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[Any]			,				*snake_case__		:  Optional[int]			,				**snake_case__		:  Union[str, Any] ):



   '''simple docstring'''




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







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							self.tokenizer.model_input_names
   UpperCAmelCase__      :					Any				       =							self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''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 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




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





'''simple docstring'''





import math







def snake_case_     (					lowercase__		):

  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(lowercase__		) + 1		)   ,       6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def snake_case_     (					lowercase__ = 0.1		):
  UpperCAmelCase__      :					Dict				       =							3
  UpperCAmelCase__      :					Tuple				       =							3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1   ,       (j + 2) * (j + 2)   ,       j + 1		):
      primes += is_prime(lowercase__		)
    j += 2
  return j


if __name__ == "__main__":
      import doctest

      doctest.testmod()



