




"""simple docstring"""



import warnings
from typing import List, Optional, Union

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






class 				snake_case__     (   __UpperCamelCase   ):
 _snake_case   :			Dict           =		["""image_processor""", """tokenizer"""]
 _snake_case   :			Union[str, Any]           =		"""ViltImageProcessor"""
 _snake_case   :			Optional[int]           =		("""BertTokenizer""", """BertTokenizerFast""")


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						**lowerCamelCase		):
        __a     				=  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  ,						)
               __a     				=  kwargs.pop("feature_extractor"		)

        __a     				=  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		)
        __a     				=  self.image_processor


 def __call__(				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = True  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = 0  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  self.tokenizer(
            text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase  ,						padding=lowerCamelCase  ,						truncation=lowerCamelCase  ,						max_length=lowerCamelCase  ,						stride=lowerCamelCase  ,						pad_to_multiple_of=lowerCamelCase  ,						return_token_type_ids=lowerCamelCase  ,						return_attention_mask=lowerCamelCase  ,						return_overflowing_tokens=lowerCamelCase  ,						return_special_tokens_mask=lowerCamelCase  ,						return_offsets_mapping=lowerCamelCase  ,						return_length=lowerCamelCase  ,						verbose=lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						**lowerCamelCase  ,						)
        # add pixel_values + pixel_mask
        __a     				=  self.image_processor(lowerCamelCase  ,						return_tensors=lowerCamelCase		)
        encoding.update(lowerCamelCase		)

        return encoding


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)


 @property
 def 		a__ (				self		):
        __a     				=  self.tokenizer.model_input_names
        __a     				=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)


 @property
 def 		a__ (				self		):
        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,						lowerCamelCase  ,						)
        return self.image_processor_class






 @property
 def 		a__ (				self		):
        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,						lowerCamelCase  ,						)
        return self.image_processor

"""simple docstring"""



import math



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  2
       __a     				=  int(math.sqrt(a							)							)  # Size of every segment
       __a     				=  [True] * (end + 1)
       __a     				=  []

       while start <= end:
              if temp[start] is True:
                     in_prime.append(a							)
                     for i in range(start * start ,  end + 1 ,  a							):
                            __a     				=  False
              start += 1
       prime += in_prime

       __a     				=  end + 1
       __a     				=  min(2 * end ,  a							)

       while low <= n:
              __a     				=  [True] * (high - low + 1)
              for each in in_prime:
                     __a     				=  math.floor(low / each							) * each
                     if t < low:
                            t += each

                     for j in range(a ,  high + 1 ,  a							):
                            __a     				=  False

              for j in range(len(a							)							):
                     if temp[j] is True:
                            prime.append(j + low							)

              __a     				=  high + 1
              __a     				=  min(high + end ,  a							)

       return prime




print(sieve(10**6))
